SOMA X v0.3.0 vs gpt-oss-120b

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.037Deepinfra · Sep 23, 2026
Output priceFrom · USD / 1M tokensNot reported$0.17Deepinfra · Sep 23, 2026
Context windowMaximum documented tokensNot reported131K
Model facts checkedSep 2, 2026View model evidence →Aug 28, 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 →

Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched evidence.

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldSOMA-X v0.3.0gpt-oss-120b
DeveloperNVIDIAOpenAI
FamilySoma XGpt Oss 120b
ModelSOMA-X v0.3.0gpt-oss-120b
VersionSOMA-X v0.3.0gpt-oss-120b
Lifecycleactiveactive
Released2026-09-02Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesModel-specific inputText
Output modalities3DText
Context windowUnknown131K
Total parametersUnknown116.8B
Active parametersUnknown5.1B
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableNoYes
Self-hostableYesYes
Provider accessUnknownCerebras (Standard), Deepinfra (Standard), Fireworks Ai (Serverless, Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard)
Capabilitiesanimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulationchat, generation, reasoning, tools

SOMA X v0.3.0 Capabilities

animationhand-modelinghuman-body-modelingmotion-retargetingpose-inversionsimulation
Serving providers0
Canonical IDnvidia/SOMA-X-v0.3.0

gpt-oss-120b Capabilities

chatgenerationreasoningtools
Serving providers7
Canonical IDopenai/gpt-oss-120b

Primary Evidence

Sources and Freshness

Questions

SOMA X v0.3.0 vs gpt-oss-120b FAQs

Is SOMA X v0.3.0 or gpt-oss-120b better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and gpt-oss-120b, 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 gpt-oss-120b?+

Only gpt-oss-120b has a directly sourced input price: $0.037 per million tokens. Only gpt-oss-120b has a directly sourced output price: $0.17 per million tokens.

Which has a larger context window, SOMA X v0.3.0 or gpt-oss-120b?+

Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and gpt-oss-120b is 131K.

Which performs better in benchmarks, SOMA X v0.3.0 or gpt-oss-120b?+

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 gpt-oss-120b be self-hosted?+

Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; gpt-oss-120b is open weight.

Can SOMA X v0.3.0 and gpt-oss-120b understand images?+

SOMA X v0.3.0 is not documented with image input; gpt-oss-120b 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 gpt-oss-120b?+

Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and gpt-oss-120b is —.

Do SOMA X v0.3.0 and gpt-oss-120b support reasoning and tool use?+

SOMA X v0.3.0: none of these features are definitively sourced. gpt-oss-120b: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, SOMA X v0.3.0 or gpt-oss-120b?+

SOMA X v0.3.0 has 0 sourced provider routes; gpt-oss-120b has 7, so gpt-oss-120b has broader tracked availability.

Which offers better value, SOMA X v0.3.0 or gpt-oss-120b?+

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.

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