Llama 3.1 405B Instruct vs OpenVLA 7B
At a Glance
| Compare | OpenVLA 7BOpenVLA Research Team | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 131K | Not reported |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | Llama-3.1-405B-Instruct | OpenVLA 7B |
|---|---|---|
| Developer | Meta | OpenVLA Research Team |
| Family | Llama 3 1 405b Instruct | OpenVLA |
| Model | Llama-3.1-405B-Instruct | OpenVLA 7B |
| Version | Llama-3.1-405B-Instruct | 7B |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2024-06-13 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | 131K | Unknown |
| Total parameters | 405.9B | 7B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | Unknown |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, tools | cross-embodiment, fine-tuning, generalist-manipulation |
| Robotics model type | Unknown | Vision-language-action model |
| Action representation | Unknown | Tokenized actions decoded to continuous robot controls |
| Control architecture | Unknown | Fused SigLIP and DINOv2 visual encoder with Llama 2 7B backbone |
| Inference location | Unknown | Flexible |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unknown | WidowX, Google Robot, Franka Panda |
| Training data | Unknown | 970,000 robot manipulation trajectories from Open X-Embodiment described by the authors. |
Llama 3.1 405B Instruct Capabilities
OpenVLA 7B Capabilities
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 405B Instruct vs OpenVLA 7B FAQs
Is Llama 3.1 405B Instruct or OpenVLA 7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B Instruct and OpenVLA 7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B Instruct 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, Llama 3.1 405B Instruct or OpenVLA 7B?+
Neither model has a larger sourced context window in this comparison. Llama 3.1 405B Instruct is 131K and OpenVLA 7B is —.
Which performs better in benchmarks, Llama 3.1 405B Instruct or OpenVLA 7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 405B Instruct or OpenVLA 7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 405B Instruct is open weight; OpenVLA 7B is open weight.
Can Llama 3.1 405B Instruct and OpenVLA 7B understand images?+
Llama 3.1 405B Instruct 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, Llama 3.1 405B Instruct or OpenVLA 7B?+
Neither has a larger sourced maximum output. Llama 3.1 405B Instruct is — and OpenVLA 7B is —.
Do Llama 3.1 405B Instruct and OpenVLA 7B support reasoning and tool use?+
Llama 3.1 405B Instruct: tool calling. OpenVLA 7B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 405B Instruct or OpenVLA 7B?+
Llama 3.1 405B Instruct has 1 sourced provider route; OpenVLA 7B has 0, so Llama 3.1 405B Instruct has broader tracked availability.
Which offers better value, Llama 3.1 405B Instruct 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.