OCR 4.1 vs OpenVLA 7B

At a Glance

Compare
OCR 4.1Mistral AI
OpenVLA 7BOpenVLA Research Team
Pricing and Limits
Context windowMaximum documented tokensNot reportedNot reported
Model facts checkedAug 29, 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

FieldOCR 4.1OpenVLA 7B
DeveloperMistral AIOpenVLA Research Team
FamilyMistral OCROpenVLA
ModelOCR 4.1OpenVLA 7B
VersionOCR 4.17B
Lifecycleactiveactive
Released2026-07-162024-06-13
Knowledge cutoffUnknownUnknown
Input modalitiesImage, DocumentText, Image, Robot state
Output modalitiesTextRobot action
Context windowUnknownUnknown
Total parametersUnknown7B
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoYes
API availableYesNo
Self-hostableNoYes
Provider accessMistral AI (Standard)Unknown
Capabilitiesbounding-box-extraction, document-ai, ocr, structured-annotationscross-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.

OCR 4.1 Capabilities

bounding-box-extractiondocument-aiocrstructured-annotations
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDmistralai/mistral-ocr-4-1

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

OCR 4.1 vs OpenVLA 7B FAQs

Is OCR 4.1 or OpenVLA 7B better for coding?+

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

Which is cheaper, OCR 4.1 or OpenVLA 7B?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

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

Neither model has a larger sourced context window in this comparison. OCR 4.1 is — and OpenVLA 7B is —.

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

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

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

OpenVLA 7B is the only model in this pair currently marked as self-hostable. OCR 4.1 is not marked open weight; OpenVLA 7B is open weight.

Can OCR 4.1 and OpenVLA 7B understand images?+

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

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

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

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

OCR 4.1: image input. OpenVLA 7B: image input. Feature support does not establish relative quality.

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

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

Which offers better value, OCR 4.1 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.

Send Feedback