Gemma 2 27B vs Mistral Large vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct comes out ahead, 50 to 44 and 35 on our weighted score, and it is the cheaper option too.
GoogleGemma 2 27B
35/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
Mistral AIMistral Large
44/100- ECI122.0
- Price$0.50 / $1.50
- Context128K
- Our pick
Alibaba (Qwen)Qwen2.5 7B Instruct
50/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Qwen2.5 7B Instruct is our pick
Qwen2.5 7B Instruct is the better all-round choice, scoring 50/100 against Mistral Large (44) and Gemma 2 27B (35). It leads on price. Mistral Large wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemma 2 27BCapabilities Index (ECI): Gemma 2 27B 122.1 · Mistral Large 122.0 · Qwen2.5 7B Instruct 118.5
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Gemma 2 27B $0.65 · Mistral Large $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B 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 7B Instruct: Text
- Self-hostingGemma 2 27B and Qwen2.5 7B InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Gemma 2 27B | Mistral Large | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 42 | 42 | 38 |
| Price | 25% | 56 | 53 | 100 |
| Inputs & features | 15% | 0 | 45 | 25 |
| Context window | 10% | 0 | 24 | 25 |
| Overall | 100% | 35/100 | 44/100 | 50/100 |
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 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.1 (best) | 122.0 | 118.5 |
| ECI rank | #152 of 165 (best) | #153 of 165 | #159 of 165 |
| GPQA DiamondGraduate-level science questions | 36.5% | 38.8% (best) | 35.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.4% | 1.9% | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.65 | $0.50 | $0.05 (best) |
| Output | $0.65 | $1.50 | $0.10 (best) |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.65 | $0.75 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Mistral API | Median of 3 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 | OpenApache 2.0 |
| API model ID | — | mistral-large-latest | — |
| API providers | 3 | 11 (best) | 4 |
| Released | Jul 13, 2024 | Feb 26, 2024 | Sep 19, 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 7B Instruct$0.70
Which should you choose?
Which is better: Gemma 2 27B, Mistral Large or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is the better all-round choice, scoring 50/100 against Mistral Large (44) and Gemma 2 27B (35). It leads on price. Mistral Large wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 2 27B, Mistral Large or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Gemma 2 27B costs $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 $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.65 for Gemma 2 27B (10× as much) and $0.75 for Mistral Large (12× as much).
Which scores higher on benchmarks?
Gemma 2 27B scores higher on the Capabilities Index (ECI): Gemma 2 27B 122.1 (#152 of 165), Mistral Large 122.0 (#153 of 165) and Qwen2.5 7B Instruct 118.5 (#159 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%, Qwen2.5 7B Instruct 35.5%; OTIS Mock AIME 2024–2025 — Qwen2.5 7B Instruct 2.5%, Mistral Large 1.9%, Gemma 2 27B 1.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 2 27B, Mistral Large and Qwen2.5 7B Instruct 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.
Which has the bigger context window?
Qwen2.5 7B 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 7B 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 7B Instruct accepts text. Mistral Large handles the widest range of inputs.
Are any of these open source?
Gemma 2 27B and Qwen2.5 7B Instruct publishes its weights (Apache 2.0) and can be self-hosted; Mistral Large is proprietary.
Which is newer?
Qwen2.5 7B Instruct is the newest, released Sep 19, 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.
