Mistral Large 3 vs SOMA X v0.3.0

At a Glance

Compare
Mistral Large 3Mistral AI
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.25Openrouter · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.75Openrouter · Sep 22, 2026Not reported
Context windowMaximum documented tokens262KNot reported
Model facts checkedAug 29, 2026View model evidence →Sep 2, 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

FieldMistral Large 3SOMA-X v0.3.0
DeveloperMistral AINVIDIA
FamilyMistral Large 3Soma X
ModelMistral Large 3SOMA-X v0.3.0
VersionMistral Large 3SOMA-X v0.3.0
Lifecycleactiveactive
Released2025-12-022026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, DocumentModel-specific input
Output modalitiesText3D
Context window262KUnknown
Total parameters675BUnknown
Active parameters41BUnknown
LicenseApache-2.0apache-2.0
Open weightsYesYes
API availableYesNo
Self-hostableYesYes
Provider accessMistral AI (Standard), Openrouter (Standard)Unknown
Capabilitiesagents, chat, generation, structured_outputs, tools, visionanimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation

Mistral Large 3 Capabilities

agentschatgenerationstructured outputstoolsvision
Serving providers2
Canonical IDmistralai/mistral-large-2512

SOMA X v0.3.0 Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Mistral Large 3 vs SOMA X v0.3.0 FAQs

Is Mistral Large 3 or SOMA X v0.3.0 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 and SOMA X v0.3.0, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Mistral Large 3 or SOMA X v0.3.0?+

Only Mistral Large 3 has a directly sourced input price: $0.25 per million tokens. Only Mistral Large 3 has a directly sourced output price: $0.75 per million tokens.

Which has a larger context window, Mistral Large 3 or SOMA X v0.3.0?+

Neither model has a larger sourced context window in this comparison. Mistral Large 3 is 262K and SOMA X v0.3.0 is —.

Which performs better in benchmarks, Mistral Large 3 or SOMA X v0.3.0?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Mistral Large 3 or SOMA X v0.3.0 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Mistral Large 3 is open weight; SOMA X v0.3.0 is open weight.

Can Mistral Large 3 and SOMA X v0.3.0 understand images?+

Mistral Large 3 is documented with image input; SOMA X v0.3.0 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Mistral Large 3 or SOMA X v0.3.0?+

Neither has a larger sourced maximum output. Mistral Large 3 is — and SOMA X v0.3.0 is —.

Do Mistral Large 3 and SOMA X v0.3.0 support reasoning and tool use?+

Mistral Large 3: tool calling and image input. SOMA X v0.3.0: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Mistral Large 3 or SOMA X v0.3.0?+

Mistral Large 3 has 2 sourced provider routes; SOMA X v0.3.0 has 0, so Mistral Large 3 has broader tracked availability.

Which offers better value, Mistral Large 3 or SOMA X v0.3.0?+

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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