Mistral Large vs Mistral Large 2407

Mistral Large is the better all-round choice, scoring 44/100 against Mistral Large 2407 (39). It leads on price and inputs & features. Mistral Large 2407 wins on capability. 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). Mistral Large 2407 costs $2.00 input / $6.00 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.75 per million tokens for Mistral Large versus $3.00 for Mistral Large 2407 (4× as much).

Mistral Large 2407 scores higher on the Capabilities Index (ECI): Mistral Large 2407 127.6 (#144 of 165) and Mistral Large 122.0 (#153 of 165). The confidence ranges of the top two overlap (123.3–129.4 vs 115.7–124.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2407 49.0%, Mistral Large 38.8%; OTIS Mock AIME 2024–2025 — Mistral Large 2407 8.5%, Mistral Large 1.9%.

There are no published SWE-bench Verified results for Mistral Large and Mistral Large 2407 yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2407 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Mistral Large 2407 has the largest context window at 131,072 tokens, against 128,000 for Mistral Large. Maximum output per response: Mistral Large up to 102,400, Mistral Large 2407 up to 104,857 tokens.

Mistral Large accepts text and PDFs; Mistral Large 2407 accepts text and PDFs. They handle the same number of input types.

Mistral Large 2407 publishes its weights and can be self-hosted; Mistral Large is proprietary.

Mistral Large 2407 is the newest, released Jul 24, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 30, 2024, Mistral Large 2407 Mar 31, 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 Nov 2, 2026.