Mistral Large 2.1 vs Mistral Large

Mistral Large is the better all-round choice, scoring 44/100 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%.

Mistral Large is cheaper at $0.50 input / $1.50 output per million tokens (official Mistral API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 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 2.1 (4× as much).

Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.6 (#142 of 165) and Mistral Large 122.0 (#153 of 165). The confidence ranges of the top two overlap (124.3–130.7 vs 115.7–124.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Mistral Large 38.8%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Mistral Large 1.9%.

There are no published SWE-bench Verified results for Mistral Large 2.1 and Mistral Large 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 Mistral Large. Maximum output per response: Mistral Large 2.1 up to 16,384, Mistral Large up to 102,400 tokens.

Mistral Large 2.1 accepts text; Mistral Large accepts text and PDFs. Mistral Large handles the widest range of inputs.

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

Mistral Large 2.1 is the newest, released Nov 18, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Mistral Large Nov 30, 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.