Granite 4.1 8B vs OpenVLA 7B

At a Glance

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OpenVLA 7BOpenVLA Research Team
Pricing and Limits
Context windowMaximum documented tokens131KNot reported
Model facts checkedAug 28, 2026View model evidence →Aug 29, 2026View model evidence →
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

Fieldgranite-4.1-8bOpenVLA 7B
DeveloperIBMOpenVLA Research Team
FamilyGranite 4 1 8bOpenVLA
Modelgranite-4.1-8bOpenVLA 7B
Versiongranite-4.1-8b7B
Lifecycleactiveactive
Released2026-04-292024-06-13
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Robot state
Output modalitiesTextRobot action
Context window131KUnknown
Total parameters8.8B7B
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesYes
API availableYesNo
Self-hostableYesYes
Provider accessOpenrouter (Standard)Unknown
Capabilitieschat, generation, toolscross-embodiment, fine-tuning, generalist-manipulation
Robotics model typeUnknownVision-language-action model
Action representationUnknownTokenized actions decoded to continuous robot controls
Control architectureUnknownFused SigLIP and DINOv2 visual encoder with Llama 2 7B backbone
Inference locationUnknownFlexible
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnknownWidowX, Google Robot, Franka Panda
Training dataUnknown970,000 robot manipulation trajectories from Open X-Embodiment described by the authors.

Granite 4.1 8B Capabilities

chatgenerationtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDibm-granite/granite-4.1-8b

OpenVLA 7B Capabilities

cross-embodimentfine-tuninggeneralist-manipulation
Model typeVision-language-action model
InferenceFlexible
Action representationTokenized actions decoded to continuous robot controls
Supported embodiments3
Canonical IDopenvla/openvla-7b

Primary Evidence

Sources and Freshness

Questions

Granite 4.1 8B vs OpenVLA 7B FAQs

Is Granite 4.1 8B or OpenVLA 7B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite 4.1 8B and OpenVLA 7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Granite 4.1 8B or OpenVLA 7B?+

Only Granite 4.1 8B has a directly sourced input price: $0.050 per million tokens. Only Granite 4.1 8B has a directly sourced output price: $0.10 per million tokens.

Which has a larger context window, Granite 4.1 8B or OpenVLA 7B?+

Neither model has a larger sourced context window in this comparison. Granite 4.1 8B is 131K and OpenVLA 7B is —.

Which performs better in benchmarks, Granite 4.1 8B or OpenVLA 7B?+

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

Can Granite 4.1 8B or OpenVLA 7B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite 4.1 8B is open weight; OpenVLA 7B is open weight.

Can Granite 4.1 8B and OpenVLA 7B understand images?+

Granite 4.1 8B is not documented with image input; OpenVLA 7B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Granite 4.1 8B or OpenVLA 7B?+

Neither has a larger sourced maximum output. Granite 4.1 8B is — and OpenVLA 7B is —.

Do Granite 4.1 8B and OpenVLA 7B support reasoning and tool use?+

Granite 4.1 8B: tool calling. OpenVLA 7B: image input. Feature support does not establish relative quality.

Which is available from more inference providers, Granite 4.1 8B or OpenVLA 7B?+

Granite 4.1 8B has 1 sourced provider route; OpenVLA 7B has 0, so Granite 4.1 8B has broader tracked availability.

Which offers better value, Granite 4.1 8B or OpenVLA 7B?+

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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