The take away is that this model is a smaller model that competes with Haiku, I would hope they come out with a "Sonnet" competing model, then Opus. I have been wondering why Microsoft is kind of "sleeping" on offering models they themselves have made on Copilot, maybe it was part of their deal with OpenAI? Not sure.
Yes, it's a "smaller" (137B) model that competes with Haiku, but it's basically the performance of Qwen3.6-35B-A3B which is 75% smaller and 98% smaller in terms of active parameters (since it's a mixture of experts model). Microsoft should be comparing its model to good smaller models, not Haiku 4.5.
Qwen-3.6-27b is closer to Claude Opus 4.7 than it is to Haiku 4.5 in a lot of benchmarks - and it's way smaller than Microsoft's new model.
Sure, it competes with Haiku, but it shows how far Microsoft is behind lots of other small models that are available.
I understand what you’re saying, but I am generally very careful when comparing models and their benchmarks; benchmarks often don’t really match “real world” quality.
It's a start and I welcome competition but I don't think I ever used small cloud models like Haiku 4.5. They are cute but for serious coding they tend to waste your expensive time.
And this certainly wont bring me back to GitHub Copilot which I cancelled yesterday.
GitHub Copilot had competitive pricing until yesterday when they changed from per-request to one of the most expensive per-token quotas. Seriously, take a look at their burning subreddit for some laughs: https://www.reddit.com/r/GithubCopilot
I have since changed to DeekSeek Flash on high which is Sonnet+ level for almost free.
If I feel I still need smarter models I might signup for $20/mo Codex to use GPT 5.5 which, in my opinion, is the best I can access right now.
I use larger models to organize work into a topologically sorted task graph and pin smaller models to the tasks depending on the complexity with a larger model evaluating the work and patching where necessary. This uses haiku quite often for routine work. I’m able to do multi hour highly complex work with superior results and a much lower bill as a result by doing this, with a parent orchestrator able to do a massive labor within a single context window by effectively organizing work and reviewing quality and integrating where needed. I don’t use haiku directly, but it’s often 30-40% of any major efforts token use. This further improves time to completion as well as cost - but I find haiku is better at following literal instructions and plans without “second guessing,” while opus class models second guess in their thinking constantly.
As such, haiku isn’t a waste of my time, it saves enormous amounts of time for me. But I spent a large amount of time building the orchestration system up front and iterating on it to get here. Interestingly i found my experience as a director and later a distinguished engineer gave me the tools to build it and get it working well and reliably end to end - the dynamics of multi agent workflows of varying capability is not a lot different than the dynamics of a 1000 engineer organization.
Got anything from your orchestrator you could share that’s usable by others? Sounds like how I’d like to work but is difficult to get going from scratch
I've been doing benchmarking of various models for finding hard security bugs, and my faith in Haiku (and Sonnet, even) has dropped precipitously in the process. Self-hosted Qwen 3.6 27B consistently outperforms both for finding security bugs, which was a shocking result. I expected Qwen to be around Haiku level, maybe a little worse, and I definitely expected it to be worse than Sonnet.
And, DeepSeek and MiMo perform much better than Haiku and Sonnet, near Opus/GPT 5.5 levels, at a fraction of the cost.
There's seemingly no reason to ever use Haiku or Sonnet, if you're not getting it for free or as part of a subscription (that you don't usually saturate).
I don't think that's what these small models are for. They are for things like text summarization and generating a title for your AI session. Maybe Haiku occupies a weird zone where it's overpowered for those tasks but underpowered for anything more sophisticated. But for example I used it on an agentic reasoning task recently (reading a chunk of information and drawing a written conclusion, not writing code) and it did just fine. More powerful model would have been a waste of money.
Sure, but it's priced higher than many better models. I'm not saying use the biggest models for everything. I'm saying Haiku is not a great deal as small models go. You can even self-host a model that is competitive if you've got a pretty beefy machine.
Haiku costs $1/$5. DeepSeek V4 Flash, a stronger model, is only $0.0028/$0.14/$0.28. That first number is the cached input, and DeepSeek caching is crazy efficient. So, using DeepSeek V4 Flash costs about an order of magnitude less than Haiku and performs better.
I have a Claude subscription because I'm willing to pay a premium for the best model for coding, one that doesn't waste as much of my time doing dumb stuff. But, if I need something other than Claude Code, I'm using something other than Claude models. Why burn money for no benefit?
Oh, also, Haiku chews tokens like crazy. In my benchmarks it used three times more tokens than the next highest model. Of course, security bug hunting is not in its wheelhouse, so it's not fair to judge it based on that one thing, but if it's more expensive per token and burns a lot more tokens, it ends up being a lot more expensive.
I suspect the outrageous pricing of haiku/sonnet is offsetting the cost of opus. The value proposition a year ago was they were cheaper than opus, not that they're a fantastic value (which they're not)
Haiku/Flash/small models are underpowered for literally anything where being non-false-positively correct on details matters at least like 25%. (That's not to say they are only correct 25% of the time, it's definitely more than that, but they're blatantly confidently wrong often enough that the wasted time is a significant net negative for me, even on relatively trivial tasks.)
Almost exactly the same story here. I've also had little to no refusals from DeepSeek, with it's Chinese values meaning substantially less friction when it comes to things like reverse engineering, finding copyrighted files, working with dubiously-sourced source code, et cetera. I don't think I'd go back to Copilot even if they dropped prices by 90%.
Using OpenRouter, going to migrate to DeepSeek's official API soon. I'm not using it for anything commercial or for private data so I have no privacy qualms.
Makes sense. Privacy is my only real hang up with DeepSeek. Both of the big SOTA providers have become extremely filtered. Things that I could do one version ago are now getting refusals. Anthropic is almost unusable. ChatGPT is slightly better. Even with a "cyber exception" in place and a vetted account. They are going to force me to take my business elsewhere.
GitHub Copilot refuses to do any security testing or proof-of-concepts for exploits. While I understand why, we pay for Enterprise and I’m working on our proprietary code base. It’s incredibly annoying.
I’ve actually had luck taking the analysis from GHCP and pasting it into our M365 Copilot and getting a useful poc to stick into my bug reports.
Yeah, seems like this is in the range of Qwen 3.6, Gemma 4, Nemotron 3 Super, and the like. There are lot of models, including much smaller cheaper ones (like Qwen 3.6 35B-A3B), that are similarly competitive with Haiku. I can run these on my laptop, I don't need to rent them from Microsoft.
I suppose if you're reeling at the new Copilot bill but want to stay in their ecosystem, this gives you something to use, but for most folks, there's a plethora of better options.
The $20/month ChatGPT plan that comes with codex is good value. Even just have premium ChatGPT is nice. I get rate limited regularly but it still lets me do most things.
The $100/month is excellent value. I don’t understand how’s that not the default option for all professional developers. Unless people don’t produce any value writing code, like playing around and experimenting with vibe coding, I understand. But if software development is your actual income, and assuming you live in a wealthy country, $100/month is nothing for a tool like Codex.
Work pays for my work stuff and I have both claude and codex there. On the personal side I sometimes go days without using it. It's more like my assistant to do annoying terminal shit on my home computer and like personal projects I guess. It's plenty for that.
The supermajority of respondents did report that they do engage in some coding outside of working hours, for one reason or another. I'm impressed; I'm basically a zombie after hours, rarely in any shape to touch anything technical. Good for them.
