GPT-5.6 Sol vs Muse Wonder vs Nemotron 3.5 Super 120B A12B
Nemotron 3.5 Super 120B A12B comes out ahead, 59 to 45 and 39 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.6 Sol
39/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
MetaMuse Wonder
45/100- ECI161.7
- Price$4.00 / $20.00
- Context1.05M
- Our pick
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Nemotron 3.5 Super 120B A12B is our pick
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 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 priceNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · 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 Wonder 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 tokens
- Widest inputsMuse WonderGPT-5.6 Sol: Text, Images, PDFs · Muse Wonder: Text, Images, PDFs, Audio, Video · Nemotron 3.5 Super 120B A12B: Text
- Self-hostingNemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | GPT-5.6 Sol | Muse Wonder | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 7 | 7 | 73 |
| Inputs & features | 30% | 80 | 100 | 35 |
| Context window | 20% | 61 | 61 | 61 |
| Overall | 100% | 39/100 | 45/100 | 59/100 |
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 | GPT-5.6 SolOpenAI | Muse WonderMeta | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.6 (best) | 161.7 | — |
| ECI rank | #12 of 165 (best) | #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% | — | — |
| Price per million tokens | |||
| Input | $4.00 | $4.00 | $0.20 (best) |
| Output | $20.00 | $20.00 | $0.50 (best) |
| Cached input | $0.40 | $0.20 (best) | — |
| Blended (3:1) | $8.00 | $8.00 | $0.275 (best) |
| Long-context rate | Over 272K: $8.00 / $30.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Meta API | Median of 1 providers |
| 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 | No |
| PDFs | Yes | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yesminimal · low · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.6-sol | muse-wonder-1.0 | — |
| API providers | 49 (best) | 9 | 1 |
| Released | Jul 9, 2026 | Oct 12, 2026 | Oct 15, 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 Wonder$80.00
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: GPT-5.6 Sol, Muse Wonder or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is the better all-round choice, scoring 59/100 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, GPT-5.6 Sol, Muse Wonder or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). 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 $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $8.00 for GPT-5.6 Sol (29× as much) and $8.00 for Muse Wonder (29× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.6 Sol has an ECI of 162.6, Muse Wonder has an ECI of 161.7 and Nemotron 3.5 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Sol, Muse Wonder and Nemotron 3.5 Super 120B A12B 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?
Their context windows are effectively the same size: GPT-5.6 Sol 1,050,000, Muse Wonder 1,048,576 and Nemotron 3.5 Super 120B A12B 1,048,576 tokens. Maximum output per response: GPT-5.6 Sol up to 128,000, Muse Wonder up to 131,072, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.6 Sol accepts text, images and PDFs; Muse Wonder accepts text, images, PDFs, audio and video; Nemotron 3.5 Super 120B A12B accepts text. Muse Wonder handles the widest range of inputs.
Are any of these open source?
Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; GPT-5.6 Sol and Muse Wonder is proprietary.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 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.
