SOMA X v0.3.0 vs Inkling
At a Glance
| Compare |  SOMA X v0.3.0NVIDIA | InklingThinking Machines Lab |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #34 of 4645.4 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #18 of 44$0.090 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #28 of 3848.1 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.95Deepinfra ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.05Deepinfra ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,049K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 3, 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 | Inkling |
|---|---|---|
| Developer | NVIDIA | Thinking Machines Lab |
| Family | Soma X | Inkling |
| Model | SOMA-X v0.3.0 | Inkling |
| Version | SOMA-X v0.3.0 | Inkling |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-07-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Model-specific input | Text, Image, Video, Audio |
| Output modalities | 3D | Text |
| Context window | Unknown | 1,049K |
| Total parameters | Unknown | 975B |
| Active parameters | Unknown | 41B |
| License | apache-2.0 | apache-2.0 |
| Open weights | Yes | Yes |
| API available | No | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation | agents, chat, coding, generation, reasoning, tools, vision |
SOMA X v0.3.0 Capabilities
Inkling Capabilities
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Primary Evidence
Sources and Freshness
Questions
SOMA X v0.3.0 vs Inkling FAQs
Is SOMA X v0.3.0 or Inkling better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and Inkling, 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 Inkling?+
Only Inkling has a directly sourced input price: $0.95 per million tokens. Only Inkling has a directly sourced output price: $4.05 per million tokens.
Which has a larger context window, SOMA X v0.3.0 or Inkling?+
Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and Inkling is 1,049K.
Which performs better in benchmarks, SOMA X v0.3.0 or Inkling?+
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 Inkling be self-hosted?+
Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; Inkling is open weight.
Can SOMA X v0.3.0 and Inkling understand images?+
SOMA X v0.3.0 is not documented with image input; Inkling is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SOMA X v0.3.0 or Inkling?+
Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and Inkling is —.
Do SOMA X v0.3.0 and Inkling support reasoning and tool use?+
SOMA X v0.3.0: none of these features are definitively sourced. Inkling: 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 Inkling?+
SOMA X v0.3.0 has 0 sourced provider routes; Inkling has 4, so Inkling has broader tracked availability.
Which offers better value, SOMA X v0.3.0 or Inkling?+
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.