SOMA X v0.3.0 vs Qwen3.8 Flash
At a Glance
| Compare |  SOMA X v0.3.0NVIDIA |  Qwen3.8 FlashQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #4 of 44$0.021 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.113Deepinfra ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.382Deepinfra ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Sep 2, 2026View model evidence → | Aug 29, 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 | Qwen3.8-Flash |
|---|---|---|
| Developer | NVIDIA | Qwen |
| Family | Soma X | Qwen3 8 Flash |
| Model | SOMA-X v0.3.0 | Qwen3.8-Flash |
| Version | SOMA-X v0.3.0 | Qwen3.8-Flash |
| Lifecycle | active | active |
| Released | 2026-09-02 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Model-specific input | Text, Image, Video |
| Output modalities | 3D | Text |
| Context window | Unknown | 1,000K |
| 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 | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
SOMA X v0.3.0 Capabilities
Qwen3.8 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
SOMA X v0.3.0 vs Qwen3.8 Flash FAQs
Is SOMA X v0.3.0 or Qwen3.8 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and Qwen3.8 Flash, 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 Qwen3.8 Flash?+
Only Qwen3.8 Flash has a directly sourced input price: $0.113 per million tokens. Only Qwen3.8 Flash has a directly sourced output price: $0.382 per million tokens.
Which has a larger context window, SOMA X v0.3.0 or Qwen3.8 Flash?+
Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and Qwen3.8 Flash is 1,000K.
Which performs better in benchmarks, SOMA X v0.3.0 or Qwen3.8 Flash?+
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 Qwen3.8 Flash 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; Qwen3.8 Flash is not marked open weight.
Can SOMA X v0.3.0 and Qwen3.8 Flash understand images?+
SOMA X v0.3.0 is not documented with image input; Qwen3.8 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SOMA X v0.3.0 or Qwen3.8 Flash?+
Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and Qwen3.8 Flash is 131K.
Do SOMA X v0.3.0 and Qwen3.8 Flash support reasoning and tool use?+
SOMA X v0.3.0: none of these features are definitively sourced. Qwen3.8 Flash: 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 Qwen3.8 Flash?+
SOMA X v0.3.0 has 0 sourced provider routes; Qwen3.8 Flash has 4, so Qwen3.8 Flash has broader tracked availability.
Which offers better value, SOMA X v0.3.0 or Qwen3.8 Flash?+
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.