Qwen-MT Turbo vs Muse Glimmer 30B vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Muse Glimmer 30B 57, 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
MetaMuse Glimmer 30B
57/100- ECI—
- Price$0.30 / $1.20
- Context131K
- Meta
Llama-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 (Llama-3.2-11B-Vision-Instruct 58/100, Muse Glimmer 30B 57/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Qwen-MT Turbo on price and Muse Glimmer 30B 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 priceQwen-MT TurboQwen-MT Turbo $0.242 · Llama-3.2-11B-Vision-Instruct $0.275 · Muse Glimmer 30B $0.525 per 1M tokens (3:1 blend)
- Longest contextMuse Glimmer 30BMuse Glimmer 30B 131,072 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen-MT Turbo 16,384 tokens
- Widest inputsMuse Glimmer 30B and Llama-3.2-11B-Vision-InstructQwen-MT Turbo: Text · Muse Glimmer 30B: Text, Images · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingMuse Glimmer 30B and Llama-3.2-11B-Vision-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen-MT Turbo | Muse Glimmer 30B | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 79 | 63 | 76 |
| Inputs & features | 30% | 0 | 70 | 50 |
| Context window | 20% | 0 | 24 | 24 |
| Overall | 100% | 40/100 | 57/100 | 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) | Muse Glimmer 30BMeta | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.16 (best) | $0.30 | $0.197 |
| Output | $0.49 (best) | $1.20 | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.242 (best) | $0.525 | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 10 providers | Median of 2 providers |
| Limits | |||
| Context window | 16,384 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 131,072 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 | Yes | 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 | 12 (best) | 2 |
| Released | Jan 2025 | Aug 10, 2026 | Sep 25, 2024 |
| Knowledge cutoff | Apr 2024 | Jan 4, 2026 | 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
- Muse Glimmer 30B$5.40
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Qwen-MT Turbo, Muse Glimmer 30B or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within a point (Llama-3.2-11B-Vision-Instruct 58/100, Muse Glimmer 30B 57/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Qwen-MT Turbo on price and Muse Glimmer 30B 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, Muse Glimmer 30B 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); Muse Glimmer 30B costs $0.30 input / $1.20 output per million tokens (median across 10 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 $0.525 for Muse Glimmer 30B (2.2× 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, Muse Glimmer 30B 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, Muse Glimmer 30B 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?
Muse Glimmer 30B has the largest context window at 131,072 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, Muse Glimmer 30B up to 131,072, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen-MT Turbo accepts text; Muse Glimmer 30B accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. Muse Glimmer 30B handles the widest range of inputs.
Are any of these open source?
Muse Glimmer 30B 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?
Muse Glimmer 30B is the newest, released Aug 10, 2026. 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, Muse Glimmer 30B Jan 4, 2026, 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.
