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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

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 regular gaming GPU it's a lot more accessible than the full blown R1. https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-...

adventured 1y ago
It's because DeepSeek was a fast copy. That was the easy part and it's why they didn't have to use so much compute to get near the top. Going well beyond o3 or 2.5 Pro is drastically more expensive than fast copy. China's cultural approach to building substantial things produces this sort of outcome regularly, you see the same approach in automobiles, planes, Internet services, industrial machinery, military, et al. Innovation is very expensive and time consuming, fast copy is more often very inexpensive and rapid. 85% good enough is often good enough, that additional 10-15% is comically expensive and difficult as you climb.
- MaxPock 1y ago
  I understand that the French are very innovative so why isn't their model SOTA ?
- deleted 1y ago
  [deleted]
- natrys 1y ago
  Not disagreeing with the overarching point but: > That was the easy part Is a bit hand-wavy in that it doesn't explain why it's only DeepSeek who can do this "easy" thing, but still not Meta, Mistral or anyone else really. There are many other players who have way more compute than DeepSeek (even inside China, not even considering rest of the world), and I can assure you more or less everyone trains on synthetic data/distillation from whatever bigger model they can access.
  - refulgentis 1y ago
    They all have. I don't hope to convince you of that, everyones use case differs. Generally, AIME / prose / code benchmarks that don't involve successive tool calls are used to hide some very dark realities. IMHO tool calling is by far the most clearly economically valuable function for an LLM, and r1 self-admittedly just...couldn't do it. There's a lot of puff out there that's just completely misaligned with reality, ex. Gemini 2.5 Pro is by far the worst tool caller, Gemini 2.5 Flash thinking is better, 2.5 Flash is even better. And either Llama 4 beats all Gemini 2.5s except 2.5 Flash not thinking. I'm all for "these differences will net out in the long run", Google's at least figured out how to micro optimize for Aider edit formatting without tools. Over the last 3 months, they're up 10% on edit performance. But it's horrible UX to have these specially formatted code blocks in the middle of prose. They desperately need to clean up their absurd tool-calling system. But I've been saying that for a year now. And they don't take it seriously, at all. One of their most visible leads tweeted "hey what are the best edit formats?" and a day later is tweeting the official guide for doing edits. I'm a Xoogler and that absolutely reeks of BigCo dysfunction - someone realized a problem 2 months after release and now we have "fixed" it without training, and now that's the right way to do things. Because if it isn't, well, what would we do? Shrugs I'm also unsure how much longer it's worth giving a pass on this stuff. Everyone is competing on agentic stuff because that's the golden goose, real automation, and that needs tools. It would be utterly unsurprising to me for Google to keep missing a pain signal on this, vis a vis Anthropic, which doubled down on it mid-2024. As long as I'm dumping info, BFCL is not a good proxy for this quality. Think "converts prose to JSON" not "file reading and editing"
    - natrys 1y ago
