GPT-5.6 Sol vs Muse Wonder Full vs Nemotron 3.5 Super 120B A12B
Too close to call on our weighted score (GPT-5.6 Sol 72, Muse Wonder Full 72, Nemotron 3.5 Super 120B A12B 45). The right pick depends on what you value most.
OpenAIGPT-5.6 Sol
72/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
MetaMuse Wonder Full
72/100- ECI—
- Price—
- Context1.05M
NVIDIANemotron 3.5 Super 120B A12B
45/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (GPT-5.6 Sol 72/100, Muse Wonder Full 72/100, Nemotron 3.5 Super 120B A12B 45/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Muse Wonder Full unpriced
- Longest contextAbout the sameGPT-5.6 Sol 1,050,000 · Muse Wonder Full 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 tokens
- Widest inputsMuse Wonder FullGPT-5.6 Sol: Text, Images, PDFs · Muse Wonder Full: Text, Images, 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 Full | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 80 | 35 |
| Context window | 40% | 61 | 61 | 61 |
| Overall | 100% | 72/100 | 72/100 | 45/100 |
Left out because at least one model lacks the data: capability and price. 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 Wonder FullMeta | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 162.6 | — | — |
| ECI rank | #12 of 165 | — | — |
| GPQA DiamondGraduate-level science questions | 93.5% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 89.1% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | — |
| SimpleQA VerifiedShort factual questions | 69.7% | — | — |
| Price per million tokens | |||
| Input | $4.00 | — | $0.20 (best) |
| Output | $20.00 | — | $0.50 (best) |
| Cached input | $0.40 | — | — |
| Blended (3:1) | $8.00 | — | $0.275 (best) |
| Long-context rate | Over 272K: $8.00 / $30.00 | — | Same rate |
| Price source | Official OpenAI 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) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.6-sol | — | — |
| API providers | 49 (best) | — | 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 Full—
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: GPT-5.6 Sol, Muse Wonder Full or Nemotron 3.5 Super 120B A12B?
It is close. Our weighted score puts them within a point (GPT-5.6 Sol 72/100, Muse Wonder Full 72/100, Nemotron 3.5 Super 120B A12B 45/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. The score weighs inputs & features 60%, context window 40%. 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 Full 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). 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). Muse Wonder Full has no published per-token price.
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 Full has not been scored yet 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 Full 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 Full 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, 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 Full accepts text, images, audio and video; Nemotron 3.5 Super 120B A12B accepts text. Muse Wonder Full 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 Full is proprietary.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 2026. Muse Wonder Full 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.
