Muse Glimmer 30B vs Qwen-MT Turbo vs Solar Pro 4
Too close to call on our weighted score (Muse Glimmer 30B 57, Solar Pro 4 55, Qwen-MT Turbo 40). The right pick depends on what you value most.
MetaMuse Glimmer 30B
57/100- ECI—
- Price$0.30 / $1.20
- Context131K
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
Upstage
Solar Pro 4
55/100- ECI—
- Price$0.30 / $1.20
- Context524K
Too close to call
It is close. Our weighted score puts them within 3 points (Muse Glimmer 30B 57/100, Solar Pro 4 55/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Qwen-MT Turbo on price and Solar Pro 4 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 · Muse Glimmer 30B $0.525 · Solar Pro 4 $0.525 per 1M tokens (3:1 blend)
- Longest contextSolar Pro 4Solar Pro 4 524,288 · Muse Glimmer 30B 131,072 · Qwen-MT Turbo 16,384 tokens
- Widest inputsMuse Glimmer 30BMuse Glimmer 30B: Text, Images · Qwen-MT Turbo: Text · Solar Pro 4: Text
- Self-hostingMuse Glimmer 30BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Muse Glimmer 30B | Qwen-MT Turbo | Solar Pro 4 |
|---|---|---|---|---|
| Price | 50% | 63 | 79 | 63 |
| Inputs & features | 30% | 70 | 0 | 45 |
| Context window | 20% | 24 | 0 | 49 |
| Overall | 100% | 57/100 | 40/100 | 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 | Muse Glimmer 30BMeta | Qwen-MT TurboAlibaba (Qwen) | Solar Pro 4Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.16 (best) | $0.30 |
| Output | $1.20 | $0.49 (best) | $1.20 |
| Cached input | — | — | $0.06 |
| Blended (3:1) | $0.525 | $0.242 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Alibaba API | Official Upstage API |
| Limits | |||
| Context window | 131,072 tokens | 16,384 tokens | 524,288 tokens (best) |
| Max output | 131,072 tokens (best) | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | Proprietary | Proprietary |
| API model ID | — | qwen-mt-turbo | solar-pro4 |
| API providers | 12 (best) | 1 | 3 |
| Released | Aug 10, 2026 | Jan 2025 | Aug 6, 2026 |
| Knowledge cutoff | Jan 4, 2026 | Apr 2024 | Feb 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Muse Glimmer 30B$5.40
- Qwen-MT Turbo$2.58
- Solar Pro 4$5.40
Which should you choose?
Which is better: Muse Glimmer 30B, Qwen-MT Turbo or Solar Pro 4?
It is close. Our weighted score puts them within 3 points (Muse Glimmer 30B 57/100, Solar Pro 4 55/100, Qwen-MT Turbo 40/100), so choose by what matters most for your work: Qwen-MT Turbo on price and Solar Pro 4 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, Muse Glimmer 30B, Qwen-MT Turbo or Solar Pro 4?
Qwen-MT Turbo is cheaper at $0.16 input / $0.49 output per million tokens (official Alibaba API price). Muse Glimmer 30B costs $0.30 input / $1.20 output per million tokens (median across 10 API providers); Solar Pro 4 costs $0.30 input / $1.20 output per million tokens (official Upstage API price). At a typical mix of three input tokens to one output token, that is $0.242 per million tokens for Qwen-MT Turbo versus $0.525 for Muse Glimmer 30B (2.2× as much) and $0.525 for Solar Pro 4 (2.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Muse Glimmer 30B has not been scored yet, Qwen-MT Turbo has not been scored yet and Solar Pro 4 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Glimmer 30B, Qwen-MT Turbo and Solar Pro 4 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 4 has the largest context window at 524,288 tokens, against 131,072 for Muse Glimmer 30B and 16,384 for Qwen-MT Turbo. Maximum output per response: Muse Glimmer 30B up to 131,072, Qwen-MT Turbo up to 8,192, Solar Pro 4 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Muse Glimmer 30B accepts text and images; Qwen-MT Turbo accepts text; Solar Pro 4 accepts text. Muse Glimmer 30B handles the widest range of inputs.
Are any of these open source?
Muse Glimmer 30B publishes its weights (Apache 2.0) and can be self-hosted; Qwen-MT Turbo and Solar Pro 4 is proprietary.
Which is newer?
Muse Glimmer 30B is the newest, released Aug 10, 2026. Solar Pro 4 came out Aug 6, 2026; Qwen-MT Turbo came out Jan 2025. Knowledge cutoff: Muse Glimmer 30B Jan 4, 2026, Qwen-MT Turbo Apr 2024, Solar Pro 4 Feb 2026.
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.
