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ith model size and math- or reasoning-specific pre-training data (see also [4]). This observation aligns with the practices of similar strong, small reasoning models. For example, DeepSeek-R1-Distill-Qwen-1.5B [2] was trained on approximately 1M samples, with Nvidia’s OpenMath-Nemotron-1.5B [5] further training this model on an additional 3M samples. We found Nvidia’s OpenMathReasoning [5] and Llama Nemotron Post-Training [6] datasets particularly effective among other openly available datasets,

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t finding and can be further improved to create higher reward scores for lower-resource languages. We used the real-valued scoring approach to compare three models: Llama 3.3 70B, Deepseek v3, and Deepseek R1. For outputs in formal Indic languages (i.e., not code-mixed or romanised) we found that Deepseek R1 with English thinking tokens and Indic language output in non-thinking tokens, generates consistently the highest quality completions, with an average score of over 8 (on a 0 to 9 scale) acr

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ior open-source SoTA models by more than 6% points. When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 (671B) and Qwen3 232B-A22B. In the table below, we also compare Devstral to closed and open models evaluated under any scaffold (including ones custom for the model). Here, we find that Devstral achieves substantially better performance than a number of closed-source alternatives. For example, Devstral

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ance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems. Additionally, Mistral Medium 3 can also be deployed on any cloud, including self-hosted environments of four GPUs and above. Mistral Medium 3 is designed to be frontier-class, particularly in categories of professional use. In the evaluations below, we use numbers rep

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ans designing for the real world: heterogeneous GPUs, public internet latency, and unreliable but abundant FLOPs. With the rise of reinforcement learning for reasoning models like DeepSeek R1, inference has moved to center stage, and is now a core component of the entire AI stack: Training: Generate rollouts during reinforcement learning (e.g. INTELLECT-2); Distillation: Creating synthetic data at scale (e.g. SYNTHETIC-1); Evaluation: Benchmarking model performance and safety That's why our next

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(PPO)—are often hindered by substantial computational overhead. This challenge is particularly pronounced with models that excel at complex reasoning tasks (such as OpenAI-o1 and DeepSeek-R1), where generating long chain-of-thought (CoT) outputs can account for up to 90% of total training time. These models must produce detailed, step-by-step reasoning that can span thousands of tokens, making inference significantly more time-consuming than the training phase itself. As a pioneering inference

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idth interconnects between each GPU, like NVLink or InfiniBand, to minimize overhead from the increased communication costs. Pipeline Parallelism For extremely large models (e.g., DeepSeek R1, Llama 3.1 405B), a single node may not suffice. Pipeline parallelism shards models across nodes, each handling specific contiguous model layers. How It Works Each GPU loads and processes a distinct set of layers; Send/Receive Operations: Intermediate activations are transmitted between GPUs as computation

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a powerful base for accelerating progress. Note that Mistral Small 3 is neither trained with RL nor synthetic data, so is earlier in the model production pipeline than models like Deepseek R1 (a great and complementary piece of open-source technology!). It can serve as a great base model for building accrued reasoning capacities. We look forward to seeing how the open-source community adopts and customizes it. Performance Human Evaluations We conducted side by side evaluations with an external t

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implementing the speculative decoding support; Kuntai Du is implementing the prefill disaggregation and KV Cache transfer support; Simon Mo and Zhuohan Li contributed to the V1 system design Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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policy initializations for online reinforcement learning. We use this insight to train INTELLECT-MATH, a state-of-the-art model for mathematical reasoning (prior to the release of Deepseek-R1) in the 7B parameter regime that reduces the training time required to match the existing state-of-the-art by 10x. Additionally, we release NuminaMath-QwQ-CoT-5M, a dataset containing five million reasoning traces across 860k mathematics questions verified for correctness. All of our code as well as weights

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o the same mechanism of "using a format for the model to structurally sampling outputs.” ↩; See this blog post from HuggingFace for using logit processors to control the generation process. ↩ Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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has highest throughput on ShareGPT and Decode-heavy dataset (though the throughput is only marginally higher than TensorRT-LLM), but the throughput of vLLM is lower on Prefill-heavy dataset. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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ing popular features like stop strings and parallel sampling (e.g., beam search). We do expect the DeepSpeed-FastGen is eager to catch up and we welcome the creative innovation in the market! Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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ire team — Siyuan Zhuang, Ying Sheng, Lianmin Zheng (UC Berkeley), Cody Yu (Independent Researcher), Joey Gonzalez (UC Berkeley), Hao Zhang (UC Berkeley & UCSD), and Ion Stoica (UC Berkeley). Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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