GPT-6 Luna vs Mistral Small 3.2

GPT-6 Luna is the better all-round choice, scoring 86/100 against Mistral Small 3.2 (52). 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%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because GPT-6 Luna has no Capabilities Index score yet.

Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). GPT-6 Luna costs $0.10 input / $0.50 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 $0.20 for GPT-6 Luna (1.3× as much).

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): GPT-6 Luna 94.7% and Mistral Small 3.2 39.7%. On individual benchmarks: GPQA Diamond — GPT-6 Luna 90.5%, Mistral Small 3.2 49.1%; OTIS Mock AIME 2024–2025 — GPT-6 Luna 98.9%, Mistral Small 3.2 30.3%.

There are no published SWE-bench Verified results for GPT-6 Luna and Mistral Small 3.2 yet, so there is no like-for-like coding score. On overall capability, GPT-6 Luna leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-6 Luna has the largest context window at 1,050,000 tokens, against 128,000 for Mistral Small 3.2. Maximum output per response: GPT-6 Luna up to 128,000, Mistral Small 3.2 up to 16,384 tokens.

GPT-6 Luna accepts text, images and PDFs; Mistral Small 3.2 accepts text and images. GPT-6 Luna handles the widest range of inputs.

Mistral Small 3.2 publishes its weights and can be self-hosted; GPT-6 Luna is proprietary.

GPT-6 Luna is the newest, released Sep 22, 2026. Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: GPT-6 Luna May 18, 2026, 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.