GPT-5.6 Sol vs Muse Spark 1.2 vs Muse Wonder
Too close to call on our weighted score (Muse Spark 1.2 71, Muse Wonder 68, GPT-5.6 Sol 66). The right pick depends on what you value most.
OpenAIGPT-5.6 Sol
66/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
MetaMuse Spark 1.2
71/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
- Meta
Muse Wonder
68/100- ECI161.7
- Price$4.00 / $20.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 3 points (Muse Spark 1.2 71/100, Muse Wonder 68/100, GPT-5.6 Sol 66/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.6 SolCapabilities Index (ECI): GPT-5.6 Sol 162.6 · Muse Wonder 161.7 · Muse Spark 1.2 155.2
- Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GPT-5.6 Sol $8.00 · Muse Wonder $8.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-5.6 Sol 1,050,000 · Muse Spark 1.2 1,048,576 · Muse Wonder 1,048,576 tokens
- Widest inputsMuse Spark 1.2 and Muse WonderGPT-5.6 Sol: Text, Images, PDFs · Muse Spark 1.2: Text, Images, PDFs, Audio, Video · Muse Wonder: Text, Images, PDFs, Audio, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-5.6 Sol | Muse Spark 1.2 | Muse Wonder |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 92 | 83 | 91 |
| Price | 25% | 7 | 34 | 7 |
| Inputs & features | 15% | 80 | 100 | 100 |
| Context window | 10% | 61 | 61 | 61 |
| Overall | 100% | 66/100 | 71/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.6 SolOpenAI | Muse Spark 1.2Meta | Muse WonderMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.6 (best) | 155.2 | 161.7 |
| ECI rank | #12 of 165 (best) | #38 of 165 | #13 of 165 |
| GPQA DiamondGraduate-level science questions | 93.5% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 89.1% (best) | — | 82.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% (best) | — | 99.4% |
| SimpleQA VerifiedShort factual questions | 69.7% (best) | 60.3% | — |
| Price per million tokens | |||
| Input | $4.00 | $1.25 (best) | $4.00 |
| Output | $20.00 | $4.25 (best) | $20.00 |
| Cached input | $0.40 | $0.15 (best) | $0.20 |
| Blended (3:1) | $8.00 | $2.00 (best) | $8.00 |
| Long-context rate | Over 272K: $8.00 / $30.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Meta API | Official Meta API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 1,048,576 tokens | 1,048,576 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| Audio | No | Yes | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · medium · high · xhigh · max | Yesminimal · low · medium · high · xhigh | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-5.6-sol | muse-spark-1.2 | muse-wonder-1.0 |
| API providers | 49 (best) | 18 | 9 |
| Released | Jul 9, 2026 | Aug 5, 2026 | Oct 12, 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.
- GPT-5.6 Sol$80.00
- Muse Spark 1.2$21.00
- Muse Wonder$80.00
Which should you choose?
Which is better: GPT-5.6 Sol, Muse Spark 1.2 or Muse Wonder?
It is close. Our weighted score puts them within 3 points (Muse Spark 1.2 71/100, Muse Wonder 68/100, GPT-5.6 Sol 66/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.6 Sol, Muse Spark 1.2 or Muse Wonder?
Muse Spark 1.2 is cheaper at $1.25 input / $4.25 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); Muse Wonder costs $4.00 input / $20.00 output per million tokens (official Meta API price). At a typical mix of three input tokens to one output token, that is $2.00 per million tokens for Muse Spark 1.2 versus $8.00 for GPT-5.6 Sol (4× as much) and $8.00 for Muse Wonder (4× as much).
Which scores higher on benchmarks?
GPT-5.6 Sol scores higher on the Capabilities Index (ECI): GPT-5.6 Sol 162.6 (#12 of 165), Muse Wonder 161.7 (#13 of 165) and Muse Spark 1.2 155.2 (#38 of 165). The confidence ranges of the top two overlap (159.6–166.2 vs 159.2–164.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Sol, Muse Spark 1.2 and Muse Wonder yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 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: GPT-5.6 Sol 1,050,000, Muse Spark 1.2 1,048,576 and Muse Wonder 1,048,576 tokens. Maximum output per response: GPT-5.6 Sol up to 128,000, Muse Spark 1.2 up to 131,072, Muse Wonder up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.6 Sol accepts text, images and PDFs; Muse Spark 1.2 accepts text, images, PDFs, audio and video; Muse Wonder accepts text, images, PDFs, audio and video. Muse Spark 1.2 handles the widest range of inputs.
Are any of these open source?
No. GPT-5.6 Sol, Muse Spark 1.2 and Muse Wonder are proprietary and only available through APIs and apps.
Which is newer?
Muse Wonder is the newest, released Oct 12, 2026. Muse Spark 1.2 came out Aug 5, 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.