But then only 19.3% of respondents ticked that they code for freelancing reasons, and only 15% said they're doing it in an attempt to bootstrap a business. These groups were the only types that suggested revenue generating after-hours activity, and they even overlap to a non-obvious-to-me extent. But even if we pretended they didn't, that adds up to like a third at best.
So when you say:
> I don’t understand how’s that not the default option for all professional developers.
that's in contradiction with this data (and imo common sense), which suggests that the supermajority of professional developers simply do not perform revenue generating software development activity outside of work hours, period. Therefore, for them, the ROI on any potential AI subscription is a flat and constant zero.
Unless you envision people working at "bring your own license" type shops, I don't know how this is supposed to make sense. These are work tools, corporate should be providing them already. But then I'm clearly not from a "wealthy" country either, so YMMV.
The small stuff has their place. I have this safari extension and needed a way to quickly title people's chat histories. Haiku is the fast cheap thing to come up with decent titles of blocks of text. I feel like there's a bunch of those little things lying around you need a model for. I'm even finding Apple's Foundation Model is super useful for stuff like that. Even summarizing an article. It's like equally awful at doing it, but gets enough done to still be useful as a way to be like "oh yeah, this article is actually worth reading"
Haiku does quite well if given a detailed plan. That means much more detail than you otherwise would, but you can still save over e.g. having Opus or Sonnet do everything by having them expand their initial plans into more specific levels of detail and feed it to Haiku (or similar level models).
I personally wouldn't use models that class directly, though - I'd use them in a harness as a "backend" for more capable models. And Haiku itself, as opposed to other smaller models, is still expensive.
Makes sense as part of a larger coding workflow, especially if it’s fast. Using a trillion parameter model to figure out how to call a targeted edit tool or generate a commit message is a waste. Also narrow tasks like “make the background darker” or “rename this function and update callers”
Won’t (presumably) all the market actors converge on similar pricing? If OpenAI stopped operating on subsidies and charge the true costs and their most token hungry customers are the ones that switch to Anthropic and others, then their pricing model switch will also be around the corner.
Unless of course we’re thinking Copilot will be more expensive than others longer term. But is that a reasonable assumption?
Anthropic & co charge API users much more, not least to demolish the middlemen low-effort plays like Cursor and Copilot. To not own the model is not viable in 2026.
Sorry, what do you mean by "To not own the model is not viable in 2026."
I assume I'm misunderstanding you (likely my fault), because the way I read that is that you're saying nobody should currently be using models owned & hosted by companies like OpenAI and Antheopic, while clearly a huge number of people are using those in 2026 despite not owning them.
I think it’s more correct to say they charge subscription users much much less. I assume less even than the cost of providing the inference, if you actually are using it.
I've been having really good results with DeepSeek-v4-flash, qwen-3.6-moe, and the older gimini-3-flash-preview. (recent geminis suck hard)
Small models are more than enough for the majority of tasks these days. Plan and review with the bigger ones, let the little ones explore and implement.
OpenCode Go is $10/month for the open weight models with nice quotas: https://opencode.ai/go
You don’t have to limit yourself to the tiny models with the OpenCode Go plan, you can get a lot of usage from the bigger models if you keep the cache hot.
I am about 85% through my quota with 9 days left before refresh and have just used over 1B tokens, mostly DeepSeek V4 Pro, but also a little mimo 2.5 pro and kimi k2.6
For sure, I've been flipping between flash/pro (or the equivalent for other families), been trying to stick to one family per project as a way to test them out independently over longer periods and more realistic/diverse tasks. I've definitely spent more quota on pro and pushed more tokens through flash.
> "GitHub Copilot had competitive pricing until yesterday when they changed from per-request to one of the most expensive per-token quotas. Seriously, take a look at their burning subreddit for some laughs"
AI is expensive and it has been heavily subsidized. I you think $20/mo for Codex/Claude flat vs a more usage based model you're in for a shock. Especially once these companies go public and have to meet investor expectations.
I really hope one day there is something like Opus 4.8 but with Cerebras' speed -- they reach over 1,000t/s on gpt-oss-120b but that model is seemingly not even properly trained for tool calling. But watching it slam out several entire screens of thinking/reasoning per second is amazing. I'd love that with Opus quality.
I like gpt oss - great model even if not too smart.. runs on my laptop at over 100ts has a certain tone that I like over all these qwens stuck up their asses.
Does anyone actually uses these smaller models for coding? If so, how? I usually Opus everything. Is the play to plan/design/architect with a heavier model than delegate structured tasks to these smaller ones? Would appreciate to hear someone's opinion on having done and tested both paths.
Yes. Divide execution of a change into separate responsibilities. Designate the main chat as the "orchestrator", Opus. You designate a goal, then tell it to grind until it gets there using the following sub-agents in sequence:
1. Step execution (Sonnet): Work for 30 minutes / 100k tokens at the direction of the Orchestrator
2. Review (Opus): Scrutinize the previous step's work for errors, fidelity to the instructions, fix those and record opportunities to improve the agent configuration + tools to reduce errors and token usage (record those to a file).
3. Self-improvement (Opus): Implement the highest impact self-improvement items that don't require user intervention.
Repeat: Until orchestrator session token budget exhausted (set it to 1M or whatever).
The underlying rationale is to keep each step manageable to maximize adherence to instructions and minimize cost (even cached tokens cost something). Prompt tokens are much cheaper than generated, so to the extent Opus mostly reviews rather than drives that saves a lot too. Self-improvement steps are very expensive but the improvements compound, if you're going to run a job for days or weeks it's way more expensive not to do them.
Edit: I do this in Claude Code with the Anthropic models as well as Qwen family models for offline use.
Because the Haiku model is quite cheap but doesn't screw up too often I used it for interactive coding for my existing projects on the older copilot plans.
For simple features I don't have a full plan worked out. I write a bit of code then tell the model in a short line prompt what it should do. Sometimes I put temporary comments in the code to give it guidance. Generally if the code change is within a file or package, Haiku is good enough follow what you ask and not mess up too much. I also have skills created over time to give it guidance. There were some months when I used GitHub copilot where I had excess credits available at the end of the month I frantically try to use up.
Even the AI code completions can be pretty good on their own. Sometimes I write some temporary comments describing what the code should do and just press Tab-Tab-Tab and the entire function is done.
I think there is a tendency for people to go for the advanced models thinking they we screw up less but if you really understand the code its easier to interactively do it with a lesser model.
I use Gemini 3 Flash, I've seen the Claude Code setups, bullish on Anthropic people are driving up tokens but I am able to produce outcomes with a fraction of the money.
Do you mind sharing your workflow? What do you mean by fraction of the money, in my case personally, I'm yet to reach a session limit on the subscription plan. I'm not "tokenmaxxing" as they say, so hard to see a scenario in which the plan is expensive for the value I get.
I spend around $20 a month through API fees using my own harness, https://slidebits.com/isogen. Nothing too special, I prompt it produces file changes using grep and vector search and I can individually accept which files.
For comparison someone showed me an internal company tool he was working on. He had Claude agents dangerously skipping permissions and firing up github branches through a vm sandbox just to make a single feature change. One agent to code and the other to review.
If you don't hit a limit running Opus, it means you are very much in the loop.