      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 not really invested in this niche topic but I will observe that, yes I agree Llama 4 is really good here. And yet it's a far worse coder, far less intelligent than DeepSeek and that's not even arguable. So no it didn't "catch up" any more than what you could say by pointing out Llama is multimodal but DeepSeek isn't. That's just talking about a different things entirely. Regardless, I do agree BFCL is not the best measure either, the Tau-bench is more real world relevant. But end of the day, most frontier labs are not incentive aligned to care about this. Meta cares because this is something Zuck personally cares about, Llama models are actually for small businesses solving grunt automation, not for random people coding at home. People like Salesforce care (xLAM), even China had GLM before DeepSeek was a thing. DeepSeek might care so long as it looks good for coding benchmarks, but that's pretty much the extent of it. And I suspect Google doesn't truly care because in the long run they want to build everything themselves. They already have a CodeAssist product around coding which likely uses fine-tune of their mainline Gemini models to do something even more specific to their plugin. There is a possibility that at the frontier, models are struggling to be better in a specific and constrained way, without getting worse at other things. It's either this, or even Anthropic has gone rogue because their Aider scores are way down now from before. How does that make sense if they are supposed to be all around better at agentic stuff in tool agnostic way? Then you realise they now have Claude Coder and it just makes way more economic sense to tie yourself to that, be context inefficient to your heart's content so that you can burn tokens instead of being, you know, just generally better.
- orbital-decay 1y ago
  This terrible and vague stereotyping about "China" while having no clue about the subject should have no place on HN but somehow always creeps in and is upvoted by someone. DeepSeek is not "China", they had nobody to copy from, they released their first 7B reasoning model back in April 2024, it was ahead of then-SotA models in math and validated their approach. They did a ton of new things besides training a reasoning model, and likely have more to come, as they have a completely different background than most AI companies. It's more of a cross-pollination of different areas of expertise.
  - SoMomentary 1y ago
    I thought it had been bandied about that Deepseek had exfiltrated a bunch of data from OpenAI's models, which was then used to train theirs? Did this ultimately prove untrue? My apologies, I don't always keep up on the latest drama in the AI circles - so maybe that has been well proven wrong.
    - orbital-decay 1y ago
      Sam Altman threw a fit and claimed this, without providing evidence. He's... not exactly a person to trust blindly. Training on other model outputs (or at least doing sanity checks against them) is pretty common, but these models seem very different, DS has prior art, and by all signs this claim makes little sense and is hard to believe.
    - glomgril 1y ago
      one man's exfiltration is another man's distillation `¯\_(ツ)_/¯` you could say they're playing by a different set of rules, but distilling from the best available model is the current meta across the industry. only they know what fraction of their post-training data is generated from openai models, but personally i'd bet my ass it's greater than zero because they are clearly competent and in their position it would have been dumb to not do this. however you want to frame it, they have pushed the field forward -- especially in the realm of open-weight models.
reissbaker 1y ago
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...
- redman25 1y ago
  It may not have been intentionally misleading. Some benchmarks can take a lot of horsepower and time to run. Their preparation for release likely was done well in advance of the model release before the new deepseek r1 model had even been available to test.
  - reissbaker 1y ago
    AIME24, etc are pretty cheap to run using any DeepSeek API. Regardless, they didn't even run the benchmarks for R1 themselves, they just republished DeepSeek's published numbers from January. They could have published the ones from May, but chose not to.
- hmottestad 1y ago
  Mistral isn’t using misleading benchmarks. I linked to DeepSeek’s own benchmark results that DeepSeek created. I couldn’t find anything newer. Can you link me to the benchmark you found?

