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Magistral — the first reasoning model by Mistral AI

mistral.ai

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Re: Magistral — the first reasoning model by Mistral AI

#301

Post by trebligdivad »

danielhanchen wrote: post #3

I made some GGUFs for those interested in running them at https://huggingface.co/unsloth/Magistral-Small-2506-GGUF ollama run hf.co/unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL or ./llama.cpp/llama-cli -hf unsloth/Magistral-Small-2506-GGUF:UD-Q4_K_XL --jinja --temp 0.7 --top-k -1 --top-p 0.95 -ngl 99 Please use --jinja for llama.cpp and use temperature = 0.7, top-p 0.95! Also best to increase Ollama's context length…

Nice! I'm running on CPU only, so it's interesting to compare - the Magistral-Small-2506_Q8_0.gguf runs at under 2 tokens/s on my 16 core, but your UD-IQ2_XXS gets about 5.5 tokens/s which is fast enough to be useful - but it does hallucinate a bit more and loop a little; but still actually pretty good for something so small.

Re: Magistral — the first reasoning model by Mistral AI

#302

Post by natrys »

refulgentis wrote: post #298
natrys wrote: post #297

Earlier quoted context omitted.

I don't mind the info dump, but I am struggling to connect the relevance of this to topic at hand. I mean, focusing on a single specific capability and generalising it to mean "they all have" caught up with DeepSeek all across the board (which was the original topic) is a reductive and wild take. Especially when it seems to me that this seems more because of misaligned incentive than because it's truly a hard problem…

> I am struggling to connect the relevance of this > focusing on a single specific capability and > I am not really invested in this niche topic Right: I definitely ceded a "but it doesn't matter to me!" argument in my comment. I sense a little "doth protest too much", in the multiple paragraphs devoted to taking that and extending it to the underpinning of automation is "irrelevant" "single" "specific", "niche". Thi…

Interesting presumption about R1 25-01 being what's talked about, you knowledge cut-off does appear to know R1 update two weeks back was a thing, and that it even improved on function calling.

Of course you have to pretend I meant the former, otherwise "they all have" doesn't entirely make sense. Not that it made total sense before either, but if I say your definition of "they" is laughably narrow, I suspect you will go back to your google contact and confirm that nothing else really exists outside it.

Oh and do a ctrl-f on "irrelevant" please, perhaps some fact grounding is in order. There was an interesting conversation to be had about underpinning of automation somehow without intelligence (Llama 4) but who has time for that if we can have hallucination go hand in hand with forced agendas (free disclaimer to boot) and projection ("doth protest too much")? Truly unforeseeable.

Re: Magistral — the first reasoning model by Mistral AI

#303

Post by hdjrudni »

throwaway0123_5 wrote: post #152
1propionyl wrote: post #129

Earlier quoted context omitted.

Yes, specifically when it comes to open-ended research or development, collocation is non-negotiable. There are greater than linear benefits in creativity of approach, agility in adapting to new intermediate discoveries, etc that you get by putting a number of talented people who get along in the same space who form a community of practice. Remote work and flattening communication down to what digital media (Slack, Z…

I think they were talking about total time spent working rather than remote vs. in-person. I've seen more than a few studies over the years showing that going from 40 to 35 or 30 hours/wk has minimal or positive impacts on productivity. Idk if that would apply to all work environments though, and I don't recall any of the studies being about research productivity specifically.

> I think they were talking about total time spent working rather than remote vs. in-person.

I was, yes. I should have omitted the "in office" part but I was referencing the "work more hours in America than France"

Re: Magistral — the first reasoning model by Mistral AI

#304

Post by reissbaker »

hmottestad wrote: post #187
pu_pe wrote: post #11

Benchmarks suggest this model loses to Deepseek-R1 in every one-shot comparison. Considering they were likely not even pitting it against the newer R1 version (no mention of that in the article) and at more than double the cost, this looks like the best AI company in the EU is struggling to keep up with the state-of-the-art.

With how amazing the first R1 model was and how little compute they needed to create it, I'm really wondering how the new R1 model isn't beating o3 and 2.5 Pro on every single benchmark. Magistral Small is only 24B and scores 70.7% on AIME2024 while the 32B distill of R1 scores 72.6%. And with majority voting @64 the Magistral Small manages 83.3%, which is better than the full R1. Since I can run a 24B model on a reg…

It's not better than full R1; Mistral is using misleading benchmarks. The latest version of R1, R1-0528, is much better: 91.4% on AIME2024 pass@1. Mistral uses the original R1 release from January in their comparisons, presumably because it makes their numbers look more competitive.

That being said, it's still very impressive for a 24B.

I'm really wondering how the new R1 model isn't beating o3 and 2.5 Pro on every single benchmark.