For example you probably don't have days where you ask Opus to review your whole code base and look for code duplication/technical debt/robustness issues, and then to fix some of the found issues, and do this 3-5 times until no big issues are found anymore.
What’s your prompt for this, the way you described it made it seem like there’s a generalizable way I can go about this. I just rely on a testing pipeline instead so can’t think of why I would need to proactively find holes where tests haven’t already done that for me.
I use similar workflow. Here is my refactoring and code quality prompt that I regularly run:
Perform a thorough analysis of the <project_name> project (the code and the documentation).
- Explore the project, go over all important files one by one and look for any mistakes or possible bugs.
- Look for refactoring opportunities and ways to improve code quality and organization.
- Identify any potential cruft/bloat, to ensure our code is clean and logically laid out. Keep in mind that efficient and good quality code needs to avoid over-engineered constructs and needless complexity. Avoid complicated logic where simple solutions would be more elegant.
- Pay attention to comments: There should be enough of them to document the intent and provide high-level overview of the code logic, but not too much; avoid/remove excessive comments that simply restate the code logic or do not provide any useful information.
- Every important function should have a top-level docstring comment that clearly explains its purpose, high-level logic overview, arguments, and return values.
- Analyze the names of constants/variables/functions/classes and other code elements: could some of them be renamed to make their purpose more clear?
- Analyze the documentation, uncover any potential inaccuracies/omissions and ensure the docs reflect the code.
- Brainstorm ideas for improvements of the code and docs.
After you finish the analysis, save an analysis report into "<project_name>_analysis_report.md" in the project root folder.
tests will not find inconsistent naming, duplicate functions, scenarios you have not thought about testing
I use quite plain prompts, nothing fancy:
> go over the tests and do a code review, focusing on how well they test inventory management, planner and controller. maybe some tests need to be deleted, maybe other tests need to be added. the end goal should be good coverage of the core features.
> do a code review, focusing on robustness/correctness issues. validate that the code correctly implements specification.md. focus on the async client.
> there was a big refactor. please do a code review, focusing on eliminating tech debt. look for unused, obsolete or duplicate code that can be removed, look for mismatched interfaces, inconsistent function/argument/variable names. do not output what is correct, just the issues you found. for each issue output instructions for a coding agent on how to fix it. do not nitpick.
Claude code itself spins a lot of its subagents with Haiku. The model has low hallucination rate, so it is great for exploration tasks. I guess this is what the best purpose of this model here will be as well. Which is a lot of tokens - many tasks spin multiple exploration agents before the planning or fixing, that is then just a few tool calls.
I was wondering the same. I guess it makes sense to use a heavy weight model to make the entire design and split the work so that smaller models (possibly local one?) would then do the coding... But how would I even do that? I'm using Claude Code. Would I need support for this within the harness ?
From my experience, smaller models like Haïku 4.5 have indeed shown very convincing results on specific, scoped tasks (themselves generated by a more capable model such as Opus 4.6). We use this kind of workflows in production to optimize speed, efficiency, and costs.
i used to use opus for everything, thats not an option once you move to a multi agent system unless you're working on like high end research. I could easily spend 3k a day if i was using opus as just a normal dev.
As we build a better and better harness and better feedback/verifiers we're switching more to 3.5 flash. I think chinese models would work too, but we cant use those atm.
Generally theres a coordinator running opus and an ever growing set of skills and subagents that take actions using weaker models and output feedback to the coordinator opus.
I'm pretty convinced at this point we're past the level of intelligence needed for most tasks most devs do and that will trend down as we better build harnesses for our own codebases.
I keep trying to, because I really want to make qwen 3.6 35b work for end implementation of a fleshed out spec (mostly for local data privacy reasons).
...but I spend so much more time correcting it, or building pipelines to try, retry, and converge, that it's rarely worthwhile for me in either time or $ spent vs Opus.
I actually find planning/design easier with a smaller model and implementation with a larger one. I'm mostly manually working with the model on planning and design and decisions are mine and smaller models are faster. And when there's a clear design/wayforward, the bigger models are usually better at understanding the overall context and applying the specific patch they were assigned to. I call it the 1-2 punch system where you do the first light punch then the harder punch when its actually important to hit properly. I know it goes against the standard of throwing the biggest model at design but I personally experience the bigger models try to do TOO MUCH and take a lot of time which is something that's not good in the design/arch/boilterplate phase.
It's so weird to me that the benchmarks remain so low, but the models are marketed as revolutionary. And if you say that low coding capabilities aren't a problem, say that to the token price hike and 'general use' model setup.
Why not sell it as a math agent? Why do I have to set up 4 agents to check each others' work?
from what I understand, it's because unlike the other models, MAI models haven't yet fine-tuned against the synthetic datasets specifically designed to boost the benchmark scores.
Hard to know when they don't give the price per token. Presumably it will be comparable to a low-mid range model in terms of price. But otherwise their 'Ideal Zone' is meaningless without factoring in the price per token. I don't how much tokens are being used, that's an implementation detail to me. I care about price / performance / latency.
Yeah the future is probably a number of highly specialised small models you can run on your own hardware rather than massive frontier models in the cloud.
That seems to be what Microsoft is betting on also based on what was shown at the BUILD keynote today + that new surface ultra and the surface mini PC with the new Nvidia chip. Nadella really played up local AI as the main use case they have in mind.
MOE basically work that way already, QWEN/etc with low active params (A-number in name) allows to inference big models locally (only active params have to fit into memory)
Please test your websites in Safari. Almost all of your iOS users use it by default, and the desktop experience is pretty close to the mobile experience, so testing is easy.
That scroll effect is jank city for me (yeah yeah works fine in Chrome/Edge).
Unless they specifically clarify that the testing and training benchmarks are completely separate, we have to assume they test on the same 'hill' the model climbs.
If only they had launched that yesterday I might have avoided Copilot auto model selection using a 9x model, quietly burning my monthly quota in a single afternoon.
I was hoping Microsoft would make it open weights, as they have done for years with the Phi models.
The era of big tech releasing models into the wild might be over, which IMO is counter-productive, as we are shifting from "the model is the product" to "the harness is the product"
I personally do not like Microsoft, but congrats them to release this model.
While the scores are not good compare to other open weight model, the important thing to note is their training data (as they claimed) is very clean, without any synthetic datasets.
Mark Zuckerberg must be in crisis. Microsoft releasing models that compete with Claude's models. Meanwhile the only thing anyone knows about Mark's models is that they help you get hacked more easily.
i have had good results adding muse spark's contemplate mode as a roundtabler for complex questions. but you cant turn off their data ingestion for training so that is a shame.
Wait… I think he has moltbook IP as well that he can scale up.
Seriously tho, wtf is going on over at Meta? Anyone working there currently want to describe the vibe of the org when it comes to being a frontier company?
I don't understand his plan, if I were him I'd either have just gone all in on making RAM which would become very lucrative, or would have focused on building programming models. They've built some key open source technologies, but its as if Mark Zuckerberg cannot run anything that isn't a social media company / project.
Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting.
For those that don't know about this. Phi was announced with a paper called "Textbooks are all you need". What they did was use GPT 3.5 and created synthetic textbook chapters and exercises.
They also did some more interesting work like showing very small models can be coherent as long as you have very simple children's book style training data (TinyStories is pretty famous).
Lots of these ideas are still used. Learning facts at scale with active reading is an ICLR 2026 paper from Meta AI that does a lot of similar work.
