Mistral Large vs Gemma 2 27B

Mistral Large is the better all-round choice, scoring 44/100 against Gemma 2 27B (35). It leads on inputs & features and context window. Gemma 2 27B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Gemma 2 27B is cheaper at $0.65 input / $0.65 output per million tokens (median across 3 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 $0.75 for Mistral Large (1.2× as much).

Gemma 2 27B scores higher on the Capabilities Index (ECI): Gemma 2 27B 122.1 (#152 of 165) and Mistral Large 122.0 (#153 of 165). The confidence ranges of the top two overlap (115.8–124.1 vs 115.7–124.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 38.8%, Gemma 2 27B 36.5%; OTIS Mock AIME 2024–2025 — Mistral Large 1.9%, Gemma 2 27B 1.4%.

There are no published SWE-bench Verified results for Mistral Large and Gemma 2 27B yet, so there is no like-for-like coding score. On overall capability, Gemma 2 27B leads, which tends to carry over to coding, but test on your own codebase. Note that Gemma 2 27B does not support tool calling, which most coding agents need.

Mistral Large has the largest context window at 128,000 tokens, against 8,192 for Gemma 2 27B. Maximum output per response: Mistral Large up to 102,400, Gemma 2 27B up to 2,048 tokens.

Mistral Large accepts text and PDFs; Gemma 2 27B accepts text. Mistral Large handles the widest range of inputs.

Gemma 2 27B publishes its weights and can be self-hosted; Mistral Large is proprietary.

Gemma 2 27B is the newest, released 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.