Mistral Large 2.1 vs Mistral Nemo vs Qwen2.5 7B Instruct

Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 7B Instruct (44) and Mistral Large 2.1 (38). It leads on price. Mistral Large 2.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba 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 $0.15 per million tokens for Mistral Nemo versus $0.306 for Qwen2.5 7B Instruct (2× as much) and $3.00 for Mistral Large 2.1 (20× as much).

Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.5 (#130 of 148), Mistral Nemo 118.7 (#140 of 148) and Qwen2.5 7B Instruct 118.5 (#141 of 148). Their confidence ranges do not overlap (123.8–130.8 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%.

There are no published SWE-bench Verified results for Mistral Large 2.1, Mistral Nemo and Qwen2.5 7B Instruct 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. All three support tool calling for agent workflows.

Mistral Large 2.1 and Qwen2.5 7B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemo. Maximum output per response: Mistral Large 2.1 up to 16,384, Mistral Nemo up to 128,000, Qwen2.5 7B Instruct up to 8,192 tokens.

Mistral Large 2.1 accepts text; Mistral Nemo accepts text; Qwen2.5 7B Instruct accepts text. They handle the same number of input types.

Yes, all three publish their weights, so you can self-host them.

Mistral Large 2.1 is the newest, released Nov 18, 2024. Qwen2.5 7B Instruct came out Sep 19, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Mistral Nemo Jul 2024, Qwen2.5 7B Instruct Apr 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.