GPT-5.6 Sol vs Nemotron 3.5 Super 120B A12B vs Muse Wonder
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
- Our pick
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
MetaMuse Wonder
45/100- ECI161.7
- Price$4.00 / $20.00
- 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 · Nemotron 3.5 Super 120B A12B 1,048,576 · Muse Wonder 1,048,576 tokens
- Widest inputsMuse WonderGPT-5.6 Sol: Text, Images, PDFs · Nemotron 3.5 Super 120B A12B: Text · Muse Wonder: Text, Images, PDFs, Audio, Video
- Self-hostingNemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | GPT-5.6 Sol | Nemotron 3.5 Super 120B A12B | Muse Wonder |
|---|---|---|---|---|
| Price | 50% | 7 | 73 | 7 |
| Inputs & features | 30% | 80 | 35 | 100 |
| Context window | 20% | 61 | 61 | 61 |
| Overall | 100% | 39/100 | 59/100 | 45/100 |
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 | Nemotron 3.5 Super 120B A12BNVIDIA | Muse WonderMeta |
|---|---|---|---|
| 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 | $0.20 (best) | $4.00 |
| Output | $20.00 | $0.50 (best) | $20.00 |
| Cached input | $0.40 | — | $0.20 (best) |
| Blended (3:1) | $8.00 | $0.275 (best) | $8.00 |
| Long-context rate | Over 272K: $8.00 / $30.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 1 providers | 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 | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-5.6-sol | — | muse-wonder-1.0 |
| API providers | 49 (best) | 1 | 9 |
| Released | Jul 9, 2026 | Oct 15, 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
- Nemotron 3.5 Super 120B A12B$3.00
- Muse Wonder$80.00
Which should you choose?
Which is better: GPT-5.6 Sol, Nemotron 3.5 Super 120B A12B or Muse Wonder?
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, Nemotron 3.5 Super 120B A12B or Muse Wonder?
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, Nemotron 3.5 Super 120B A12B has not been scored yet and Muse Wonder has an ECI of 161.7.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Sol, Nemotron 3.5 Super 120B A12B and Muse Wonder 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, Nemotron 3.5 Super 120B A12B 1,048,576 and Muse Wonder 1,048,576 tokens. Maximum output per response: GPT-5.6 Sol up to 128,000, Nemotron 3.5 Super 120B A12B 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; Nemotron 3.5 Super 120B A12B accepts text; Muse Wonder accepts text, images, PDFs, audio and video. 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.
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