Qwen2.5 32B Instruct vs Mistral Nemo vs Mistral Large 2.1

Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mistral Large 2.1 (38). It leads on price. 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 32B Instruct costs $0.70 input / $2.80 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 $1.23 for Qwen2.5 32B Instruct (8.2× as much) and $3.00 for Mistral Large 2.1 (20× as much).

Qwen2.5 32B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 (#131 of 148), Mistral Large 2.1 128.5 (#130 of 148) and Mistral Nemo 118.7 (#140 of 148). The confidence ranges of the top two overlap (123.5–130.0 vs 123.8–130.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 32B Instruct 46.1%, Mistral Nemo 29.9%.

There are no published SWE-bench Verified results for Qwen2.5 32B Instruct, Mistral Nemo and Mistral Large 2.1 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 Mistral Large 2.1 have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemo. Maximum output per response: Qwen2.5 32B Instruct up to 8,192, Mistral Nemo up to 128,000, Mistral Large 2.1 up to 16,384 tokens.

Qwen2.5 32B Instruct accepts text; Mistral Nemo accepts text; Mistral Large 2.1 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 32B Instruct came out Sep 17, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Qwen2.5 32B Instruct Apr 2024, Mistral Nemo Jul 2024, Mistral Large 2.1 Nov 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.