GPT-4o mini vs Mistral Large 2.1

GPT-4o mini is the better all-round choice, scoring 56/100 against Mistral Large 2.1 (38). It leads on price and inputs & features. Mistral Large 2.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-4o mini is cheaper at $0.15 input / $0.60 output per million tokens (official OpenAI API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for GPT-4o mini versus $3.00 for Mistral Large 2.1 (11× as much).

Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.5 (#130 of 148) and GPT-4o mini 126.6 (#135 of 148). The confidence ranges of the top two overlap (123.8–130.8 vs 120.5–128.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, GPT-4o mini 37.7%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, GPT-4o mini 6.9%.

There are no published SWE-bench Verified results for GPT-4o mini and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Mistral Large 2.1 has the largest context window at 131,072 tokens, against 128,000 for GPT-4o mini. Maximum output per response: GPT-4o mini up to 16,384, Mistral Large 2.1 up to 16,384 tokens.

GPT-4o mini accepts text, images and PDFs; Mistral Large 2.1 accepts text. GPT-4o mini handles the widest range of inputs.

Mistral Large 2.1 publishes its weights and can be self-hosted; GPT-4o mini is proprietary.

Mistral Large 2.1 is the newest, released Nov 18, 2024. GPT-4o mini came out Jul 18, 2024. Knowledge cutoff: GPT-4o mini Sep 2023, Mistral Large 2.1 Nov 2024.

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.