Qwen-MT Turbo vs Voxtral Small 24B 2507 vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Voxtral Small 24B 2507 55, 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
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Llama-3.2-11B-Vision-Instruct 58/100, Voxtral Small 24B 2507 55/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Voxtral Small 24B 2507 on price and Llama-3.2-11B-Vision-Instruct 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 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct 128,000 · Voxtral Small 24B 2507 32,768 · Qwen-MT Turbo 16,384 tokens
- Widest inputsVoxtral Small 24B 2507 and Llama-3.2-11B-Vision-InstructQwen-MT Turbo: Text · Voxtral Small 24B 2507: Text, Audio · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingVoxtral Small 24B 2507 and Llama-3.2-11B-Vision-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen-MT Turbo | Voxtral Small 24B 2507 | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 79 | 89 | 76 |
| Inputs & features | 30% | 0 | 35 | 50 |
| Context window | 20% | 0 | 0 | 24 |
| Overall | 100% | 40/100 | 55/100 | 58/100 |
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) | Voxtral Small 24B 2507Mistral AI | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.16 | $0.10 (best) | $0.197 |
| Output | $0.49 | $0.30 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.242 | $0.15 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Mistral API | Median of 2 providers |
| Limits | |||
| Context window | 16,384 tokens | 32,768 tokens | 128,000 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Open |
| API model ID | qwen-mt-turbo | voxtral-small-latest | — |
| API providers | 1 | 7 (best) | 2 |
| Released | Jan 2025 | Jul 15, 2025 | Sep 25, 2024 |
| Knowledge cutoff | Apr 2024 | — | Dec 2023 |
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
- Voxtral Small 24B 2507$1.60
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Qwen-MT Turbo, Voxtral Small 24B 2507 or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within 3 points (Llama-3.2-11B-Vision-Instruct 58/100, Voxtral Small 24B 2507 55/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Voxtral Small 24B 2507 on price and Llama-3.2-11B-Vision-Instruct 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, Voxtral Small 24B 2507 or Llama-3.2-11B-Vision-Instruct?
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); Llama-3.2-11B-Vision-Instruct costs $0.197 input / $0.51 output per million tokens (median across 2 API providers). 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 $0.275 for Llama-3.2-11B-Vision-Instruct (1.8× 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, Voxtral Small 24B 2507 has not been scored yet and Llama-3.2-11B-Vision-Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen-MT Turbo, Voxtral Small 24B 2507 and Llama-3.2-11B-Vision-Instruct 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?
Llama-3.2-11B-Vision-Instruct has the largest context window at 128,000 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, Voxtral Small 24B 2507 up to 32,768, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Turbo accepts text; Voxtral Small 24B 2507 accepts text and audio; Llama-3.2-11B-Vision-Instruct accepts text and images. Voxtral Small 24B 2507 handles the widest range of inputs.
Are any of these open source?
Voxtral Small 24B 2507 and Llama-3.2-11B-Vision-Instruct publishes its weights (Apache 2.0) and can be self-hosted; Qwen-MT Turbo is proprietary.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 2025. Qwen-MT Turbo came out Jan 2025; Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Qwen-MT Turbo Apr 2024, Llama-3.2-11B-Vision-Instruct Dec 2023.
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.
