Beam 1.0 501B A23B vs Muse Wonder Full vs Nemotron 3.5 Super 120B A12B
Muse Wonder Full comes out ahead, 72 to 45 and 45 on our weighted score.
Reflection AIBeam 1.0 501B A23B
45/100- ECI—
- Price$0.30 / $1.00
- Context1.05M
- Our pick
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
Muse Wonder Full is our pick
Muse Wonder Full is the better all-round choice, scoring 72/100 against Beam 1.0 501B A23B (45) and Nemotron 3.5 Super 120B A12B (45). It leads on inputs & features. 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 · Beam 1.0 501B A23B $0.475 per 1M tokens (3:1 blend) · Muse Wonder Full unpriced
- Longest contextAbout the sameBeam 1.0 501B A23B 1,048,576 · Muse Wonder Full 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 tokens
- Widest inputsMuse Wonder FullBeam 1.0 501B A23B: Text · Muse Wonder Full: Text, Images, Audio, Video · Nemotron 3.5 Super 120B A12B: Text
- Self-hostingBeam 1.0 501B A23B and Nemotron 3.5 Super 120B A12BPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Beam 1.0 501B A23B | Muse Wonder Full | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 80 | 35 |
| Context window | 40% | 61 | 61 | 61 |
| Overall | 100% | 45/100 | 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 | Beam 1.0 501B A23BReflection AI | Muse Wonder FullMeta | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | — | $0.20 (best) |
| Output | $1.00 | — | $0.50 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.475 | — | $0.275 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 7 providers | — | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens | 1,048,576 tokens | 1,048,576 tokens |
| Max output | 131,072 tokens | — | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 12 (best) | — | 1 |
| Released | Oct 5, 2026 | Oct 12, 2026 | Oct 15, 2026 |
| Knowledge cutoff | Jun 30, 2026 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Beam 1.0 501B A23B$5.00
- Muse Wonder Full—
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: Beam 1.0 501B A23B, Muse Wonder Full or Nemotron 3.5 Super 120B A12B?
Muse Wonder Full is the better all-round choice, scoring 72/100 against Beam 1.0 501B A23B (45) and Nemotron 3.5 Super 120B A12B (45). It leads on inputs & features. 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, Beam 1.0 501B A23B, 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). Beam 1.0 501B A23B costs $0.30 input / $1.00 output per million tokens (median across 7 API providers). 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 $0.475 for Beam 1.0 501B A23B (1.7× 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. Beam 1.0 501B A23B has not been scored yet, 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 Beam 1.0 501B A23B, 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?
Beam 1.0 501B A23B, Muse Wonder Full and Nemotron 3.5 Super 120B A12B share the same 1,048,576-token context window. Maximum output per response: Beam 1.0 501B A23B up to 131,072, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Beam 1.0 501B A23B accepts text; 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?
Beam 1.0 501B A23B and Nemotron 3.5 Super 120B A12B publishes its weights (Apache-2.0) and can be self-hosted; 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; Beam 1.0 501B A23B came out Oct 5, 2026. Knowledge cutoff: Beam 1.0 501B A23B Jun 30, 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.
