Mixtral 8x22B Instruct vs Solar Pro 2 vs Gemma 2 27B
Solar Pro 2 comes out ahead, 51 to 28 and 26 on our weighted score, and it is the cheaper option too.
Mistral AIMixtral 8x22B Instruct
26/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
- Our pick
UpstageSolar Pro 2
51/100- ECI—
- Price$0.25 / $0.25
- Context66K
GoogleGemma 2 27B
28/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
Solar Pro 2 is our pick
Solar Pro 2 is the better all-round choice, scoring 51/100 against Gemma 2 27B (28) and Mixtral 8x22B Instruct (26). It leads 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 priceSolar Pro 2Solar Pro 2 $0.25 · Gemma 2 27B $0.65 · Mixtral 8x22B Instruct $3.00 per 1M tokens (3:1 blend)
- Longest contextMixtral 8x22B Instruct and Solar Pro 2Mixtral 8x22B Instruct 65,536 · Solar Pro 2 65,536 · Gemma 2 27B 8,192 tokens
- Widest inputsMixtral 8x22B InstructMixtral 8x22B Instruct: Text, PDFs · Solar Pro 2: Text · Gemma 2 27B: Text
- Self-hostingMixtral 8x22B Instruct and Gemma 2 27BPublishes downloadable weights
| Measure | Weight | Mixtral 8x22B Instruct | Solar Pro 2 | Gemma 2 27B |
|---|---|---|---|---|
| Price | 50% | 26 | 75 | 56 |
| Inputs & features | 30% | 35 | 35 | 0 |
| Context window | 20% | 12 | 12 | 0 |
| Overall | 100% | 26/100 | 51/100 | 28/100 |
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 | Mixtral 8x22B InstructMistral AI | Solar Pro 2Upstage | Gemma 2 27BGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.0 | — | 122.1 (best) |
| ECI rank | #155 of 165 | — | #152 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 34.1% | — | 36.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 1.4% |
| Price per million tokens | |||
| Input | $2.00 | $0.25 (best) | $0.65 |
| Output | $6.00 | $0.25 (best) | $0.65 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.25 (best) | $0.65 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Official Upstage API | Median of 3 providers |
| Limits | |||
| Context window | 65,536 tokens (best) | 65,536 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 | Yesminimal · high | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | solar-pro2 | — |
| API providers | 5 (best) | 2 | 3 |
| Released | Apr 17, 2024 | May 20, 2025 | Jul 13, 2024 |
| Knowledge cutoff | Jan 31, 2024 | Mar 2025 | 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
- Solar Pro 2$3.00
- Gemma 2 27B$7.80
Which should you choose?
Which is better: Mixtral 8x22B Instruct, Solar Pro 2 or Gemma 2 27B?
Solar Pro 2 is the better all-round choice, scoring 51/100 against Gemma 2 27B (28) and Mixtral 8x22B Instruct (26). It leads 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, Mixtral 8x22B Instruct, Solar Pro 2 or Gemma 2 27B?
Solar Pro 2 is cheaper at $0.25 input / $0.25 output per million tokens (official Upstage API price). 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 $0.25 per million tokens for Solar Pro 2 versus $0.65 for Gemma 2 27B (2.6× as much) and $3.00 for Mixtral 8x22B Instruct (12× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mixtral 8x22B Instruct has an ECI of 122.0, Solar Pro 2 has not been scored yet and Gemma 2 27B has an ECI of 122.1.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B Instruct, Solar Pro 2 and Gemma 2 27B 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?
Mixtral 8x22B Instruct and Solar Pro 2 have the largest context windows (65,536 and 65,536 tokens), against 8,192 for Gemma 2 27B. Maximum output per response: Mixtral 8x22B Instruct up to 52,428, Solar Pro 2 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; Solar Pro 2 accepts text; Gemma 2 27B accepts text. Mixtral 8x22B Instruct handles the widest range of inputs.
Are any of these open source?
Mixtral 8x22B Instruct and Gemma 2 27B publishes its weights and can be self-hosted; Solar Pro 2 is proprietary.
Which is newer?
Solar Pro 2 is the newest, released May 20, 2025. Gemma 2 27B came out Jul 13, 2024; Mixtral 8x22B Instruct came out Apr 17, 2024. Knowledge cutoff: Mixtral 8x22B Instruct Jan 31, 2024, Solar Pro 2 Mar 2025, 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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