OpenVLA 7B vs Grok 4.5
At a Glance
| Compare |  OpenVLA 7BOpenVLA Research Team | Grok 4.5xAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #25 of 4660.1 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #15 of 44$0.063 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #11 of 3859.2 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 500K |
| Model facts checked | Aug 29, 2026View model evidence → | Sep 3, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | OpenVLA 7B | Grok 4.5 |
|---|---|---|
| Developer | OpenVLA Research Team | xAI |
| Family | OpenVLA | Grok 4 |
| Model | OpenVLA 7B | Grok 4.5 |
| Version | 7B | Grok 4.5 |
| Lifecycle | active | active |
| Released | 2024-06-13 | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image |
| Output modalities | Robot action | Text |
| Context window | Unknown | 500K |
| Total parameters | 7B | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | Yes | No |
| API available | No | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Xai (Standard) |
| Capabilities | cross-embodiment, fine-tuning, generalist-manipulation | chat, generation, reasoning, structured_outputs, tools |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Tokenized actions decoded to continuous robot controls | Unknown |
| Control architecture | Fused SigLIP and DINOv2 visual encoder with Llama 2 7B backbone | Unknown |
| Inference location | Flexible | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | WidowX, Google Robot, Franka Panda | Unknown |
| Training data | 970,000 robot manipulation trajectories from Open X-Embodiment described by the authors. | Unknown |
OpenVLA 7B Capabilities
Grok 4.5 Capabilities
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Primary Evidence
Sources and Freshness
Questions
OpenVLA 7B vs Grok 4.5 FAQs
Is OpenVLA 7B or Grok 4.5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both OpenVLA 7B and Grok 4.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, OpenVLA 7B or Grok 4.5?+
Only Grok 4.5 has a directly sourced input price: $2.00 per million tokens. Only Grok 4.5 has a directly sourced output price: $6.00 per million tokens.
Which has a larger context window, OpenVLA 7B or Grok 4.5?+
Neither model has a larger sourced context window in this comparison. OpenVLA 7B is — and Grok 4.5 is 500K.
Which performs better in benchmarks, OpenVLA 7B or Grok 4.5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can OpenVLA 7B or Grok 4.5 be self-hosted?+
OpenVLA 7B is the only model in this pair currently marked as self-hostable. OpenVLA 7B is open weight; Grok 4.5 is not marked open weight.
Can OpenVLA 7B and Grok 4.5 understand images?+
OpenVLA 7B is documented with image input; Grok 4.5 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, OpenVLA 7B or Grok 4.5?+
Neither has a larger sourced maximum output. OpenVLA 7B is — and Grok 4.5 is —.
Do OpenVLA 7B and Grok 4.5 support reasoning and tool use?+
OpenVLA 7B: image input. Grok 4.5: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, OpenVLA 7B or Grok 4.5?+
OpenVLA 7B has 0 sourced provider routes; Grok 4.5 has 1, so Grok 4.5 has broader tracked availability.
Which offers better value, OpenVLA 7B or Grok 4.5?+
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.