Gemma 2 27B vs Mistral Large vs Qwen2.5-Coder-32B-Instruct
Mistral Large comes out ahead, 45 to 39 and 28 on our weighted score, though Gemma 2 27B is 13% cheaper per token.
GoogleGemma 2 27B
28/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
- Our pick
Mistral AIMistral Large
45/100- ECI122.0
- Price$0.50 / $1.50
- Context128K
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
Mistral Large is our pick
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.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGemma 2 27BGemma 2 27B $0.65 · Qwen2.5-Coder-32B-Instruct $0.745 · Mistral Large $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Mistral Large 128,000 · Gemma 2 27B 8,192 tokens
- Widest inputsMistral LargeGemma 2 27B: Text · Mistral Large: Text, PDFs · Qwen2.5-Coder-32B-Instruct: Text
- Self-hostingGemma 2 27B and Qwen2.5-Coder-32B-InstructPublishes downloadable weights
| Measure | Weight | Gemma 2 27B | Mistral Large | Qwen2.5-Coder-32B-Instruct |
|---|---|---|---|---|
| Price | 50% | 56 | 53 | 54 |
| Inputs & features | 30% | 0 | 45 | 25 |
| Context window | 20% | 0 | 24 | 25 |
| Overall | 100% | 28/100 | 45/100 | 39/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemma 2 27BGoogle | Mistral LargeMistral AI | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.1 (best) | 122.0 | — |
| ECI rank | #152 of 165 (best) | #153 of 165 | — |
| GPQA DiamondGraduate-level science questions | 36.5% | 38.8% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.4% | 1.9% (best) | — |
| Price per million tokens | |||
| Input | $0.65 | $0.50 (best) | $0.66 |
| Output | $0.65 (best) | $1.50 | $1.00 |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.65 (best) | $0.75 | $0.745 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Mistral API | Median of 7 providers |
| Limits | |||
| Context window | 8,192 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 2,048 tokens | 102,400 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | mistral-large-latest | — |
| API providers | 3 | 11 (best) | 7 |
| Released | Jul 13, 2024 | Feb 26, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Jun 30, 2024 | Nov 30, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemma 2 27B$7.80
- Mistral Large$8.00
- Qwen2.5-Coder-32B-Instruct$8.60
Which should you choose?
Which is better: Gemma 2 27B, Mistral Large or Qwen2.5-Coder-32B-Instruct?
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.
Which is cheaper, Gemma 2 27B, Mistral Large or Qwen2.5-Coder-32B-Instruct?
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).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma 2 27B has an ECI of 122.1, Mistral Large has an ECI of 122.0 and Qwen2.5-Coder-32B-Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 2 27B, Mistral Large and Qwen2.5-Coder-32B-Instruct 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.
Which has the bigger context window?
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: Gemma 2 27B up to 2,048, Mistral Large up to 102,400, Qwen2.5-Coder-32B-Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemma 2 27B accepts text; Mistral Large accepts text and PDFs; Qwen2.5-Coder-32B-Instruct accepts text. Mistral Large handles the widest range of inputs.
Are any of these open source?
Gemma 2 27B and Qwen2.5-Coder-32B-Instruct publishes its weights and can be self-hosted; Mistral Large is proprietary.
Which is newer?
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: Gemma 2 27B Jun 30, 2024, Mistral Large Nov 30, 2024.
How do you decide the winner?
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.
