SOMA X v0.3.0 vs MiMo V2.6 Pro

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.435Openrouter · Sep 23, 2026
Output priceFrom · USD / 1M tokensNot reported$0.87Openrouter · Sep 23, 2026
Context windowMaximum documented tokensNot reported1,049K
Model facts checkedSep 2, 2026View model evidence →Sep 22, 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 v0.3.0MiMo-V2.6-Pro
DeveloperNVIDIAXiaomi
FamilySoma XMimo V2 6
ModelSOMA-X v0.3.0MiMo-V2.6-Pro
VersionSOMA-X v0.3.0MiMo-V2.6-Pro
Lifecycleactiveactive
Released2026-09-022026-09-21
Knowledge cutoffUnknownUnknown
Input modalitiesModel-specific inputText, Image, Video, Audio
Output modalities3DText
Context windowUnknown1,049K
Total parametersUnknown1T
Active parametersUnknown42B
Licenseapache-2.0MIT
Open weightsYesYes
API availableNoYes
Self-hostableYesYes
Provider accessUnknownOpenrouter (Standard), Xiaomi MiMo (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

MiMo V2.6 Pro Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDxiaomi/mimo-v2.6-pro

Primary Evidence

Sources and Freshness

Questions

SOMA X v0.3.0 vs MiMo V2.6 Pro FAQs

Is SOMA X v0.3.0 or MiMo V2.6 Pro better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and MiMo V2.6 Pro, 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 MiMo V2.6 Pro?+

Only MiMo V2.6 Pro has a directly sourced input price: $0.435 per million tokens. Only MiMo V2.6 Pro has a directly sourced output price: $0.87 per million tokens.

Which has a larger context window, SOMA X v0.3.0 or MiMo V2.6 Pro?+

Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and MiMo V2.6 Pro is 1,049K.

Which performs better in benchmarks, SOMA X v0.3.0 or MiMo V2.6 Pro?+

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 MiMo V2.6 Pro be self-hosted?+

Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; MiMo V2.6 Pro is open weight.

Can SOMA X v0.3.0 and MiMo V2.6 Pro understand images?+

SOMA X v0.3.0 is not documented with image input; MiMo V2.6 Pro is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, SOMA X v0.3.0 or MiMo V2.6 Pro?+

Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and MiMo V2.6 Pro is 131K.

Do SOMA X v0.3.0 and MiMo V2.6 Pro support reasoning and tool use?+

SOMA X v0.3.0: none of these features are definitively sourced. MiMo V2.6 Pro: 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 MiMo V2.6 Pro?+

SOMA X v0.3.0 has 0 sourced provider routes; MiMo V2.6 Pro has 2, so MiMo V2.6 Pro has broader tracked availability.

Which offers better value, SOMA X v0.3.0 or MiMo V2.6 Pro?+

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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