Sidenote, but I'm pretty sure DeepSeek is focused on V4, and after that will train an R2 on top. The V3-0324 and R1-0528 releases weren't retrained from scratch, they just continued training from the previous V3/R1 checkpoints. They're nice bumps, but V4/R2 will be more significant.

Of course, OpenAI, Google, and Anthropic will have released new models by then too...

Re: Magistral — the first reasoning model by Mistral AI

#305

Post by RandyOrion »

Below are my comments on Magistral small (not medium).

24B size is good for local inference.

As a model outputting long "reasoning" traces (~10k tokens), 40k context length is a little concerning.

Where are the results of normal benchmarks, e.g., MMLU/pro, IFEval and such.

Still, thank you Mistral team for releasing this model with Apache 2.0.

Re: Magistral — the first reasoning model by Mistral AI

#306

Post by Vetch »

refulgentis wrote: post #298
natrys wrote: post #297

Earlier quoted context omitted.

I don't mind the info dump, but I am struggling to connect the relevance of this to topic at hand. I mean, focusing on a single specific capability and generalising it to mean "they all have" caught up with DeepSeek all across the board (which was the original topic) is a reductive and wild take. Especially when it seems to me that this seems more because of misaligned incentive than because it's truly a hard problem…

> I am struggling to connect the relevance of this > focusing on a single specific capability and > I am not really invested in this niche topic Right: I definitely ceded a "but it doesn't matter to me!" argument in my comment. I sense a little "doth protest too much", in the multiple paragraphs devoted to taking that and extending it to the underpinning of automation is "irrelevant" "single" "specific", "niche". Thi…

I think the point remains that few have been able to catch up to OpenAI. For a while it was just Anthropic. Then Google after failing a bunch of times. So, if we relax this to LLMs not by OpenAI, Anthropic or Google, then Deepseek is really the only one that's managed to reach their quality tier (even though many others have thrown their hat into the ring). We can also get approximate glimpses into which models people use by looking at OpenRouter, sorted by Top Weekly.

In the top 10, are models by OpenAI (gpt4omini), Google (gemini flashes and pros), Anthropic (Sonnets) and Deepseeks'. Even though the company list grows shorter if we instead look at top model usage grouped by order of magnitude, it retains the same companies.

Personally, the models meeting my quality bar are: gpt 4.1, o4-mini, o3, gpt2.5pro, gemini2.5flash (not 2.0), claude sonnet, deepseek and deepseek r1 (both versions). Claude Sonnet 3.5 was the first time I found LLMs to be useful for programming work. This is not to say there are no good models by others (such as Alibaba, Meta, Mistral, Cohere, THUDM, LG, perhaps Microsoft), particularly in compute constrained scenarios, just that only Deepseek reaches the Quality tier of the big 3.

Re: Magistral — the first reasoning model by Mistral AI

#308

Post by zer00eyz »

LordDragonfang wrote: post #288
bobsomers wrote: post #276

Earlier quoted context omitted.

We are aware of the term of art. The point that was trying to be made, which I agree with, is that anthropomorphizing a statistical model isn’t actually helpful. It only serves to confuse laypersons into assuming these models are capable of a lot more than they really are. That’s perfect if you’re a salesperson trying to dump your bad AI startup onto the public with an IPO, but unhelpful for pretty much any other rea…

If that was their point, it would have been more constructive to actually make it. To your point, it's only anthropomorphization if you make the anthrocentric assumption that "thinking" refers to something that only humans can do.[1] And I don't think it confuses laypeople, when literally telling it to "think" achieves the very similar results as in humans - it produces output that someone provided it out-of-context…

> It also begs the question of whether there exists a clear and narrow definition of what "thinking" is that everyone can agree on. I suspect if you ask five philosophers you'll get six different answers, as the saying goes.

And yet we added a hand wavy 7th to humanize a peice of technology.

Re: Magistral — the first reasoning model by Mistral AI

#310

Post by ath92 »

adventured wrote: post #226
8n4vidtmkvmk wrote: post #94

Earlier quoted context omitted.

Are we sure more time butt in office equates to more productivity?

$89,000 GDP per capita vs $46,000 rather proves the point about productivity per butt. US office workers are extraordinarily productive in terms of what their work generates (thanks to numerous well understood things like the outsized US scaling abilities). Measuring beyond that is very difficult due to the variance of every business.

Weird take. Norway has about the same gdp per capita as the USA with stricter regulations than France. Ireland’s GDP per capita is higher than that of the USA, with less bureaucracy than France but more than the US. Not to mention that all of these are before adjusting for PPP. Almost as if GDP per capita is not a good measurement of productivity.
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