Muse Wonder vs MiniMax-M3.1-Flash vs GPT-5.6 Sol
MiniMax-M3.1-Flash comes out ahead, 70 to 45 and 39 on our weighted score, and it is the cheaper option too.
MetaMuse Wonder
45/100- ECI161.7
- Price$4.00 / $20.00
- Context1.05M
- Our pick
MiniMaxMiniMax-M3.1-Flash
70/100- ECI—
- Price$0.15 / $0.60
- Context1M
OpenAIGPT-5.6 Sol
39/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
MiniMax-M3.1-Flash is our pick
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Muse Wonder (45) and GPT-5.6 Sol (39). It leads on price. Muse Wonder wins on inputs & features. 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 priceMiniMax-M3.1-FlashMiniMax-M3.1-Flash $0.263 · Muse Wonder $8.00 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
- Longest contextGPT-5.6 Sol and Muse WonderGPT-5.6 Sol 1,050,000 · Muse Wonder 1,048,576 · MiniMax-M3.1-Flash 1,000,000 tokens
- Widest inputsMuse WonderMuse Wonder: Text, Images, PDFs, Audio, Video · MiniMax-M3.1-Flash: Text, Images, Video · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Muse Wonder | MiniMax-M3.1-Flash | GPT-5.6 Sol |
|---|---|---|---|---|
| Price | 50% | 7 | 74 | 7 |
| Inputs & features | 30% | 100 | 70 | 80 |
| Context window | 20% | 61 | 60 | 61 |
| Overall | 100% | 45/100 | 70/100 | 39/100 |
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 WonderMeta | MiniMax-M3.1-FlashMiniMax | GPT-5.6 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 161.7 | — | 162.6 (best) |
| ECI rank | #13 of 165 | — | #12 of 165 (best) |
| GPQA DiamondGraduate-level science questions | — | — | 93.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 82.1% | — | 89.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% | — | 100% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 69.7% |
| Price per million tokens | |||
| Input | $4.00 | $0.15 (best) | $4.00 |
| Output | $20.00 | $0.60 (best) | $20.00 |
| Cached input | $0.20 | $0.015 (best) | $0.40 |
| Blended (3:1) | $8.00 | $0.263 (best) | $8.00 |
| Long-context rate | Same rate | Same rate | Over 272K: $8.00 / $30.00 |
| Price source | Official Meta API | Official MiniMax (minimax.io) API | Official OpenAI API |
| Limits | |||
| Context window | 1,048,576 tokens | 1,000,000 tokens | 1,050,000 tokens (best) |
| Max output | 131,072 tokens | 512,000 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yesminimal · low · medium · high · xhigh · max | Yeslow · medium · high · xhigh · max | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | muse-wonder-1.0 | MiniMax-M3.1-Flash | gpt-5.6-sol |
| API providers | 9 | 25 | 49 (best) |
| Released | Oct 12, 2026 | Oct 19, 2026 | Jul 9, 2026 |
| Knowledge cutoff | — | — | Feb 16, 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 Wonder$80.00
- MiniMax-M3.1-Flash$2.70
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Muse Wonder, MiniMax-M3.1-Flash or GPT-5.6 Sol?
MiniMax-M3.1-Flash is the better all-round choice, scoring 70/100 against Muse Wonder (45) and GPT-5.6 Sol (39). It leads on price. Muse Wonder wins on inputs & features. 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 Wonder, MiniMax-M3.1-Flash or GPT-5.6 Sol?
MiniMax-M3.1-Flash is cheaper at $0.15 input / $0.60 output per million tokens (official MiniMax (minimax.io) API price). Muse Wonder costs $4.00 input / $20.00 output per million tokens (official Meta API price); GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for MiniMax-M3.1-Flash versus $8.00 for Muse Wonder (30× as much) and $8.00 for GPT-5.6 Sol (30× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Muse Wonder has an ECI of 161.7, MiniMax-M3.1-Flash has not been scored yet and GPT-5.6 Sol has an ECI of 162.6.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Wonder, MiniMax-M3.1-Flash and GPT-5.6 Sol yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.6 Sol and Muse Wonder have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for MiniMax-M3.1-Flash. Maximum output per response: Muse Wonder up to 131,072, MiniMax-M3.1-Flash up to 512,000, GPT-5.6 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Muse Wonder accepts text, images, PDFs, audio and video; MiniMax-M3.1-Flash accepts text, images and video; GPT-5.6 Sol accepts text, images and PDFs. Muse Wonder handles the widest range of inputs.
Are any of these open source?
No. Muse Wonder, MiniMax-M3.1-Flash and GPT-5.6 Sol are proprietary and only available through APIs and apps.
Which is newer?
MiniMax-M3.1-Flash is the newest, released Oct 19, 2026. Muse Wonder came out Oct 12, 2026; GPT-5.6 Sol came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Sol Feb 16, 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.
