NVIDIA Nemotron 3 Super 120B A12B Base BF16 vs SOMA X v0.3.0
At a Glance
| Compare |  SOMA X v0.3.0NVIDIA | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 | SOMA-X v0.3.0 |
|---|---|---|
| Developer | NVIDIA | NVIDIA |
| Family | Nvidia Nemotron 3 Super 120b A12b Base Bf16 | Soma X |
| Model | NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 | SOMA-X v0.3.0 |
| Version | NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 | SOMA-X v0.3.0 |
| Lifecycle | active | active |
| Released | 2026-03-04 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Model-specific input |
| Output modalities | Text | 3D |
| Context window | 1,049K | Unknown |
| Total parameters | 123.6B | Unknown |
| Active parameters | 12B | Unknown |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | generation | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation |
NVIDIA Nemotron 3 Super 120B A12B Base BF16 Capabilities
SOMA X v0.3.0 Capabilities
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Primary Evidence
Sources and Freshness
Questions
NVIDIA Nemotron 3 Super 120B A12B Base BF16 vs SOMA X v0.3.0 FAQs
Is NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both NVIDIA Nemotron 3 Super 120B A12B Base BF16 and SOMA X v0.3.0, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.
Which has a larger context window, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
Neither model has a larger sourced context window in this comparison. NVIDIA Nemotron 3 Super 120B A12B Base BF16 is 1,049K and SOMA X v0.3.0 is —.
Which performs better in benchmarks, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0 be self-hosted?+
Both models have the same recorded self-hosting status: supported. NVIDIA Nemotron 3 Super 120B A12B Base BF16 is open weight; SOMA X v0.3.0 is open weight.
Can NVIDIA Nemotron 3 Super 120B A12B Base BF16 and SOMA X v0.3.0 understand images?+
NVIDIA Nemotron 3 Super 120B A12B Base BF16 is not documented with image input; SOMA X v0.3.0 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
Neither has a larger sourced maximum output. NVIDIA Nemotron 3 Super 120B A12B Base BF16 is — and SOMA X v0.3.0 is —.
Do NVIDIA Nemotron 3 Super 120B A12B Base BF16 and SOMA X v0.3.0 support reasoning and tool use?+
NVIDIA Nemotron 3 Super 120B A12B Base BF16: none of these features are definitively sourced. SOMA X v0.3.0: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
NVIDIA Nemotron 3 Super 120B A12B Base BF16 has 0 sourced provider routes; SOMA X v0.3.0 has 0, a tie.
Which offers better value, NVIDIA Nemotron 3 Super 120B A12B Base BF16 or SOMA X v0.3.0?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.