Mistral Nemo vs Qwen2.5 32B Instruct vs Qwen2.5 7B Instruct

Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 7B Instruct (44) and Qwen2.5 32B Instruct (43). It leads on price. Qwen2.5 32B Instruct 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); Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba 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 $1.23 for Qwen2.5 32B Instruct (8.2× as much).

Qwen2.5 32B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 (#131 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 (123.5–130.0 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen2.5 32B Instruct 46.1%, Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%.

There are no published SWE-bench Verified results for Mistral Nemo, Qwen2.5 32B Instruct and Qwen2.5 7B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 32B Instruct leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Qwen2.5 32B Instruct 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 Nemo up to 128,000, Qwen2.5 32B Instruct up to 8,192, Qwen2.5 7B Instruct up to 8,192 tokens.

Mistral Nemo accepts text; Qwen2.5 32B Instruct 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.

Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. Qwen2.5 32B Instruct came out Sep 17, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Mistral Nemo Jul 2024, Qwen2.5 32B Instruct Apr 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.