Mistral Small 4 replaces three separate Mistral models - Magistral for reasoning, Devstral for coding agents, and Mistral Small for instruct - with a single 119B MoE model (128 experts, 4 active, 6.5B active params per token). You pick the behavior per request with a reasoning_effort parameter:

  • reasoning_effort="none": fast chat-style responses, equivalent to Mistral Small 3.2
  • reasoning_effort="high": deep step-by-step reasoning, equivalent to Magistral

Same weights, same deployment, different behavior at inference time.

The efficiency numbers are notable: it matches or beats GPT-OSS 120B on LCR, LiveCodeBench, and AIME 2025 while producing 20-75% shorter outputs than comparable Qwen reasoning models at similar scores. Other specs: 256k context window, multimodal (text + image input), Apache 2.0.

NVFP4 quantized checkpoint

Mistral also released an NVFP4 (4-bit float) quantized version (Mistral-Small-4-119B-2603-NVFP4), created with llm-compressor in collaboration with vLLM and Red Hat, with NVIDIA contributing kernel optimizations for vLLM and SGLang. It uses post-training-activation quantization, so accuracy stays close to the BF16 base while significantly reducing memory footprint and improving throughput.

The NVFP4 checkpoint uses a different attention backend flag when serving:

# Base model
vllm serve mistralai/Mistral-Small-4-119B-2603 \
  --attention-backend FLASH_ATTN_MLA \
  --tensor-parallel-size 2 ...

# NVFP4 quantized
vllm serve mistralai/Mistral-Small-4-119B-2603-NVFP4 \
  --attention-backend TRITON_MLA \
  --tensor-parallel-size 2 ...

Both models require a custom vLLM build until the Mistral parsing fixes land in main (expected mid-March 2026). Mistral provides a Docker image (mistralllm/vllm-ms4:latest) with everything pre-patched.

Mistral Small 4 on Hugging Face NVFP4 checkpoint