Qwen2.5 72B Instruct vs Mistral Nemo vs Mistral Large 2.1

Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 72B Instruct (40) 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 72B Instruct costs $1.40 input / $5.60 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 $2.45 for Qwen2.5 72B Instruct (16× as much) and $3.00 for Mistral Large 2.1 (20× as much).

Qwen2.5 72B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 72B Instruct 129.0 (#128 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 (123.8–130.7 vs 123.8–130.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 72B Instruct 49.2%, Mistral Nemo 29.9%.

There are no published SWE-bench Verified results for Qwen2.5 72B Instruct, Mistral Nemo and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 72B 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 72B 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 72B Instruct up to 8,192, Mistral Nemo up to 128,000, Mistral Large 2.1 up to 16,384 tokens.

Qwen2.5 72B 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 72B Instruct came out Sep 19, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Qwen2.5 72B 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.