What's with the lack of Microsoft design language on the website? It's painfully obvious they're trying to emulate Anthropic's style here and it looks tacky.
Brand guidelines and web design pretty much don't exist any more as far as I can tell. Gotta get it out yesteday, and the only way to do that is vibe coding, styling be damned.
That's neither Microsoft nor Anthropic design. It's from their acquisition of Inflection AI. Even Copilot mobile app design is basically what was Inflection's design
I've always wondered where Consumer CoPilot's design language was from.
If you watch the Build keynote with Satya, you'll notice that the design of the slides changed to Serif typography and warmer colors when Mustafa/Microsoft AI segment came on which was completely different from the rest of the keynote. Now it makes sense!
I'd really like to get back to an autocomplete flow, ideally with some shared and optimized context with the relationship with my larger agent models.
But it seems like, by and large, even the faster models are now aimed at longer-running agentic flows and not sub-1s autocomplete. Or am I wrong about that?
You aren't wrong, the field is moving to a world where we do less in the code editor, so autocomplete is not needed any more. I've only manually edited code a few times in the last month. Haven't used autocomplete in 6+ months since I left Copilot to build my own agent harness (I'm now mainly using OpenCode)
"Clean data" is impossible. Language models have polluted the landscape to such a degree it's impossible to filter them out now. OpenAI has no doubt discarded or muddled their dataset that was used to train the original ChatGPT, so there may be no dataset in existence now that isn't contaminated.
I went to VSC specifically to avoid the pricing I started experiencing on Cursor. After this change I have no reason to stick with GH Copilot, I'd rather keep buying OR credits.
is 51% good enough to reliably use? There's no world in which I use an AI agent where it gets even 15% of the code wrong, that's as bad a Tesla FSD where you need to pay attention to the road while engaging FSD. What's the point? My attention is what I'm trying to relieve, not mostly correct functionality. The only thing that matters is whether you can one-shot code like Claude or Codex, I'm not interested in a small but mostly-okay-but-annoyingly-buggy-every-now-and-then AI.
51% does not mean it randomly gets things wrong half the time.
These things can be useful if you can accurately predict which tasks they will reliably do, and which they will usually fail on. Then you can get much more reliable work from them.
They're comparing to Haiku, not Opus. Haiku is currently at 4.5.
Even if it were Opus, comparing to a version number makes for an interesting snapshot of time comparison: if you knew how a model performed at whatever time in was in vogue, you can say "well, it looks like Model X is about 6 months/1 year/etc. behind the frontier SOTA" - which is exactly the discussion that happens in the open-weight/local LLM space. (interesting, MAI-Code-1-Flash does not appear to be such an open-weight model, following the western trend of locking models up)
Huh, according to that model card this is a 137B total parameter model.
Performance doesn't seem that good:
- MAI-Code-1-Flash (137B-A5B) = 51% on SWE-bench pro
- Qwen3.6-35B-A3B = 49.5% on SWE-bench pro (https://huggingface.co/Qwen/Qwen3.6-35B-A3B)
They benchmark against Claude Haiku but Haiku is not good, it's worse than tiny open models you can run locally or via API at 10% the cost.
The take away is that this model is a smaller model that competes with Haiku, I would hope they come out with a "Sonnet" competing model, then Opus. I have been wondering why Microsoft is kind of "sleeping" on offering models they themselves have made on Copilot, maybe it was part of their deal with OpenAI? Not sure.
Yes, it's a "smaller" (137B) model that competes with Haiku, but it's basically the performance of Qwen3.6-35B-A3B which is 75% smaller and 98% smaller in terms of active parameters (since it's a mixture of experts model). Microsoft should be comparing its model to good smaller models, not Haiku 4.5.
Qwen-3.6-27b is closer to Claude Opus 4.7 than it is to Haiku 4.5 in a lot of benchmarks - and it's way smaller than Microsoft's new model.
Sure, it competes with Haiku, but it shows how far Microsoft is behind lots of other small models that are available.
I understand what you’re saying, but I am generally very careful when comparing models and their benchmarks; benchmarks often don’t really match “real world” quality.
They did release, MAI-Thinking-1 to compete with Sonnet. Totally not sure why that isn't at the top here.
Good question, and I missed that entirely!
Compete? It is behind Kimi K2.6, which is in turn away behind Sonnet.
> 137B-A5B
Yeah, not a 5B param model as the earlier title implied!
It's a start and I welcome competition but I don't think I ever used small cloud models like Haiku 4.5. They are cute but for serious coding they tend to waste your expensive time.
And this certainly wont bring me back to GitHub Copilot which I cancelled yesterday.
GitHub Copilot had competitive pricing until yesterday when they changed from per-request to one of the most expensive per-token quotas. Seriously, take a look at their burning subreddit for some laughs: https://www.reddit.com/r/GithubCopilot
I have since changed to DeekSeek Flash on high which is Sonnet+ level for almost free.
If I feel I still need smarter models I might signup for $20/mo Codex to use GPT 5.5 which, in my opinion, is the best I can access right now.
I use larger models to organize work into a topologically sorted task graph and pin smaller models to the tasks depending on the complexity with a larger model evaluating the work and patching where necessary. This uses haiku quite often for routine work. I’m able to do multi hour highly complex work with superior results and a much lower bill as a result by doing this, with a parent orchestrator able to do a massive labor within a single context window by effectively organizing work and reviewing quality and integrating where needed. I don’t use haiku directly, but it’s often 30-40% of any major efforts token use. This further improves time to completion as well as cost - but I find haiku is better at following literal instructions and plans without “second guessing,” while opus class models second guess in their thinking constantly.
As such, haiku isn’t a waste of my time, it saves enormous amounts of time for me. But I spent a large amount of time building the orchestration system up front and iterating on it to get here. Interestingly i found my experience as a director and later a distinguished engineer gave me the tools to build it and get it working well and reliably end to end - the dynamics of multi agent workflows of varying capability is not a lot different than the dynamics of a 1000 engineer organization.
Everyone does that. But I don't find Haiku useful for actual coding tasks. Good to, ehm, generate commit messages and summaries.
In my tests, openweight Qwens and GLM are way better than it.
Got anything from your orchestrator you could share that’s usable by others? Sounds like how I’d like to work but is difficult to get going from scratch
https://github.com/7mind/baboon - all the backends apart from C# and Scala ones were created automatically, same for LSP server, same for playground.
I've been doing benchmarking of various models for finding hard security bugs, and my faith in Haiku (and Sonnet, even) has dropped precipitously in the process. Self-hosted Qwen 3.6 27B consistently outperforms both for finding security bugs, which was a shocking result. I expected Qwen to be around Haiku level, maybe a little worse, and I definitely expected it to be worse than Sonnet.
And, DeepSeek and MiMo perform much better than Haiku and Sonnet, near Opus/GPT 5.5 levels, at a fraction of the cost.
There's seemingly no reason to ever use Haiku or Sonnet, if you're not getting it for free or as part of a subscription (that you don't usually saturate).
I don't think that's what these small models are for. They are for things like text summarization and generating a title for your AI session. Maybe Haiku occupies a weird zone where it's overpowered for those tasks but underpowered for anything more sophisticated. But for example I used it on an agentic reasoning task recently (reading a chunk of information and drawing a written conclusion, not writing code) and it did just fine. More powerful model would have been a waste of money.
