Muse Wonder vs GPT-6.1 Sol vs GPT-5.6 Sol
GPT-6.1 Sol comes out ahead, 72 to 68 and 66 on our weighted score, and it is the cheaper option too.
MetaMuse Wonder
68/100- ECI161.7
- Price$4.00 / $20.00
- Context1.05M
- Our pick
OpenAIGPT-6.1 Sol
72/100- ECI167.8
- Price$2.00 / $10.00
- Context1.05M
- OpenAI
GPT-5.6 Sol
66/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
GPT-6.1 Sol is our pick
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Muse Wonder (68) and GPT-5.6 Sol (66). It leads on capability and price. Muse Wonder wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-6.1 SolCapabilities Index (ECI): GPT-6.1 Sol 167.8 · GPT-5.6 Sol 162.6 · Muse Wonder 161.7
- Lowest priceGPT-6.1 SolGPT-6.1 Sol $4.00 · Muse Wonder $8.00 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-6.1 Sol 1,050,000 · GPT-5.6 Sol 1,050,000 · Muse Wonder 1,048,576 tokens
- Widest inputsMuse WonderMuse Wonder: Text, Images, PDFs, Audio, Video · GPT-6.1 Sol: Text, Images, PDFs · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Muse Wonder | GPT-6.1 Sol | GPT-5.6 Sol |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 91 | 99 | 92 |
| Price | 25% | 7 | 20 | 7 |
| Inputs & features | 15% | 100 | 80 | 80 |
| Context window | 10% | 61 | 61 | 61 |
| Overall | 100% | 68/100 | 72/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Muse WonderMeta | GPT-6.1 SolOpenAI | GPT-5.6 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 161.7 | 167.8 (best) | 162.6 |
| ECI rank | #13 of 165 | #4 of 165 (best) | #12 of 165 |
| GPQA DiamondGraduate-level science questions | — | 95.4% (best) | 93.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 82.1% | 93.7% (best) | 89.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% | 100% (best) | 100% (best) |
| SimpleQA VerifiedShort factual questions | — | 73.9% (best) | 69.7% |
| Price per million tokens | |||
| Input | $4.00 | $2.00 (best) | $4.00 |
| Output | $20.00 | $10.00 (best) | $20.00 |
| Cached input | $0.20 | $0.10 (best) | $0.40 |
| Blended (3:1) | $8.00 | $4.00 (best) | $8.00 |
| Long-context rate | Same rate | Over 272K: $4.00 / $15.00 | Over 272K: $8.00 / $30.00 |
| Price source | Official Meta API | Official OpenAI API | Official OpenAI API |
| Limits | |||
| Context window | 1,048,576 tokens | 1,050,000 tokens (best) | 1,050,000 tokens (best) |
| Max output | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| Audio | Yes | No | No |
| Video | Yes | No | 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 | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | muse-wonder-1.0 | gpt-6.1-sol | gpt-5.6-sol |
| API providers | 9 | 33 | 49 (best) |
| Released | Oct 12, 2026 | Sep 29, 2026 | Jul 9, 2026 |
| Knowledge cutoff | — | Apr 30, 2026 | 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
- GPT-6.1 Sol$40.00
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Muse Wonder, GPT-6.1 Sol or GPT-5.6 Sol?
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Muse Wonder (68) and GPT-5.6 Sol (66). It leads on capability and price. Muse Wonder wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Muse Wonder, GPT-6.1 Sol or GPT-5.6 Sol?
GPT-6.1 Sol is cheaper at $2.00 input / $10.00 output per million tokens (official OpenAI 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 $4.00 per million tokens for GPT-6.1 Sol versus $8.00 for Muse Wonder (2× as much) and $8.00 for GPT-5.6 Sol (2× as much).
Which scores higher on benchmarks?
GPT-6.1 Sol scores higher on the Capabilities Index (ECI): GPT-6.1 Sol 167.8 (#4 of 165), GPT-5.6 Sol 162.6 (#12 of 165) and Muse Wonder 161.7 (#13 of 165). The confidence ranges of the top two overlap (164.3–172.5 vs 159.6–166.2), so treat the gap as small. On individual benchmarks: FrontierMath Tiers 1–3 — GPT-6.1 Sol 93.7%, GPT-5.6 Sol 89.1%, Muse Wonder 82.1%; OTIS Mock AIME 2024–2025 — GPT-6.1 Sol 100%, GPT-5.6 Sol 100%, Muse Wonder 99.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Wonder, GPT-6.1 Sol and GPT-5.6 Sol yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 Sol leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Their context windows are effectively the same size: Muse Wonder 1,048,576, GPT-6.1 Sol 1,050,000 and GPT-5.6 Sol 1,050,000 tokens. Maximum output per response: Muse Wonder up to 131,072, GPT-6.1 Sol up to 128,000, 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; GPT-6.1 Sol accepts text, images and PDFs; 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, GPT-6.1 Sol and GPT-5.6 Sol are proprietary and only available through APIs and apps.
Which is newer?
Muse Wonder is the newest, released Oct 12, 2026. GPT-6.1 Sol came out Sep 29, 2026; GPT-5.6 Sol came out Jul 9, 2026. Knowledge cutoff: GPT-6.1 Sol Apr 30, 2026, 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.
