Qwen-MT Turbo vs Solar Pro 2 vs Voxtral Small 24B 2507
Too close to call on our weighted score (Voxtral Small 24B 2507 55, Solar Pro 2 52, Qwen-MT Turbo 40). The right pick depends on what you value most.
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
Upstage
Solar Pro 2
52/100- ECI—
- Price$0.25 / $0.25
- Context66K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Too close to call
It is close. Our weighted score puts them within 3 points (Voxtral Small 24B 2507 55/100, Solar Pro 2 52/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Voxtral Small 24B 2507 on price and Solar Pro 2 for long inputs. 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 priceVoxtral Small 24B 2507Voxtral Small 24B 2507 $0.15 · Qwen-MT Turbo $0.242 · Solar Pro 2 $0.25 per 1M tokens (3:1 blend)
- Longest contextSolar Pro 2Solar Pro 2 65,536 · Voxtral Small 24B 2507 32,768 · Qwen-MT Turbo 16,384 tokens
- Widest inputsVoxtral Small 24B 2507Qwen-MT Turbo: Text · Solar Pro 2: Text · Voxtral Small 24B 2507: Text, Audio
- Self-hostingVoxtral Small 24B 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen-MT Turbo | Solar Pro 2 | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 79 | 78 | 89 |
| Inputs & features | 30% | 0 | 35 | 35 |
| Context window | 20% | 0 | 12 | 0 |
| Overall | 100% | 40/100 | 52/100 | 55/100 |
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 | Qwen-MT TurboAlibaba (Qwen) | Solar Pro 2Upstage | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.16 | $0.25 | $0.10 (best) |
| Output | $0.49 | $0.25 (best) | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.242 | $0.25 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Upstage API | Official Mistral API |
| Limits | |||
| Context window | 16,384 tokens | 65,536 tokens (best) | 32,768 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | No | Yesminimal · high | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenApache 2.0 |
| API model ID | qwen-mt-turbo | solar-pro2 | voxtral-small-latest |
| API providers | 1 | 2 | 7 (best) |
| Released | Jan 2025 | May 20, 2025 | Jul 15, 2025 |
| Knowledge cutoff | Apr 2024 | Mar 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen-MT Turbo$2.58
- Solar Pro 2$3.00
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Qwen-MT Turbo, Solar Pro 2 or Voxtral Small 24B 2507?
It is close. Our weighted score puts them within 3 points (Voxtral Small 24B 2507 55/100, Solar Pro 2 52/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Voxtral Small 24B 2507 on price and Solar Pro 2 for long inputs. 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, Qwen-MT Turbo, Solar Pro 2 or Voxtral Small 24B 2507?
Voxtral Small 24B 2507 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). Qwen-MT Turbo costs $0.16 input / $0.49 output per million tokens (official Alibaba API price); Solar Pro 2 costs $0.25 input / $0.25 output per million tokens (official Upstage API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Voxtral Small 24B 2507 versus $0.242 for Qwen-MT Turbo (1.6× as much) and $0.25 for Solar Pro 2 (1.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen-MT Turbo has not been scored yet, Solar Pro 2 has not been scored yet and Voxtral Small 24B 2507 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen-MT Turbo, Solar Pro 2 and Voxtral Small 24B 2507 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-MT Turbo does not support tool calling, which most coding agents need.
Which has the bigger context window?
Solar Pro 2 has the largest context window at 65,536 tokens, against 32,768 for Voxtral Small 24B 2507 and 16,384 for Qwen-MT Turbo. Maximum output per response: Qwen-MT Turbo up to 8,192, Solar Pro 2 up to 8,192, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Turbo accepts text; Solar Pro 2 accepts text; Voxtral Small 24B 2507 accepts text and audio. Voxtral Small 24B 2507 handles the widest range of inputs.
Are any of these open source?
Voxtral Small 24B 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen-MT Turbo and Solar Pro 2 is proprietary.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 2025. Solar Pro 2 came out May 20, 2025; Qwen-MT Turbo came out Jan 2025. Knowledge cutoff: Qwen-MT Turbo Apr 2024, Solar Pro 2 Mar 2025.
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 Oct 4, 2026.
