SOMA X v0.3.0 vs GPT-4.1

At a Glance

Compare
GPT-4.1OpenAI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#44 of 463.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 2.5–35.8
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.00Openrouter · Sep 3, 2026
Output priceFrom · USD / 1M tokensNot reported$4.00Openrouter · Sep 3, 2026
Context windowMaximum documented tokensNot reported1,048K
Model facts checkedSep 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 →

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-4.1
DeveloperNVIDIAOpenAI
FamilySoma XGpt 4 1
ModelSOMA-X v0.3.0GPT-4.1
VersionSOMA-X v0.3.0GPT-4.1
Lifecycleactiveactive
Released2026-09-02Unknown
Knowledge cutoffUnknown2024-06-01
Input modalitiesModel-specific inputText, Image
Output modalities3DText
Context windowUnknown1,048K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableNoYes
Self-hostableYesNo
Provider accessUnknownOpenai (Standard), Openrouter (Standard)
Capabilitiesanimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulationchat, generation, tools

SOMA X v0.3.0 Capabilities

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

GPT-4.1 Capabilities

chatgenerationtools
Serving providers2
Canonical IDopenai/gpt-4.1

Primary Evidence

Sources and Freshness

Questions

SOMA X v0.3.0 vs GPT-4.1 FAQs

Is SOMA X v0.3.0 or GPT-4.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and GPT-4.1, 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-4.1?+

Only GPT-4.1 has a directly sourced input price: $1.00 per million tokens. Only GPT-4.1 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, SOMA X v0.3.0 or GPT-4.1?+

Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and GPT-4.1 is 1,048K.

Which performs better in benchmarks, SOMA X v0.3.0 or GPT-4.1?+

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-4.1 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; GPT-4.1 is not marked open weight.

Can SOMA X v0.3.0 and GPT-4.1 understand images?+

SOMA X v0.3.0 is not documented with image input; GPT-4.1 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, SOMA X v0.3.0 or GPT-4.1?+

Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and GPT-4.1 is 33K.

Do SOMA X v0.3.0 and GPT-4.1 support reasoning and tool use?+

SOMA X v0.3.0: none of these features are definitively sourced. GPT-4.1: 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 GPT-4.1?+

SOMA X v0.3.0 has 0 sourced provider routes; GPT-4.1 has 2, so GPT-4.1 has broader tracked availability.

Which offers better value, SOMA X v0.3.0 or GPT-4.1?+

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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