SOMA X v0.3.0 vs Jev
At a Glance
| Compare |  SOMA X v0.3.0NVIDIA |  JevTypeSafe |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.042TypeSafe ↗ · Sep 17, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.000TypeSafe ↗ · Sep 17, 2026 |
| Context windowMaximum documented tokens | Not reported | 64K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 17, 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 | Jev |
|---|---|---|
| Developer | NVIDIA | TypeSafe |
| Family | Soma X | Jev |
| Model | SOMA-X v0.3.0 | Jev |
| Version | SOMA-X v0.3.0 | Jev |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Model-specific input | Text, Model-specific input |
| Output modalities | 3D | Model-specific input |
| Context window | Unknown | 64K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | TypeSafe (Standard) |
| Capabilities | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Maximum Choice cardinality | Unknown | 255 options |
| Default request rate limit | Unknown | 1200 requests per minute |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
SOMA X v0.3.0 Capabilities
Jev Capabilities
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
SOMA-X v0.3.0NVIDIA | vs |  GPT-6 AstraOpenAI | cross-developer peers |
Claude Fable 5.1Anthropic | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
 Gemini 3.1 ProGoogle DeepMind | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
 DeepSeek-V4.1-FlashDeepSeek | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
SOMA-X v0.3.0NVIDIA | vs | Grok 4.6xAI | cross-developer peers |
SOMA-X v0.3.0NVIDIA | vs |  Qwen3.8-MaxQwen | cross-developer peers |
 Kimi-K3Moonshot AI | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
 MiniMax-M3MiniMax | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
SOMA-X v0.3.0NVIDIA | vs |  GLM-5.3Z.ai | cross-developer peers |
SOMA-X v0.3.0NVIDIA | vs |  Hy4 previewTencent | cross-developer peers |
Seed 2.1 ProByteDance Seed | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
 Mistral Large 3Mistral AI | vs | SOMA-X v0.3.0NVIDIA | cross-developer peers |
Primary Evidence
Sources and Freshness
Questions
SOMA X v0.3.0 vs Jev FAQs
Is SOMA X v0.3.0 or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and Jev, 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 Jev?+
Only Jev has a directly sourced input price: $0.042 per million tokens. Only Jev has a directly sourced output price: $0.000 per million tokens.
Which has a larger context window, SOMA X v0.3.0 or Jev?+
Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and Jev is 64K.
Which performs better in benchmarks, SOMA X v0.3.0 or Jev?+
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 Jev be self-hosted?+
SOMA X v0.3.0 is the only model in this pair currently marked as self-hostable. SOMA X v0.3.0 is open weight; Jev is not marked open weight.
Can SOMA X v0.3.0 and Jev understand images?+
SOMA X v0.3.0 is not documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SOMA X v0.3.0 or Jev?+
Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and Jev is —.
Do SOMA X v0.3.0 and Jev support reasoning and tool use?+
SOMA X v0.3.0: none of these features are definitively sourced. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, SOMA X v0.3.0 or Jev?+
SOMA X v0.3.0 has 0 sourced provider routes; Jev has 1, so Jev has broader tracked availability.
Which offers better value, SOMA X v0.3.0 or Jev?+
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.