Sure, but it's priced higher than many better models. I'm not saying use the biggest models for everything. I'm saying Haiku is not a great deal as small models go. You can even self-host a model that is competitive if you've got a pretty beefy machine.
Haiku costs $1/$5. DeepSeek V4 Flash, a stronger model, is only $0.0028/$0.14/$0.28. That first number is the cached input, and DeepSeek caching is crazy efficient. So, using DeepSeek V4 Flash costs about an order of magnitude less than Haiku and performs better.
I have a Claude subscription because I'm willing to pay a premium for the best model for coding, one that doesn't waste as much of my time doing dumb stuff. But, if I need something other than Claude Code, I'm using something other than Claude models. Why burn money for no benefit?
Oh, also, Haiku chews tokens like crazy. In my benchmarks it used three times more tokens than the next highest model. Of course, security bug hunting is not in its wheelhouse, so it's not fair to judge it based on that one thing, but if it's more expensive per token and burns a lot more tokens, it ends up being a lot more expensive.
I suspect the outrageous pricing of haiku/sonnet is offsetting the cost of opus. The value proposition a year ago was they were cheaper than opus, not that they're a fantastic value (which they're not)
Haiku/Flash/small models are underpowered for literally anything where being non-false-positively correct on details matters at least like 25%. (That's not to say they are only correct 25% of the time, it's definitely more than that, but they're blatantly confidently wrong often enough that the wasted time is a significant net negative for me, even on relatively trivial tasks.)
Almost exactly the same story here. I've also had little to no refusals from DeepSeek, with it's Chinese values meaning substantially less friction when it comes to things like reverse engineering, finding copyrighted files, working with dubiously-sourced source code, et cetera. I don't think I'd go back to Copilot even if they dropped prices by 90%.
Are you purchasing directly from DeepSeek? Any concerns as far as privacy or data protection?
Using OpenRouter, going to migrate to DeepSeek's official API soon. I'm not using it for anything commercial or for private data so I have no privacy qualms.
Makes sense. Privacy is my only real hang up with DeepSeek. Both of the big SOTA providers have become extremely filtered. Things that I could do one version ago are now getting refusals. Anthropic is almost unusable. ChatGPT is slightly better. Even with a "cyber exception" in place and a vetted account. They are going to force me to take my business elsewhere.
GitHub Copilot refuses to do any security testing or proof-of-concepts for exploits. While I understand why, we pay for Enterprise and I’m working on our proprietary code base. It’s incredibly annoying.
I’ve actually had luck taking the analysis from GHCP and pasting it into our M365 Copilot and getting a useful poc to stick into my bug reports.
Yeah, seems like this is in the range of Qwen 3.6, Gemma 4, Nemotron 3 Super, and the like. There are lot of models, including much smaller cheaper ones (like Qwen 3.6 35B-A3B), that are similarly competitive with Haiku. I can run these on my laptop, I don't need to rent them from Microsoft.
I suppose if you're reeling at the new Copilot bill but want to stay in their ecosystem, this gives you something to use, but for most folks, there's a plethora of better options.
The $20/month ChatGPT plan that comes with codex is good value. Even just have premium ChatGPT is nice. I get rate limited regularly but it still lets me do most things.
The $100/month is excellent value. I don’t understand how’s that not the default option for all professional developers. Unless people don’t produce any value writing code, like playing around and experimenting with vibe coding, I understand. But if software development is your actual income, and assuming you live in a wealthy country, $100/month is nothing for a tool like Codex.
Work pays for my work stuff and I have both claude and codex there. On the personal side I sometimes go days without using it. It's more like my assistant to do annoying terminal shit on my home computer and like personal projects I guess. It's plenty for that.
Picked up the most recent SO developer survey that features relevant info, the 2024 release: https://survey.stackoverflow.co/2024/work#coding-outside-of-...
The supermajority of respondents did report that they do engage in some coding outside of working hours, for one reason or another. I'm impressed; I'm basically a zombie after hours, rarely in any shape to touch anything technical. Good for them.
But then only 19.3% of respondents ticked that they code for freelancing reasons, and only 15% said they're doing it in an attempt to bootstrap a business. These groups were the only types that suggested revenue generating after-hours activity, and they even overlap to a non-obvious-to-me extent. But even if we pretended they didn't, that adds up to like a third at best.
So when you say:
> I don’t understand how’s that not the default option for all professional developers.
that's in contradiction with this data (and imo common sense), which suggests that the supermajority of professional developers simply do not perform revenue generating software development activity outside of work hours, period. Therefore, for them, the ROI on any potential AI subscription is a flat and constant zero.
Unless you envision people working at "bring your own license" type shops, I don't know how this is supposed to make sense. These are work tools, corporate should be providing them already. But then I'm clearly not from a "wealthy" country either, so YMMV.
Every developer who writes code for a living should get an AI subscription from work and not have to pay for it himself.
The small stuff has their place. I have this safari extension and needed a way to quickly title people's chat histories. Haiku is the fast cheap thing to come up with decent titles of blocks of text. I feel like there's a bunch of those little things lying around you need a model for. I'm even finding Apple's Foundation Model is super useful for stuff like that. Even summarizing an article. It's like equally awful at doing it, but gets enough done to still be useful as a way to be like "oh yeah, this article is actually worth reading"
Small models are super useful. But I'm skeptical of their use for coding in particular, which is what this model is advertised for.
Haiku does quite well if given a detailed plan. That means much more detail than you otherwise would, but you can still save over e.g. having Opus or Sonnet do everything by having them expand their initial plans into more specific levels of detail and feed it to Haiku (or similar level models).
I personally wouldn't use models that class directly, though - I'd use them in a harness as a "backend" for more capable models. And Haiku itself, as opposed to other smaller models, is still expensive.
Makes sense as part of a larger coding workflow, especially if it’s fast. Using a trillion parameter model to figure out how to call a targeted edit tool or generate a commit message is a waste. Also narrow tasks like “make the background darker” or “rename this function and update callers”
> “rename this function and update callers”
I'm old enough to remember when IDEs could do this without needing a couple gigabytes of matrices to do it
(LLMs are great for anything even slightly more complicated ofc)
Won’t (presumably) all the market actors converge on similar pricing? If OpenAI stopped operating on subsidies and charge the true costs and their most token hungry customers are the ones that switch to Anthropic and others, then their pricing model switch will also be around the corner.
Unless of course we’re thinking Copilot will be more expensive than others longer term. But is that a reasonable assumption?
Anthropic & co charge API users much more, not least to demolish the middlemen low-effort plays like Cursor and Copilot. To not own the model is not viable in 2026.
Sorry, what do you mean by "To not own the model is not viable in 2026."
I assume I'm misunderstanding you (likely my fault), because the way I read that is that you're saying nobody should currently be using models owned & hosted by companies like OpenAI and Antheopic, while clearly a huge number of people are using those in 2026 despite not owning them.
It's that companies like copilot/cursor are in real trouble if they are in the business of reselling expensive Anthropic tokens
I think it’s more correct to say they charge subscription users much much less. I assume less even than the cost of providing the inference, if you actually are using it.
What application/UI are you using deep seek flash high on? Still copilot or something else
I've been having really good results with DeepSeek-v4-flash, qwen-3.6-moe, and the older gimini-3-flash-preview. (recent geminis suck hard)
Small models are more than enough for the majority of tasks these days. Plan and review with the bigger ones, let the little ones explore and implement.
