Qwen-MT Turbo vs Phi-4-mini vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Phi-4-mini 58, Llama-3.2-11B-Vision-Instruct 58, 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
MicrosoftPhi-4-mini
58/100- ECI—
- Price$0.075 / $0.30
- Context128K
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 a point (Phi-4-mini 58/100, Llama-3.2-11B-Vision-Instruct 58/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Phi-4-mini 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 pricePhi-4-miniPhi-4-mini $0.131 · Qwen-MT Turbo $0.242 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextPhi-4-mini and Llama-3.2-11B-Vision-InstructPhi-4-mini 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen-MT Turbo 16,384 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructQwen-MT Turbo: Text · Phi-4-mini: Text · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingPhi-4-mini and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Qwen-MT Turbo | Phi-4-mini | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 79 | 92 | 76 |
| Inputs & features | 30% | 0 | 25 | 50 |
| Context window | 20% | 0 | 24 | 24 |
| Overall | 100% | 40/100 | 58/100 | 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) | Phi-4-miniMicrosoft | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.16 | $0.075 (best) | $0.197 |
| Output | $0.49 | $0.30 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.242 | $0.131 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Azure API | Median of 2 providers |
| Limits | |||
| Context window | 16,384 tokens | 128,000 tokens (best) | 128,000 tokens (best) |
| Max output | 8,192 tokens (best) | 4,096 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | 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 | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | qwen-mt-turbo | phi-4-mini | — |
| API providers | 1 | 1 | 2 (best) |
| Released | Jan 2025 | Dec 11, 2024 | Sep 25, 2024 |
| Knowledge cutoff | Apr 2024 | Oct 2023 | 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
- Phi-4-mini$1.35
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Qwen-MT Turbo, Phi-4-mini or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within a point (Phi-4-mini 58/100, Llama-3.2-11B-Vision-Instruct 58/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Phi-4-mini 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, Phi-4-mini or Llama-3.2-11B-Vision-Instruct?
Phi-4-mini is cheaper at $0.075 input / $0.30 output per million tokens (official Azure 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 $0.131 per million tokens for Phi-4-mini versus $0.242 for Qwen-MT Turbo (1.8× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (2.1× 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, Phi-4-mini 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, Phi-4-mini 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?
Phi-4-mini and Llama-3.2-11B-Vision-Instruct have the largest context windows (128,000 and 128,000 tokens), against 16,384 for Qwen-MT Turbo. Maximum output per response: Qwen-MT Turbo up to 8,192, Phi-4-mini up to 4,096, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Turbo accepts text; Phi-4-mini accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Phi-4-mini and Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Qwen-MT Turbo is proprietary.
Which is newer?
Qwen-MT Turbo is the newest, released Jan 2025. Phi-4-mini came out Dec 11, 2024; Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Qwen-MT Turbo Apr 2024, Phi-4-mini Oct 2023, 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.
