Muse Wonder Full vs Ling 3.1 Flash
Muse Wonder Full comes out ahead, 72 to 36 on our weighted score.
- Our pick
Meta
Muse Wonder Full
72/100- ECI—
- Price—
- Context1.05M
inclusionAI
Ling 3.1 Flash
36/100- ECI—
- Price$0.30 / $0.90
- Context262K
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Muse Wonder Full is our pick
Muse Wonder Full is the better all-round choice, scoring 72/100 against Ling 3.1 Flash (36). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 priceLing 3.1 FlashLing 3.1 Flash $0.45 per 1M tokens (3:1 blend) · Muse Wonder Full unpriced
- Longest contextMuse Wonder FullMuse Wonder Full 1,048,576 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMuse Wonder FullMuse Wonder Full: Text, Images, Audio, Video · Ling 3.1 Flash: Text
- Self-hostingLing 3.1 FlashPublishes downloadable weights
| Measure | Weight | Muse Wonder Full | Ling 3.1 Flash |
|---|---|---|---|
| Inputs & features | 60% | 80 | 35 |
| Context window | 40% | 61 | 37 |
| Overall | 100% | 72/100 | 36/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ||
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $0.30 |
| Output | — | $0.90 |
| Cached input | — | — |
| Blended (3:1) | — | $0.45 |
| Long-context rate | — | Same rate |
| Price source | — | Median of 5 providers |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | — | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | Yes | No |
| Video | Yes | No |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | — | 7 |
| Released | Oct 12, 2026 | Sep 29, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
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Muse Wonder Full—
Ling 3.1 Flash$4.80
Which should you choose?
Which is better: Muse Wonder Full or Ling 3.1 Flash?
Muse Wonder Full is the better all-round choice, scoring 72/100 against Ling 3.1 Flash (36). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Muse Wonder Full or Ling 3.1 Flash?
Ling 3.1 Flash is cheaper at $0.30 input / $0.90 output per million tokens (median across 5 API providers). . At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Ling 3.1 Flash versus . Muse Wonder Full has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Muse Wonder Full has not been scored yet and Ling 3.1 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Wonder Full and Ling 3.1 Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Muse Wonder Full has the largest context window at 1,048,576 tokens, against 262,144 for Ling 3.1 Flash. Maximum output per response: Ling 3.1 Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Muse Wonder Full accepts text, images, audio and video; Ling 3.1 Flash accepts text. Muse Wonder Full handles the widest range of inputs.
Are any of these open source?
Ling 3.1 Flash publishes its weights and can be self-hosted; Muse Wonder Full is proprietary.
Which is newer?
Muse Wonder Full is the newest, released Oct 12, 2026. Ling 3.1 Flash came out Sep 29, 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 Nov 2, 2026.