OpenCode Go is $10/month for the open weight models with nice quotas: https://opencode.ai/go
You don’t have to limit yourself to the tiny models with the OpenCode Go plan, you can get a lot of usage from the bigger models if you keep the cache hot.
I am about 85% through my quota with 9 days left before refresh and have just used over 1B tokens, mostly DeepSeek V4 Pro, but also a little mimo 2.5 pro and kimi k2.6
For sure, I've been flipping between flash/pro (or the equivalent for other families), been trying to stick to one family per project as a way to test them out independently over longer periods and more realistic/diverse tasks. I've definitely spent more quota on pro and pushed more tokens through flash.
> "GitHub Copilot had competitive pricing until yesterday when they changed from per-request to one of the most expensive per-token quotas. Seriously, take a look at their burning subreddit for some laughs"
AI is expensive and it has been heavily subsidized. I you think $20/mo for Codex/Claude flat vs a more usage based model you're in for a shock. Especially once these companies go public and have to meet investor expectations.
I really hope one day there is something like Opus 4.8 but with Cerebras' speed -- they reach over 1,000t/s on gpt-oss-120b but that model is seemingly not even properly trained for tool calling. But watching it slam out several entire screens of thinking/reasoning per second is amazing. I'd love that with Opus quality.
I like gpt oss - great model even if not too smart.. runs on my laptop at over 100ts has a certain tone that I like over all these qwens stuck up their asses.
I wonder when THEY make it illegal to vote with your wallet.
Does anyone actually uses these smaller models for coding? If so, how? I usually Opus everything. Is the play to plan/design/architect with a heavier model than delegate structured tasks to these smaller ones? Would appreciate to hear someone's opinion on having done and tested both paths.
I am using Opus 4.x at work, and these "smaller" (20-80bn, 3-4bn active) models at home. Unfortunately there is no comparison, yet (IMHO anyway).
With Opus I can work, trust its designs, architecture suggestions, and code changes, even in a complex code base.
The smaller models seem to "try". They work for smaller tasks, but for more complex task it's often more work than doing it myself.
I wish it were different, and maybe in a year or two it will be.
Ya when it’s your own token budget on the line the smaller/cheaper models are more attractive.
I’ve used GPT mini quite a bit and it’s decent.
>Is the play to plan/design/architect with a heavier model than delegate structured tasks to these smaller ones?
always has been
claude code has opusplan — uses opus while in plan mode, switches to sonnet for execution.
https://code.claude.com/docs/en/model-config#opusplan-model-...
edit: you can make it work with sonnet for planning, and haiku for execution, or any other combination you fancy to work with.
https://code.claude.com/docs/en/model-config#control-the-mod...
Yes. Divide execution of a change into separate responsibilities. Designate the main chat as the "orchestrator", Opus. You designate a goal, then tell it to grind until it gets there using the following sub-agents in sequence:
1. Step execution (Sonnet): Work for 30 minutes / 100k tokens at the direction of the Orchestrator
2. Review (Opus): Scrutinize the previous step's work for errors, fidelity to the instructions, fix those and record opportunities to improve the agent configuration + tools to reduce errors and token usage (record those to a file).
3. Self-improvement (Opus): Implement the highest impact self-improvement items that don't require user intervention.
Repeat: Until orchestrator session token budget exhausted (set it to 1M or whatever).
The underlying rationale is to keep each step manageable to maximize adherence to instructions and minimize cost (even cached tokens cost something). Prompt tokens are much cheaper than generated, so to the extent Opus mostly reviews rather than drives that saves a lot too. Self-improvement steps are very expensive but the improvements compound, if you're going to run a job for days or weeks it's way more expensive not to do them.
Edit: I do this in Claude Code with the Anthropic models as well as Qwen family models for offline use.
Because the Haiku model is quite cheap but doesn't screw up too often I used it for interactive coding for my existing projects on the older copilot plans.
For simple features I don't have a full plan worked out. I write a bit of code then tell the model in a short line prompt what it should do. Sometimes I put temporary comments in the code to give it guidance. Generally if the code change is within a file or package, Haiku is good enough follow what you ask and not mess up too much. I also have skills created over time to give it guidance. There were some months when I used GitHub copilot where I had excess credits available at the end of the month I frantically try to use up.
Even the AI code completions can be pretty good on their own. Sometimes I write some temporary comments describing what the code should do and just press Tab-Tab-Tab and the entire function is done.
I think there is a tendency for people to go for the advanced models thinking they we screw up less but if you really understand the code its easier to interactively do it with a lesser model.
I use Gemini 3 Flash, I've seen the Claude Code setups, bullish on Anthropic people are driving up tokens but I am able to produce outcomes with a fraction of the money.
Do you mind sharing your workflow? What do you mean by fraction of the money, in my case personally, I'm yet to reach a session limit on the subscription plan. I'm not "tokenmaxxing" as they say, so hard to see a scenario in which the plan is expensive for the value I get.
I spend around $20 a month through API fees using my own harness, https://slidebits.com/isogen. Nothing too special, I prompt it produces file changes using grep and vector search and I can individually accept which files.
I also work on a consumer AI application https://apps.apple.com/us/app/slidebits-studio/id1138731130
For comparison someone showed me an internal company tool he was working on. He had Claude agents dangerously skipping permissions and firing up github branches through a vm sandbox just to make a single feature change. One agent to code and the other to review.
If you don't hit a limit running Opus, it means you are very much in the loop.
For example you probably don't have days where you ask Opus to review your whole code base and look for code duplication/technical debt/robustness issues, and then to fix some of the found issues, and do this 3-5 times until no big issues are found anymore.
What’s your prompt for this, the way you described it made it seem like there’s a generalizable way I can go about this. I just rely on a testing pipeline instead so can’t think of why I would need to proactively find holes where tests haven’t already done that for me.
I use similar workflow. Here is my refactoring and code quality prompt that I regularly run:
Thank you!
tests will not find inconsistent naming, duplicate functions, scenarios you have not thought about testing
I use quite plain prompts, nothing fancy:
> go over the tests and do a code review, focusing on how well they test inventory management, planner and controller. maybe some tests need to be deleted, maybe other tests need to be added. the end goal should be good coverage of the core features.
> do a code review, focusing on robustness/correctness issues. validate that the code correctly implements specification.md. focus on the async client.
> there was a big refactor. please do a code review, focusing on eliminating tech debt. look for unused, obsolete or duplicate code that can be removed, look for mismatched interfaces, inconsistent function/argument/variable names. do not output what is correct, just the issues you found. for each issue output instructions for a coding agent on how to fix it. do not nitpick.
Noted, thank you! Appreciate it
Claude code itself spins a lot of its subagents with Haiku. The model has low hallucination rate, so it is great for exploration tasks. I guess this is what the best purpose of this model here will be as well. Which is a lot of tokens - many tasks spin multiple exploration agents before the planning or fixing, that is then just a few tool calls.
I was wondering the same. I guess it makes sense to use a heavy weight model to make the entire design and split the work so that smaller models (possibly local one?) would then do the coding... But how would I even do that? I'm using Claude Code. Would I need support for this within the harness ?
Not sure if considered it's considered small in any way, but DeepSeek V4 Flash is really decent.
