Mixtral 8x22B Instruct vs Qwen2.5 7B Instruct vs Gemma 2 27B
Qwen2.5 7B Instruct comes out ahead, 50 to 35 and 34 on our weighted score, and it is the cheaper option too.
Mistral AIMixtral 8x22B Instruct
34/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
- Our pick
Alibaba (Qwen)Qwen2.5 7B Instruct
50/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
GoogleGemma 2 27B
35/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
Qwen2.5 7B Instruct is our pick
Qwen2.5 7B Instruct is the better all-round choice, scoring 50/100 against Gemma 2 27B (35) and Mixtral 8x22B Instruct (34). It leads on price and context window. Mixtral 8x22B Instruct 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 · Mixtral 8x22B Instruct 122.0 · Qwen2.5 7B Instruct 118.5
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Gemma 2 27B $0.65 · Mixtral 8x22B Instruct $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Mixtral 8x22B Instruct 65,536 · Gemma 2 27B 8,192 tokens
- Widest inputsMixtral 8x22B InstructMixtral 8x22B Instruct: Text, PDFs · Qwen2.5 7B Instruct: 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 | Qwen2.5 7B Instruct | Gemma 2 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 42 | 38 | 42 |
| Price | 25% | 26 | 100 | 56 |
| Inputs & features | 15% | 35 | 25 | 0 |
| Context window | 10% | 12 | 25 | 0 |
| Overall | 100% | 34/100 | 50/100 | 35/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mixtral 8x22B InstructMistral AI | Qwen2.5 7B InstructAlibaba (Qwen) | Gemma 2 27BGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.0 | 118.5 | 122.1 (best) |
| ECI rank | #155 of 165 | #159 of 165 | #152 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 34.1% | 35.5% | 36.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 2.5% (best) | 1.4% |
| Price per million tokens | |||
| Input | $2.00 | $0.05 (best) | $0.65 |
| Output | $6.00 | $0.10 (best) | $0.65 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.063 (best) | $0.65 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 3 providers | Median of 3 providers |
| Limits | |||
| Context window | 65,536 tokens | 131,072 tokens (best) | 8,192 tokens |
| Max output | 52,428 tokens (best) | 8,192 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 | OpenApache 2.0 | Open |
| API model ID | — | — | — |
| API providers | 5 (best) | 4 | 3 |
| Released | Apr 17, 2024 | Sep 19, 2024 | Jul 13, 2024 |
| Knowledge cutoff | Jan 31, 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
- Qwen2.5 7B Instruct$0.70
- Gemma 2 27B$7.80
Which should you choose?
Which is better: Mixtral 8x22B Instruct, Qwen2.5 7B Instruct or Gemma 2 27B?
Qwen2.5 7B Instruct is the better all-round choice, scoring 50/100 against Gemma 2 27B (35) and Mixtral 8x22B Instruct (34). It leads on price and context window. Mixtral 8x22B Instruct wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mixtral 8x22B Instruct, Qwen2.5 7B Instruct or Gemma 2 27B?
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); Mixtral 8x22B Instruct costs $2.00 input / $6.00 output per million tokens (median across 5 API providers). 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 $3.00 for Mixtral 8x22B Instruct (48× 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), Mixtral 8x22B Instruct 122.0 (#155 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 115.7–124.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemma 2 27B 36.5%, Qwen2.5 7B Instruct 35.5%, Mixtral 8x22B Instruct 34.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B Instruct, Qwen2.5 7B Instruct 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.
Which has the bigger context window?
Qwen2.5 7B Instruct 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, Qwen2.5 7B Instruct up to 8,192, Gemma 2 27B up to 2,048 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x22B Instruct accepts text and PDFs; Qwen2.5 7B Instruct 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 (Apache 2.0), so you can self-host them.
Which is newer?
Qwen2.5 7B Instruct is the newest, released Sep 19, 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, 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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