ERNIE 4.5 0.3B PT vs OpenVLA 7B
At a Glance
| Compare | ERNIE 4.5 0.3B PTBaidu | 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 | ERNIE-4.5-0.3B-PT | OpenVLA 7B |
|---|---|---|
| Developer | Baidu | OpenVLA Research Team |
| Family | Ernie 4 5 0 3b Pt | OpenVLA |
| Model | ERNIE-4.5-0.3B-PT | OpenVLA 7B |
| Version | ERNIE-4.5-0.3B-PT | 7B |
| Lifecycle | active | active |
| Released | Unknown | 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 | 360.7M | 7B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | Yes |
| API available | Unknown | No |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | chat, generation | 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. |
ERNIE 4.5 0.3B PT Capabilities
OpenVLA 7B Capabilities
Primary Evidence
Sources and Freshness
Questions
ERNIE 4.5 0.3B PT vs OpenVLA 7B FAQs
Is ERNIE 4.5 0.3B PT or OpenVLA 7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE 4.5 0.3B PT and OpenVLA 7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, ERNIE 4.5 0.3B PT 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, ERNIE 4.5 0.3B PT or OpenVLA 7B?+
Neither model has a larger sourced context window in this comparison. ERNIE 4.5 0.3B PT is 131K and OpenVLA 7B is —.
Which performs better in benchmarks, ERNIE 4.5 0.3B PT or OpenVLA 7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can ERNIE 4.5 0.3B PT or OpenVLA 7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. ERNIE 4.5 0.3B PT is open weight; OpenVLA 7B is open weight.
Can ERNIE 4.5 0.3B PT and OpenVLA 7B understand images?+
ERNIE 4.5 0.3B PT 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, ERNIE 4.5 0.3B PT or OpenVLA 7B?+
Neither has a larger sourced maximum output. ERNIE 4.5 0.3B PT is — and OpenVLA 7B is —.
Do ERNIE 4.5 0.3B PT and OpenVLA 7B support reasoning and tool use?+
ERNIE 4.5 0.3B PT: none of these features are definitively sourced. OpenVLA 7B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, ERNIE 4.5 0.3B PT or OpenVLA 7B?+
ERNIE 4.5 0.3B PT has 0 sourced provider routes; OpenVLA 7B has 0, a tie.
Which offers better value, ERNIE 4.5 0.3B PT 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.