Qwen-MT Turbo vs Ministral 3 14B vs Llama-3.2-11B-Vision-Instruct
Ministral 3 14B comes out ahead, 63 to 58 and 40 on our weighted score, though Qwen-MT Turbo is 14% cheaper per token.
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
- Our pick
Mistral AIMinistral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Ministral 3 14B is our pick
Ministral 3 14B is the better all-round choice, scoring 63/100 against Llama-3.2-11B-Vision-Instruct (58) and Qwen-MT Turbo (40). It leads on inputs & features and context window. Qwen-MT Turbo wins 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 priceQwen-MT TurboQwen-MT Turbo $0.242 · Llama-3.2-11B-Vision-Instruct $0.275 · Ministral 3 14B $0.282 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 14BMinistral 3 14B 262,144 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen-MT Turbo 16,384 tokens
- Widest inputsMinistral 3 14B and Llama-3.2-11B-Vision-InstructQwen-MT Turbo: Text · Ministral 3 14B: Text, Images · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingMinistral 3 14B and Llama-3.2-11B-Vision-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen-MT Turbo | Ministral 3 14B | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 79 | 76 | 76 |
| Inputs & features | 30% | 0 | 60 | 50 |
| Context window | 20% | 0 | 37 | 24 |
| Overall | 100% | 40/100 | 63/100 | 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) | Ministral 3 14BMistral AI | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.16 (best) | $0.268 | $0.197 |
| Output | $0.49 | $0.325 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.242 (best) | $0.282 | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Median of 2 providers |
| Limits | |||
| Context window | 16,384 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 262,144 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Open |
| API model ID | qwen-mt-turbo | — | — |
| API providers | 1 | 2 (best) | 2 (best) |
| Released | Jan 2025 | Dec 2, 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
- Ministral 3 14B$3.33
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Qwen-MT Turbo, Ministral 3 14B or Llama-3.2-11B-Vision-Instruct?
Ministral 3 14B is the better all-round choice, scoring 63/100 against Llama-3.2-11B-Vision-Instruct (58) and Qwen-MT Turbo (40). It leads on inputs & features and context window. Qwen-MT Turbo wins 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, Qwen-MT Turbo, Ministral 3 14B or Llama-3.2-11B-Vision-Instruct?
Qwen-MT Turbo is cheaper at $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); Ministral 3 14B costs $0.268 input / $0.325 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.242 per million tokens for Qwen-MT Turbo versus $0.275 for Llama-3.2-11B-Vision-Instruct (1.1× as much) and $0.282 for Ministral 3 14B (1.2× 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, Ministral 3 14B 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, Ministral 3 14B 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?
Ministral 3 14B has the largest context window at 262,144 tokens, against 128,000 for Llama-3.2-11B-Vision-Instruct and 16,384 for Qwen-MT Turbo. Maximum output per response: Qwen-MT Turbo up to 8,192, Ministral 3 14B up to 262,144, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Turbo accepts text; Ministral 3 14B accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. Ministral 3 14B handles the widest range of inputs.
Are any of these open source?
Ministral 3 14B 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?
Ministral 3 14B is the newest, released Dec 2, 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.
