Qwen2.5 7B Instruct vs Mistral Large

Qwen2.5 7B Instruct is the better all-round choice, scoring 50/100 against Mistral Large (44). It leads on price. Mistral Large wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Mistral Large costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.75 for Mistral Large (12× as much).

Mistral Large scores higher on the Capabilities Index (ECI): Mistral Large 122.0 (#153 of 165) and Qwen2.5 7B Instruct 118.5 (#159 of 165). The confidence ranges of the top two overlap (115.7–124.8 vs 110.6–121.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 38.8%, Qwen2.5 7B Instruct 35.5%; OTIS Mock AIME 2024–2025 — Qwen2.5 7B Instruct 2.5%, Mistral Large 1.9%.

There are no published SWE-bench Verified results for Qwen2.5 7B Instruct and Mistral Large yet, so there is no like-for-like coding score. On overall capability, Mistral Large leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Large. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Mistral Large up to 102,400 tokens.

Qwen2.5 7B Instruct accepts text; Mistral Large accepts text and PDFs. Mistral Large handles the widest range of inputs.

Qwen2.5 7B Instruct publishes its weights (Apache 2.0) and can be self-hosted; Mistral Large is proprietary.

Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: 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.