adventured 1y ago

It's because DeepSeek was a fast copy. That was the easy part and it's why they didn't have to use so much compute to get near the top. Going well beyond o3 or 2.5 Pro is drastically more expensive than fast copy. China's cultural approach to building substantial things produces this sort of outcome regularly, you see the same approach in automobiles, planes, Internet services, industrial machinery, military, et al. Innovation is very expensive and time consuming, fast copy is more often very inexpensive and rapid. 85% good enough is often good enough, that additional 10-15% is comically expensive and difficult as you climb.

MaxPock 1y ago
I understand that the French are very innovative so why isn't their model SOTA ?
deleted 1y ago
[deleted]
natrys 1y ago
Not disagreeing with the overarching point but: > That was the easy part Is a bit hand-wavy in that it doesn't explain why it's only DeepSeek who can do this "easy" thing, but still not Meta, Mistral or anyone else really. There are many other players who have way more compute than DeepSeek (even inside China, not even considering rest of the world), and I can assure you more or less everyone trains on synthetic data/distillation from whatever bigger model they can access.
- refulgentis 1y ago
  They all have. I don't hope to convince you of that, everyones use case differs. Generally, AIME / prose / code benchmarks that don't involve successive tool calls are used to hide some very dark realities. IMHO tool calling is by far the most clearly economically valuable function for an LLM, and r1 self-admittedly just...couldn't do it. There's a lot of puff out there that's just completely misaligned with reality, ex. Gemini 2.5 Pro is by far the worst tool caller, Gemini 2.5 Flash thinking is better, 2.5 Flash is even better. And either Llama 4 beats all Gemini 2.5s except 2.5 Flash not thinking. I'm all for "these differences will net out in the long run", Google's at least figured out how to micro optimize for Aider edit formatting without tools. Over the last 3 months, they're up 10% on edit performance. But it's horrible UX to have these specially formatted code blocks in the middle of prose. They desperately need to clean up their absurd tool-calling system. But I've been saying that for a year now. And they don't take it seriously, at all. One of their most visible leads tweeted "hey what are the best edit formats?" and a day later is tweeting the official guide for doing edits. I'm a Xoogler and that absolutely reeks of BigCo dysfunction - someone realized a problem 2 months after release and now we have "fixed" it without training, and now that's the right way to do things. Because if it isn't, well, what would we do? Shrugs I'm also unsure how much longer it's worth giving a pass on this stuff. Everyone is competing on agentic stuff because that's the golden goose, real automation, and that needs tools. It would be utterly unsurprising to me for Google to keep missing a pain signal on this, vis a vis Anthropic, which doubled down on it mid-2024. As long as I'm dumping info, BFCL is not a good proxy for this quality. Think "converts prose to JSON" not "file reading and editing"
  - natrys 1y ago
    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 not really invested in this niche topic but I will observe that, yes I agree Llama 4 is really good here. And yet it's a far worse coder, far less intelligent than DeepSeek and that's not even arguable. So no it didn't "catch up" any more than what you could say by pointing out Llama is multimodal but DeepSeek isn't. That's just talking about a different things entirely. Regardless, I do agree BFCL is not the best measure either, the Tau-bench is more real world relevant. But end of the day, most frontier labs are not incentive aligned to care about this. Meta cares because this is something Zuck personally cares about, Llama models are actually for small businesses solving grunt automation, not for random people coding at home. People like Salesforce care (xLAM), even China had GLM before DeepSeek was a thing. DeepSeek might care so long as it looks good for coding benchmarks, but that's pretty much the extent of it. And I suspect Google doesn't truly care because in the long run they want to build everything themselves. They already have a CodeAssist product around coding which likely uses fine-tune of their mainline Gemini models to do something even more specific to their plugin. There is a possibility that at the frontier, models are struggling to be better in a specific and constrained way, without getting worse at other things. It's either this, or even Anthropic has gone rogue because their Aider scores are way down now from before. How does that make sense if they are supposed to be all around better at agentic stuff in tool agnostic way? Then you realise they now have Claude Coder and it just makes way more economic sense to tie yourself to that, be context inefficient to your heart's content so that you can burn tokens instead of being, you know, just generally better.
orbital-decay 1y ago
This terrible and vague stereotyping about "China" while having no clue about the subject should have no place on HN but somehow always creeps in and is upvoted by someone. DeepSeek is not "China", they had nobody to copy from, they released their first 7B reasoning model back in April 2024, it was ahead of then-SotA models in math and validated their approach. They did a ton of new things besides training a reasoning model, and likely have more to come, as they have a completely different background than most AI companies. It's more of a cross-pollination of different areas of expertise.
- SoMomentary 1y ago
  I thought it had been bandied about that Deepseek had exfiltrated a bunch of data from OpenAI's models, which was then used to train theirs? Did this ultimately prove untrue? My apologies, I don't always keep up on the latest drama in the AI circles - so maybe that has been well proven wrong.
  - orbital-decay 1y ago
    Sam Altman threw a fit and claimed this, without providing evidence. He's... not exactly a person to trust blindly. Training on other model outputs (or at least doing sanity checks against them) is pretty common, but these models seem very different, DS has prior art, and by all signs this claim makes little sense and is hard to believe.
  - glomgril 1y ago
    one man's exfiltration is another man's distillation `¯\_(ツ)_/¯` you could say they're playing by a different set of rules, but distilling from the best available model is the current meta across the industry. only they know what fraction of their post-training data is generated from openai models, but personally i'd bet my ass it's greater than zero because they are clearly competent and in their position it would have been dumb to not do this. however you want to frame it, they have pushed the field forward -- especially in the realm of open-weight models.

reissbaker 1y ago

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...

redman25 1y ago
It may not have been intentionally misleading. Some benchmarks can take a lot of horsepower and time to run. Their preparation for release likely was done well in advance of the model release before the new deepseek r1 model had even been available to test.
- reissbaker 1y ago
  AIME24, etc are pretty cheap to run using any DeepSeek API. Regardless, they didn't even run the benchmarks for R1 themselves, they just republished DeepSeek's published numbers from January. They could have published the ones from May, but chose not to.
hmottestad 1y ago
Mistral isn’t using misleading benchmarks. I linked to DeepSeek’s own benchmark results that DeepSeek created. I couldn’t find anything newer. Can you link me to the benchmark you found?