Mixtral 8x22B Instruct vs Mistral Large 2.1 vs Gemma 2 27B
Too close to call on our weighted score (Mistral Large 2.1 38, Gemma 2 27B 35, Mixtral 8x22B Instruct 34). The right pick depends on what you value most.
Mistral AIMixtral 8x22B Instruct
34/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
- Mistral AI
Mistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
GoogleGemma 2 27B
35/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
Too close to call
It is close. Our weighted score puts them within 3 points (Mistral Large 2.1 38/100, Gemma 2 27B 35/100, Mixtral 8x22B Instruct 34/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability and Gemma 2 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Large 2.1Capabilities Index (ECI): Mistral Large 2.1 128.6 · Gemma 2 27B 122.1 · Mixtral 8x22B Instruct 122.0
- Lowest priceGemma 2 27BGemma 2 27B $0.65 · Mixtral 8x22B Instruct $3.00 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1Mistral Large 2.1 131,072 · Mixtral 8x22B Instruct 65,536 · Gemma 2 27B 8,192 tokens
- Widest inputsMixtral 8x22B InstructMixtral 8x22B Instruct: Text, PDFs · Mistral Large 2.1: Text · Gemma 2 27B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mixtral 8x22B Instruct | Mistral Large 2.1 | Gemma 2 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 42 | 50 | 42 |
| Price | 25% | 26 | 26 | 56 |
| Inputs & features | 15% | 35 | 25 | 0 |
| Context window | 10% | 12 | 25 | 0 |
| Overall | 100% | 34/100 | 38/100 | 35/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mixtral 8x22B InstructMistral AI | Mistral Large 2.1Mistral AI | Gemma 2 27BGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.0 | 128.6 (best) | 122.1 |
| ECI rank | #155 of 165 | #142 of 165 (best) | #152 of 165 |
| GPQA DiamondGraduate-level science questions | 34.1% | 51.3% (best) | 36.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% (best) | 1.4% |
| Price per million tokens | |||
| Input | $2.00 | $2.00 | $0.65 (best) |
| Output | $6.00 | $6.00 | $0.65 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $3.00 | $0.65 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 2 providers | Median of 3 providers |
| Limits | |||
| Context window | 65,536 tokens | 131,072 tokens (best) | 8,192 tokens |
| Max output | 52,428 tokens (best) | 16,384 tokens | 2,048 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | — | — |
| API providers | 5 (best) | 2 | 3 |
| Released | Apr 17, 2024 | Nov 18, 2024 | Jul 13, 2024 |
| Knowledge cutoff | Jan 31, 2024 | Nov 2024 | Jun 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.
- Mixtral 8x22B Instruct$32.00
- Mistral Large 2.1$32.00
- Gemma 2 27B$7.80
Which should you choose?
Which is better: Mixtral 8x22B Instruct, Mistral Large 2.1 or Gemma 2 27B?
It is close. Our weighted score puts them within 3 points (Mistral Large 2.1 38/100, Gemma 2 27B 35/100, Mixtral 8x22B Instruct 34/100), so choose by what matters most for your work: Mistral Large 2.1 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, Mixtral 8x22B Instruct, Mistral Large 2.1 or Gemma 2 27B?
Gemma 2 27B is cheaper at $0.65 input / $0.65 output per million tokens (median across 3 API providers). Mixtral 8x22B Instruct costs $2.00 input / $6.00 output per million tokens (median across 5 API providers); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.65 per million tokens for Gemma 2 27B versus $3.00 for Mixtral 8x22B Instruct (4.6× as much) and $3.00 for Mistral Large 2.1 (4.6× as much).
Which scores higher on benchmarks?
Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.6 (#142 of 165), Gemma 2 27B 122.1 (#152 of 165) and Mixtral 8x22B Instruct 122.0 (#155 of 165). Their confidence ranges do not overlap (124.3–130.7 vs 115.8–124.1), so the gap is a real one. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Gemma 2 27B 36.5%, Mixtral 8x22B Instruct 34.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B Instruct, Mistral Large 2.1 and Gemma 2 27B yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 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?
Mistral Large 2.1 has the largest context window at 131,072 tokens, against 65,536 for Mixtral 8x22B Instruct and 8,192 for Gemma 2 27B. Maximum output per response: Mixtral 8x22B Instruct up to 52,428, Mistral Large 2.1 up to 16,384, Gemma 2 27B up to 2,048 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x22B Instruct accepts text and PDFs; Mistral Large 2.1 accepts text; Gemma 2 27B accepts text. Mixtral 8x22B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Gemma 2 27B came out Jul 13, 2024; Mixtral 8x22B Instruct came out Apr 17, 2024. Knowledge cutoff: Mixtral 8x22B Instruct Jan 31, 2024, Mistral Large 2.1 Nov 2024, Gemma 2 27B Jun 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.
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