Model datasheet · Mistral Small
Mistral Small 4
Mistral folded Magistral, Pixtral and Devstral into one model: reasoning, vision and agentic coding, Apache licensed, 6.5B active of 119B. The catch is attention. It kept full multi-head attention where everything else its size uses grouped-query, so its KV cache costs 576KB per token — the most expensive on this site, and more than a 358B model's. Cheap to compute, dear to give context to.
- Vendor
- Mistral AI
- Architecture
- MoE · 119B total / ~6.5B active
- Context
- 262,144 tokens
- License
- Apache-2.0
- Released
- 2026-03-16
- Vision
- Yes
§1 Characteristics by quantization band
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q3_K_M · aggregated · 2026-08-31
Editorial derivation: two points down. At 3-bit a sparse MoE loses expert-routing precision first, which surfaces as dropped tool arguments and broken multi-file edits before prose visibly suffers.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q4_K_M · aggregated · 2026-08-31
Editorial derivation: one point down from the measured bf16 figure. 4-bit is the band a model this size is actually run at, and the loss is real but modest.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q4_K_M · aggregated · 2026-08-31
Editorial derivation: one point down from the measured bf16 figure. 4-bit is the band a model this size is actually run at, and the loss is real but modest.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31
Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31
Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31
Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size.
data checked Aug 2026
- quant-degradation-community — editorial band derivation @ Q6_K · aggregated · 2026-08-31
Editorial derivation: no drop from the measured bf16 figure. 6-8 bit is effectively lossless on a model this size.
FP16 — full precision (fp16/bf16)
238GB weights · e.g. bf16 + KV: 4.5GB at 8K · 18GB at 32K · 72GB at 128Kdata checked Aug 2026
- vendor-model-card — AIME 2025 = 83.8 @ bf16 · vendor-reported · 2026-08-31
GPQA Diamond 71.2. Reasoning is toggled per request with reasoning_effort, so the figure assumes it is on.
data checked Aug 2026
- vendor-model-card — LiveCodeBench = 63.6 @ bf16 · vendor-reported · 2026-08-31
Mistral claim it beats gpt-oss-120b here while producing 20% less output. No SWE-bench figure is published, which is the number that would matter most for the agentic coding it absorbed from Devstral.
data checked Aug 2026
- vendor-model-card — MMLU-Pro = 78 @ bf16 · vendor-reported · 2026-08-31
MMMU-Pro 60 on the vision side. No hallucination-rate benchmark is published, so this is a knowledge score rather than a truthfulness one.
data checked Aug 2026
- artificial-analysis — AA-LCR long-context reasoning (link-only — no numbers republished) @ bf16 · aggregated · 2026-08-31
Cited by link under their terms. Mistral report it as matching gpt-oss-120b. Worth reading against this model's KV cost: the context is affordable to reason over and expensive to hold.
§2 Known issues & what fixes them
The most expensive KV cache in this catalogue, and by a long way. Its config keeps full multi-head attention — 32 key-value heads against 32 attention heads — where every comparable model uses grouped-query attention to shrink exactly this. The result is 576KB per token: 18GB of cache at 32K context, about 72GB at 128K, on top of 74GB of 4-bit weights. A 358B model with grouped-query attention costs less. The sparsity that makes it fast to run does nothing for the memory it needs to hold a conversation.
Architectural. Budget the cache explicitly — the RUNS AT line prices it at the context you pick — or run it at short context, which is where its 6.5B active parameters pay off.
Evidence · 2 sources · community-consensus
- Mistral Small 4 config.json: num_key_value_heads 32 equals num_attention_heads 32 (vendor, 2026-08-31)
- unsloth GGUF sizes: UD-Q4_K_M 73.8GB of weights before any cache (other, 2026-08-31)
Sold as the model that absorbed Devstral's agentic coding, and published with no tool-calling or instruction-following benchmark to show for it — no BFCL, no IFEval, no SWE-bench. Those cells are blank here rather than estimated, which the coverage discount then penalises. Every figure that does exist is Mistral's own; no independent replication was found.
A gap in the evidence, not a defect in the model. Treat the agentic claim as unverified until someone benchmarks it.
Evidence · 2 sources · community-consensus
- Mistral Small 4 announcement: AIME, MMLU-Pro, GPQA and LiveCodeBench only (vendor, 2026-08-31)
- Model card publishes no BFCL, IFEval or SWE-bench figure (vendor, 2026-08-31)