From my experience, smaller models like Haïku 4.5 have indeed shown very convincing results on specific, scoped tasks (themselves generated by a more capable model such as Opus 4.6). We use this kind of workflows in production to optimize speed, efficiency, and costs.
i used to use opus for everything, thats not an option once you move to a multi agent system unless you're working on like high end research. I could easily spend 3k a day if i was using opus as just a normal dev.
As we build a better and better harness and better feedback/verifiers we're switching more to 3.5 flash. I think chinese models would work too, but we cant use those atm.
Generally theres a coordinator running opus and an ever growing set of skills and subagents that take actions using weaker models and output feedback to the coordinator opus.
I'm pretty convinced at this point we're past the level of intelligence needed for most tasks most devs do and that will trend down as we better build harnesses for our own codebases.
Implicitly, yes. A lot of harnesses will invoke small models to do small changes, saving time and tokens.
plan using opus execute using local
I keep trying to, because I really want to make qwen 3.6 35b work for end implementation of a fleshed out spec (mostly for local data privacy reasons).
...but I spend so much more time correcting it, or building pipelines to try, retry, and converge, that it's rarely worthwhile for me in either time or $ spent vs Opus.
I use it for smaller changes that I need to make, mainly on UI fixes or some easy logic fixes.
In DeepSWE anything from Antropic is a whole class lower than what's achievable with gpt-5.5
So by using Opus you are using "smaller" model. Well, not really smaller, just worse. The actual smaller models can at least be faster.
I actually find planning/design easier with a smaller model and implementation with a larger one. I'm mostly manually working with the model on planning and design and decisions are mine and smaller models are faster. And when there's a clear design/wayforward, the bigger models are usually better at understanding the overall context and applying the specific patch they were assigned to. I call it the 1-2 punch system where you do the first light punch then the harder punch when its actually important to hit properly. I know it goes against the standard of throwing the biggest model at design but I personally experience the bigger models try to do TOO MUCH and take a lot of time which is something that's not good in the design/arch/boilterplate phase.
It's so weird to me that the benchmarks remain so low, but the models are marketed as revolutionary. And if you say that low coding capabilities aren't a problem, say that to the token price hike and 'general use' model setup.
Why not sell it as a math agent? Why do I have to set up 4 agents to check each others' work?
from what I understand, it's because unlike the other models, MAI models haven't yet fine-tuned against the synthetic datasets specifically designed to boost the benchmark scores.
It’s about bang for buck. That high a score for 5B params is pretty good, nigh unbelievable a short while ago.
It is my belief that smaller models will get better and better, and even cloud SOTA models will shrink.
Yet another reason the current buildout will feel like the railroads.
It's 5B active params in MoE, not 5B total params (total is 137B).
> It’s about bang for buck.
Hard to know when they don't give the price per token. Presumably it will be comparable to a low-mid range model in terms of price. But otherwise their 'Ideal Zone' is meaningless without factoring in the price per token. I don't how much tokens are being used, that's an implementation detail to me. I care about price / performance / latency.
Yeah the future is probably a number of highly specialised small models you can run on your own hardware rather than massive frontier models in the cloud.
That's what I'm betting on anyway.
That seems to be what Microsoft is betting on also based on what was shown at the BUILD keynote today + that new surface ultra and the surface mini PC with the new Nvidia chip. Nadella really played up local AI as the main use case they have in mind.
Step 3.7 Flash on my Asus GB10 based mini pc is incredibly close to that today. I’m very impressed, and that’s without MTP to boost performance
MOE basically work that way already, QWEN/etc with low active params (A-number in name) allows to inference big models locally (only active params have to fit into memory)
The SOTA models will not shrink, because the problems will get bigger, from "write me a C compiler" to "clone Stripe business and run it".
They are comparing it to Haiku 4.5. Not Opus, not Sonnet, but Haiku, the smallest Anthropic model, 3 versions old.
4.5 is still the latest Haiku model
What is with people reimplementing window scrolling badly?
Probably vibe coded. I use StopTheMadness to prevent it.
Immediately noticed that and then closed out.
The introductory blog post has a lot more information
https://microsoft.ai/news/introducingmai-code-1-flash/
and the model card
https://microsoft.ai/pdf/MAI-Code-1-Flash-Model-Card.PDF
The broader announcement of 7 MAI models seems to be where the 5B active in the title comes from
https://microsoft.ai/news/building-a-hillclimbing-machine-la...
Thanks! I've changed the top link to the blog post and put the other links in the toptext.
Please test your websites in Safari. Almost all of your iOS users use it by default, and the desktop experience is pretty close to the mobile experience, so testing is easy.
That scroll effect is jank city for me (yeah yeah works fine in Chrome/Edge).
some kind of scroll hijack going on for sure, feels terrible on firefox+macos
I instantly close websites which use this weird scroll hijacking and slow animation nonsense.
Let me slide as fast and unrestricted as I want. I do not want to "transition" to the next paragraph.
This trend needs to stop.
I had to remind myself what Haiku is even for. Anthropic hasn't spent a lot of recent marketing on it.
When I need a light model, I reach for Sonnet. It is nearly free on the max plans, and quite fast. I don't see a place for Haiku in regular coding.
Haiku I guess is when you need summarization/categorization at scale.
Microsoft setting Haiku as the benchmark is a low bar.
> "It is nearly free on the max plans"
is a funny oxymoron
So it's trained on the SWE Bench Pro evalset
That's not accurate. Take a look at the paper to see what it is trained on! And specifically decontamination is called out in A.4
https://microsoft.ai/wp-content/uploads/2026/06/main_2026060...
What is your evidence for this claim?
They say hill climbing
https://microsoft.ai/news/building-a-hillclimbing-machine-la...
Unless they specifically clarify that the testing and training benchmarks are completely separate, we have to assume they test on the same 'hill' the model climbs.
Hill climbing doesn't mean much but absolutely doesn't imply they cheat on benchmarks. They have more details here https://microsoft.ai/news/introducing-mai-thinking-1/ it seems to be "RL on everything".
If only they had launched that yesterday I might have avoided Copilot auto model selection using a 9x model, quietly burning my monthly quota in a single afternoon.
not open weight or at least I did not find anything indicating open weight
:(
I was hoping Microsoft would make it open weights, as they have done for years with the Phi models.
The era of big tech releasing models into the wild might be over, which IMO is counter-productive, as we are shifting from "the model is the product" to "the harness is the product"
Gemma 4 26B-A4B scored exceptionally well with 20% less params, so this isn't unprecedented.
Copilot brand is tarnished, so time to bung everything under MAI?
Maybe the next Windows update will change This PC back to MAI Computer. ;)
Shouldn’t the next model focus not be on code but system design?
Seems like the work from a good system design to code is practically solved.
Now it’s a matter of the design of the system. Or is that represented in these evals?
Have you tried system design with LLMs? I find them pretty good at suggesting 5 architectures for a problem and then iterating on the solutions.
Even if I had no idea, going with the default suggestion would not be a terrible mistake, assuming you did describe your requirements relatively well.
I don't see the point in comparing yourself to Haiku which is not only useless for coding but also old. No thanks Microsoft.
Scroll wheel hijacked on this entire domain
Fix:
The fix is to close the tab.
Yeah this website is horrendous to use. What were they thinking?
You mean "what was the LLM thinking?"
