Gemma 2 27B vs GPT-4o vs Mistral Large
Too close to call on our weighted score (Mistral Large 44, GPT-4o 43, Gemma 2 27B 35). The right pick depends on what you value most.
Google
Gemma 2 27B
35/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
OpenAI
GPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
Mistral AI
Mistral Large
44/100- ECI122.0
- Price$0.50 / $1.50
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Large 44/100, GPT-4o 43/100, Gemma 2 27B 35/100), so choose by what matters most for your work: GPT-4o for raw capability and Gemma 2 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-4oCapabilities Index (ECI): GPT-4o 129.0 · Gemma 2 27B 122.1 · Mistral Large 122.0
- Lowest priceGemma 2 27BGemma 2 27B $0.65 · Mistral Large $0.75 · GPT-4o $4.38 per 1M tokens (3:1 blend)
- Longest contextGPT-4o and Mistral LargeGPT-4o 128,000 · Mistral Large 128,000 · Gemma 2 27B 8,192 tokens
- Widest inputsGPT-4oGemma 2 27B: Text · GPT-4o: Text, Images, PDFs · Mistral Large: Text, PDFs
- Self-hostingGemma 2 27BPublishes downloadable weights
| Measure | Weight | Gemma 2 27B | GPT-4o | Mistral Large |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 42 | 51 | 42 |
| Price | 25% | 56 | 19 | 53 |
| Inputs & features | 15% | 0 | 70 | 45 |
| Context window | 10% | 0 | 24 | 24 |
| Overall | 100% | 35/100 | 43/100 | 44/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.1 | 129.0 (best) | 122.0 |
| ECI rank | #152 of 165 | #141 of 165 (best) | #153 of 165 |
| GPQA DiamondGraduate-level science questions | 36.5% | 48.9% (best) | 38.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.4% | 6.3% (best) | 1.9% |
| Price per million tokens | |||
| Input | $0.65 | $2.50 | $0.50 (best) |
| Output | $0.65 (best) | $10.00 | $1.50 |
| Cached input | — | $1.25 | $0.05 (best) |
| Blended (3:1) | $0.65 (best) | $4.38 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official OpenAI API | Official Mistral API |
| Limits | |||
| Context window | 8,192 tokens | 128,000 tokens (best) | 128,000 tokens (best) |
| Max output | 2,048 tokens | 16,384 tokens | 102,400 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | — | gpt-4o | mistral-large-latest |
| API providers | 3 | 26 (best) | 11 |
| Released | Jul 13, 2024 | May 13, 2024 | Feb 26, 2024 |
| Knowledge cutoff | Jun 30, 2024 | Sep 2023 | 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
GPT-4o$45.00
Mistral Large$8.00
Which should you choose?
Which is better: Gemma 2 27B, GPT-4o or Mistral Large?
It is close. Our weighted score puts them within a point (Mistral Large 44/100, GPT-4o 43/100, Gemma 2 27B 35/100), so choose by what matters most for your work: GPT-4o for raw capability and Gemma 2 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 2 27B, GPT-4o or Mistral Large?
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); GPT-4o costs $2.50 input / $10.00 output per million tokens (official OpenAI 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) and $4.38 for GPT-4o (6.7× as much).
Which scores higher on benchmarks?
GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165), Gemma 2 27B 122.1 (#152 of 165) and Mistral Large 122.0 (#153 of 165). Their confidence ranges do not overlap (124.7–131.3 vs 115.8–124.1), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-4o 48.9%, Mistral Large 38.8%, Gemma 2 27B 36.5%; OTIS Mock AIME 2024–2025 — GPT-4o 6.3%, 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, GPT-4o and Mistral Large yet, so there is no like-for-like coding score. On overall capability, GPT-4o 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?
GPT-4o and Mistral Large have the largest context windows (128,000 and 128,000 tokens), against 8,192 for Gemma 2 27B. Maximum output per response: Gemma 2 27B up to 2,048, GPT-4o up to 16,384, Mistral Large up to 102,400 tokens.
Which can read images, PDFs, audio or video?
Gemma 2 27B accepts text; GPT-4o accepts text, images and PDFs; Mistral Large accepts text and PDFs. GPT-4o handles the widest range of inputs.
Are any of these open source?
Gemma 2 27B publishes its weights and can be self-hosted; GPT-4o and Mistral Large is proprietary.
Which is newer?
Gemma 2 27B is the newest, released Jul 13, 2024. GPT-4o came out May 13, 2024; Mistral Large came out Feb 26, 2024. Knowledge cutoff: Gemma 2 27B Jun 30, 2024, GPT-4o Sep 2023, 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.