Qwen2.5 7B Instruct vs Mixtral 8x7B vs Mistral Nemo

Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 7B Instruct (44) and Mixtral 8x7B (37). 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 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba API price); Mixtral 8x7B costs $0.70 input / $0.70 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 $0.70 for Mixtral 8x7B (4.7× as much).

Mistral Nemo scores higher on the Capabilities Index (ECI): Mistral Nemo 118.7 (#140 of 148), Qwen2.5 7B Instruct 118.5 (#141 of 148) and Mixtral 8x7B 118.5 (#142 of 148). The confidence ranges of the top two overlap (111.3–121.5 vs 110.7–121.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Mixtral 8x7B 30.6%, Mistral Nemo 29.9%.

There are no published SWE-bench Verified results for Qwen2.5 7B Instruct, Mixtral 8x7B and Mistral Nemo yet, so there is no like-for-like coding score. On overall capability, Mistral Nemo leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Nemo and 32,000 for Mixtral 8x7B. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Mixtral 8x7B up to 32,000, Mistral Nemo up to 128,000 tokens.

Qwen2.5 7B Instruct accepts text; Mixtral 8x7B accepts text; Mistral Nemo 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. Mistral Nemo came out Jul 1, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Qwen2.5 7B Instruct Apr 2024, Mixtral 8x7B Jan 2024, Mistral Nemo Jul 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.