>Build for developers, not benchmarks
That sounds like something you say when you don't benchmark well
I personally do not like Microsoft, but congrats them to release this model.
While the scores are not good compare to other open weight model, the important thing to note is their training data (as they claimed) is very clean, without any synthetic datasets.
Related ongoing thread:
MAI-Thinking-1 - https://news.ycombinator.com/item?id=48374362 - June 2026 (64 comments)
Mark Zuckerberg must be in crisis. Microsoft releasing models that compete with Claude's models. Meanwhile the only thing anyone knows about Mark's models is that they help you get hacked more easily.
Meta recently launched Muse Spark [1] and they themselves compare against Claude Opus 4.6 Max.
Here Microsoft is comparing against Claude Haiku, the smallest and least capable model from Anthropic.
[1] https://ai.meta.com/blog/introducing-muse-spark-msl/
i have had good results adding muse spark's contemplate mode as a roundtabler for complex questions. but you cant turn off their data ingestion for training so that is a shame.
Wait… I think he has moltbook IP as well that he can scale up.
Seriously tho, wtf is going on over at Meta? Anyone working there currently want to describe the vibe of the org when it comes to being a frontier company?
I don't understand his plan, if I were him I'd either have just gone all in on making RAM which would become very lucrative, or would have focused on building programming models. They've built some key open source technologies, but its as if Mark Zuckerberg cannot run anything that isn't a social media company / project.
You lost me at forced scrolling. Ugh!
From https://news.ycombinator.com/newsguidelines.html
Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting.
It is good to se big companies like Microsoft launching LLMs. They have large amount of compute power and good scientists to create useful models.
Microsoft has been releasing LLMs for years.
Sort of. Phi models were just trained on GPT outputs though.
For those that don't know about this. Phi was announced with a paper called "Textbooks are all you need". What they did was use GPT 3.5 and created synthetic textbook chapters and exercises.
They also did some more interesting work like showing very small models can be coherent as long as you have very simple children's book style training data (TinyStories is pretty famous).
Lots of these ideas are still used. Learning facts at scale with active reading is an ICLR 2026 paper from Meta AI that does a lot of similar work.
By design. The whole point of Phi is the "textbooks is all you need" theory on curated training data, as opposed to kitchen sinks.
And occasionally un-releasing them like with WizardLM.
They were mostly distilled or fine-tuned OAI models.
huh? The granite series isn't distilled
Granite is IBM
What's with the lack of Microsoft design language on the website? It's painfully obvious they're trying to emulate Anthropic's style here and it looks tacky.
Definitely vibed microslop, the giveaway is the broken header and scrolling on mobile.
The broken header is an incredible distraction. I can't believe this slipped through.
Brand guidelines and web design pretty much don't exist any more as far as I can tell. Gotta get it out yesteday, and the only way to do that is vibe coding, styling be damned.
That's neither Microsoft nor Anthropic design. It's from their acquisition of Inflection AI. Even Copilot mobile app design is basically what was Inflection's design
I've always wondered where Consumer CoPilot's design language was from.
If you watch the Build keynote with Satya, you'll notice that the design of the slides changed to Serif typography and warmer colors when Mustafa/Microsoft AI segment came on which was completely different from the rest of the keynote. Now it makes sense!
Tangentially, I've seen a couple people use "CoPilot" here instead of "Copilot" (the actual product name).
Where does the Pascal case inspired variant come from? Is it a reference to something? Is it like "M$" was used back in the days?
maybe it was coded by Claude
Thank you! This website is dreadful for accessibility and usability.
i think it is AI generated.
"It’s not just smarter; it’s leaner"
This is needlessly embarrassing, seems like a small thing, but it makes them look... desperate?
A little to minimalist - only a few hundred words on entire page!
I'd love to see a tokens per second metric. I always prioritize speed over raw intelligence for flash models.
> I always prioritize speed over raw intelligence for flash models.
This model might have a perfect speed:
Leave it long enough, and it'll print the work of Shakespear!
To be clear about the size of the model: MAI-Code-1-Flash is 137B A5B.
wtf are they doing to the scroll on that page
even worse on mobile
Claude Haiku 4.5 results with 60% fewer tokens. Sounds good, but they don't list token costs.
Would be cool if this were an open model.
“ Build for developers, not benchmarks” is the worst marketing shot I ever heard
It claims that, then promptly proceeds to showcase a bunch of benchmarks.
Why do websites still hijack scrolling? It sucks
"Mai" means "never" in Italian. Ain't gonna happen.
微软死磕代码模型。通用模型在追上来了。实际问题：凌晨2点调试屎山代码的时候，这个有用吗。
I'd really like to get back to an autocomplete flow, ideally with some shared and optimized context with the relationship with my larger agent models.
But it seems like, by and large, even the faster models are now aimed at longer-running agentic flows and not sub-1s autocomplete. Or am I wrong about that?
You aren't wrong, the field is moving to a world where we do less in the code editor, so autocomplete is not needed any more. I've only manually edited code a few times in the last month. Haven't used autocomplete in 6+ months since I left Copilot to build my own agent harness (I'm now mainly using OpenCode)
"Clean data" is impossible. Language models have polluted the landscape to such a degree it's impossible to filter them out now. OpenAI has no doubt discarded or muddled their dataset that was used to train the original ChatGPT, so there may be no dataset in existence now that isn't contaminated.
So lame … using haiku 4.5 as comparison
I mean they are comparing themselves to Haiku of all things, geez that's not a good start...
I went to VSC specifically to avoid the pricing I started experiencing on Cursor. After this change I have no reason to stick with GH Copilot, I'd rather keep buying OR credits.
"Build for developers, not benchmarks" Shouldn't that be.. Built?
"superintellegence team"
Why not assign them to make windows good :D
a lot of people got paid way too much for this garbage, enjoy your performance bonuses for taking initiative
So it's not an open model while not being much better? Meh.
is 51% good enough to reliably use? There's no world in which I use an AI agent where it gets even 15% of the code wrong, that's as bad a Tesla FSD where you need to pay attention to the road while engaging FSD. What's the point? My attention is what I'm trying to relieve, not mostly correct functionality. The only thing that matters is whether you can one-shot code like Claude or Codex, I'm not interested in a small but mostly-okay-but-annoyingly-buggy-every-now-and-then AI.
Claude opus 4.6 scores 51.9% on the same benchmark. Microsoft's result is quite good.
51% does not mean it randomly gets things wrong half the time.
These things can be useful if you can accurately predict which tasks they will reliably do, and which they will usually fail on. Then you can get much more reliable work from them.
how long until they rebrand this shit as copilot?
TLDR; this is just Claude Haiku altrenative, you can probably skip whole article.
Comparing against Claude 4.5? Aren't we up to 4.8? But disingenuous?
They're comparing to Haiku, not Opus. Haiku is currently at 4.5.
Even if it were Opus, comparing to a version number makes for an interesting snapshot of time comparison: if you knew how a model performed at whatever time in was in vogue, you can say "well, it looks like Model X is about 6 months/1 year/etc. behind the frontier SOTA" - which is exactly the discussion that happens in the open-weight/local LLM space. (interesting, MAI-Code-1-Flash does not appear to be such an open-weight model, following the western trend of locking models up)
Latest Haiku (smallest Anthropic Model) is version 4.5, they haven't released a new version, hence the comparison to that.