SmolVLA 450M vs Llama 3.1 405B Instruct
At a Glance
| Compare |  SmolVLA 450MHugging Face LeRobot | |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 131K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | SmolVLA 450M | Llama-3.1-405B-Instruct |
|---|---|---|
| Developer | Hugging Face LeRobot | Meta |
| Family | SmolVLA | Llama 3 1 405b Instruct |
| Model | SmolVLA 450M | Llama-3.1-405B-Instruct |
| Version | 450M | Llama-3.1-405B-Instruct |
| Lifecycle | active | active |
| Released | 2025-06-03 | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text |
| Output modalities | Robot action | Text |
| Context window | Unknown | 131K |
| Total parameters | 450M | 405.9B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | llama3.1 |
| Open weights | Yes | Yes |
| API available | No | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Together Ai (Standard) |
| Capabilities | asynchronous-inference, fine-tuning, low-cost-hardware, manipulation | chat, generation, tools |
| Robotics model type | Vision-language-action model | Unknown |
| Action representation | Continuous action chunks from a flow-matching action expert | Unknown |
| Control architecture | SmolVLM2 backbone with flow-matching action expert | Unknown |
| Inference location | On device | Unknown |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | SO-100, SO-101, LeKiwi, LIBERO Franka | Unknown |
| Training data | Compatibly licensed LeRobot community datasets totaling fewer than 30,000 episodes in the cited release. | Unknown |
SmolVLA 450M Capabilities
Llama 3.1 405B Instruct Capabilities
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
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SmolVLA 450MHugging Face LeRobot | vs | Grok 4.6xAI | cross-developer peers |
SmolVLA 450MHugging Face LeRobot | vs |  Qwen3.8-MaxQwen | cross-developer peers |
SmolVLA 450MHugging Face LeRobot | vs |  Kimi-K3Moonshot AI | cross-developer peers |
 MiniMax-M3MiniMax | vs | SmolVLA 450MHugging Face LeRobot | cross-developer peers |
SmolVLA 450MHugging Face LeRobot | vs |  GLM-5.3Z.ai | cross-developer peers |
SmolVLA 450MHugging Face LeRobot | vs |  Hy4 previewTencent | cross-developer peers |
Seed 2.1 ProByteDance Seed | vs | SmolVLA 450MHugging Face LeRobot | cross-developer peers |
SmolVLA 450MHugging Face LeRobot | vs |  Mistral Large 3Mistral AI | cross-developer peers |
Primary Evidence
Sources and Freshness
Questions
SmolVLA 450M vs Llama 3.1 405B Instruct FAQs
Is SmolVLA 450M or Llama 3.1 405B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SmolVLA 450M and Llama 3.1 405B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, SmolVLA 450M or Llama 3.1 405B Instruct?+
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, SmolVLA 450M or Llama 3.1 405B Instruct?+
Neither model has a larger sourced context window in this comparison. SmolVLA 450M is — and Llama 3.1 405B Instruct is 131K.
Which performs better in benchmarks, SmolVLA 450M or Llama 3.1 405B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can SmolVLA 450M or Llama 3.1 405B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. SmolVLA 450M is open weight; Llama 3.1 405B Instruct is open weight.
Can SmolVLA 450M and Llama 3.1 405B Instruct understand images?+
SmolVLA 450M is documented with image input; Llama 3.1 405B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SmolVLA 450M or Llama 3.1 405B Instruct?+
Neither has a larger sourced maximum output. SmolVLA 450M is — and Llama 3.1 405B Instruct is —.
Do SmolVLA 450M and Llama 3.1 405B Instruct support reasoning and tool use?+
SmolVLA 450M: image input. Llama 3.1 405B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, SmolVLA 450M or Llama 3.1 405B Instruct?+
SmolVLA 450M has 0 sourced provider routes; Llama 3.1 405B Instruct has 1, so Llama 3.1 405B Instruct has broader tracked availability.
Which offers better value, SmolVLA 450M or Llama 3.1 405B Instruct?+
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.