SOMA X v0.3.0 vs Ternary Bonsai 2 27B
At a Glance
| Compare |  SOMA X v0.3.0NVIDIA |  Ternary Bonsai 2 27BPrismML |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.075Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.50Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | Not reported | 262K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 18, 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 | SOMA-X v0.3.0 | Ternary Bonsai 2 27B |
|---|---|---|
| Developer | NVIDIA | PrismML |
| Family | Soma X | Bonsai 2 |
| Model | SOMA-X v0.3.0 | Ternary Bonsai 2 27B |
| Version | SOMA-X v0.3.0 | Ternary Bonsai 2 27B |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-09-17 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Model-specific input | Text, Image |
| Output modalities | 3D | Text |
| Context window | Unknown | 262K |
| Total parameters | Unknown | 27.4B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | No | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Openrouter (Standard) |
| Capabilities | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation | chat, generation, reasoning, tools, vision |
| Base model | Unknown | Qwen3.8 27B |
| Effective bit width | Unknown | 1.76 bits per weight |
| Language model size | Unknown | 5.93 GB |
| Weight format | Unknown | Ternary g128 with FP16 group scales |
SOMA X v0.3.0 Capabilities
Ternary Bonsai 2 27B Capabilities
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Primary Evidence
Sources and Freshness
Questions
SOMA X v0.3.0 vs Ternary Bonsai 2 27B FAQs
Is SOMA X v0.3.0 or Ternary Bonsai 2 27B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and Ternary Bonsai 2 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
Only Ternary Bonsai 2 27B has a directly sourced input price: $0.075 per million tokens. Only Ternary Bonsai 2 27B has a directly sourced output price: $0.50 per million tokens.
Which has a larger context window, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and Ternary Bonsai 2 27B is 262K.
Which performs better in benchmarks, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can SOMA X v0.3.0 or Ternary Bonsai 2 27B be self-hosted?+
Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; Ternary Bonsai 2 27B is open weight.
Can SOMA X v0.3.0 and Ternary Bonsai 2 27B understand images?+
SOMA X v0.3.0 is not documented with image input; Ternary Bonsai 2 27B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and Ternary Bonsai 2 27B is —.
Do SOMA X v0.3.0 and Ternary Bonsai 2 27B support reasoning and tool use?+
SOMA X v0.3.0: none of these features are definitively sourced. Ternary Bonsai 2 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
SOMA X v0.3.0 has 0 sourced provider routes; Ternary Bonsai 2 27B has 1, so Ternary Bonsai 2 27B has broader tracked availability.
Which offers better value, SOMA X v0.3.0 or Ternary Bonsai 2 27B?+
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.