Mistral Large vs GPT-4o

It is close. Our weighted score puts them within a point (Mistral Large 44/100, GPT-4o 43/100), so choose by what matters most for your work: GPT-4o for raw capability and Mistral Large on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Mistral Large is cheaper at $0.50 input / $1.50 output per million tokens (official Mistral API price). GPT-4o costs $2.50 input / $10.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.75 per million tokens for Mistral Large versus $4.38 for GPT-4o (5.8× as much).

GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165) and Mistral Large 122.0 (#153 of 165). The confidence ranges of the top two overlap (124.7–131.3 vs 115.7–124.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4o 48.9%, Mistral Large 38.8%; OTIS Mock AIME 2024–2025 — GPT-4o 6.3%, Mistral Large 1.9%.

There are no published SWE-bench Verified results for Mistral Large and GPT-4o yet, so there is no like-for-like coding score. On overall capability, GPT-4o leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Mistral Large and GPT-4o share the same 128,000-token context window. Maximum output per response: Mistral Large up to 102,400, GPT-4o up to 16,384 tokens.

Mistral Large accepts text and PDFs; GPT-4o accepts text, images and PDFs. GPT-4o handles the widest range of inputs.

No. Mistral Large and GPT-4o are proprietary and only available through APIs and apps.

GPT-4o is the newest, released May 13, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 30, 2024, GPT-4o Sep 2023.

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 Nov 2, 2026.