GPT-5.4 nano vs Mistral Small 3.2

GPT-5.4 nano is the better all-round choice, scoring 68/100 against Mistral Small 3.2 (60). It leads on capability, inputs & features and context window. Mistral Small 3.2 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $0.463 for GPT-5.4 nano (3.1× as much).

GPT-5.4 nano scores higher on the Capabilities Index (ECI): GPT-5.4 nano 145.8 (#75 of 148) and Mistral Small 3.2 131.7 (#123 of 148). Their confidence ranges do not overlap (143.2–147.7 vs 126.6–133.9), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-5.4 nano 78.5%, Mistral Small 3.2 49.1%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, Mistral Small 3.2 30.3%.

There are no published SWE-bench Verified results for GPT-5.4 nano and Mistral Small 3.2 yet, so there is no like-for-like coding score. On overall capability, GPT-5.4 nano leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5.4 nano has the largest context window at 400,000 tokens, against 128,000 for Mistral Small 3.2. Maximum output per response: GPT-5.4 nano up to 128,000, Mistral Small 3.2 up to 16,384 tokens.

GPT-5.4 nano accepts text and images; Mistral Small 3.2 accepts text and images. They handle the same number of input types.

Mistral Small 3.2 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

GPT-5.4 nano is the newest, released Mar 17, 2026. Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, Mistral Small 3.2 Mar 2025.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.