Mistral Large vs Qwen2.5-Coder-32B-Instruct vs Gemma 2 27B

Mistral Large is the better all-round choice, scoring 45/100 against Qwen2.5-Coder-32B-Instruct (39) and Gemma 2 27B (28). It leads on inputs & features. Gemma 2 27B wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Gemma 2 27B is cheaper at $0.65 input / $0.65 output per million tokens (median across 3 API providers). Qwen2.5-Coder-32B-Instruct costs $0.66 input / $1.00 output per million tokens (median across 7 API providers); Mistral Large costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.65 per million tokens for Gemma 2 27B versus $0.745 for Qwen2.5-Coder-32B-Instruct (1.1× as much) and $0.75 for Mistral Large (1.2× as much).

There is no independent benchmark that covers all three models yet. Mistral Large has an ECI of 122.0, Qwen2.5-Coder-32B-Instruct has not been scored yet and Gemma 2 27B has an ECI of 122.1.

There are no published SWE-bench Verified results for Mistral Large, Qwen2.5-Coder-32B-Instruct and Gemma 2 27B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Gemma 2 27B does not support tool calling, which most coding agents need.

Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Large and 8,192 for Gemma 2 27B. Maximum output per response: Mistral Large up to 102,400, Qwen2.5-Coder-32B-Instruct up to 8,192, Gemma 2 27B up to 2,048 tokens.

Mistral Large accepts text and PDFs; Qwen2.5-Coder-32B-Instruct accepts text; Gemma 2 27B accepts text. Mistral Large handles the widest range of inputs.

Qwen2.5-Coder-32B-Instruct and Gemma 2 27B publishes its weights and can be self-hosted; Mistral Large is proprietary.

Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Gemma 2 27B came out Jul 13, 2024; Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 30, 2024, Gemma 2 27B Jun 30, 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 Nov 2, 2026.