SmolVLA 450M vs Mistral Medium 3.5 128B
At a Glance
| Compare | SmolVLA 450MHugging Face LeRobot | Mistral Medium 3.5 128BMistral AI |
|---|---|---|
| Pricing and Limits | ||
| Context windowMaximum documented tokens | Not reported | 262K |
| Model facts checked | Aug 29, 2026View model evidence → | Aug 28, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | SmolVLA 450M | Mistral-Medium-3.5-128B |
|---|---|---|
| Developer | Hugging Face LeRobot | Mistral AI |
| Family | SmolVLA | Mistral Medium 3 5 128b |
| Model | SmolVLA 450M | Mistral-Medium-3.5-128B |
| Version | 450M | Mistral-Medium-3.5-128B |
| Lifecycle | active | active |
| Released | 2025-06-03 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Robot state | Text, Image |
| Output modalities | Robot action | Text |
| Context window | Unknown | 262K |
| Total parameters | 450M | 127.7B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | other |
| Open weights | Yes | Yes |
| API available | No | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Unknown |
| Capabilities | asynchronous-inference, fine-tuning, low-cost-hardware, manipulation | chat, generation, reasoning, 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
Mistral Medium 3.5 128B Capabilities
Primary Evidence
Sources and Freshness
Questions
SmolVLA 450M vs Mistral Medium 3.5 128B FAQs
Is SmolVLA 450M or Mistral Medium 3.5 128B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both SmolVLA 450M and Mistral Medium 3.5 128B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, SmolVLA 450M or Mistral Medium 3.5 128B?+
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 Mistral Medium 3.5 128B?+
Neither model has a larger sourced context window in this comparison. SmolVLA 450M is — and Mistral Medium 3.5 128B is 262K.
Which performs better in benchmarks, SmolVLA 450M or Mistral Medium 3.5 128B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can SmolVLA 450M or Mistral Medium 3.5 128B be self-hosted?+
Both models have the same recorded self-hosting status: supported. SmolVLA 450M is open weight; Mistral Medium 3.5 128B is open weight.
Can SmolVLA 450M and Mistral Medium 3.5 128B understand images?+
SmolVLA 450M is documented with image input; Mistral Medium 3.5 128B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, SmolVLA 450M or Mistral Medium 3.5 128B?+
Neither has a larger sourced maximum output. SmolVLA 450M is — and Mistral Medium 3.5 128B is —.
Do SmolVLA 450M and Mistral Medium 3.5 128B support reasoning and tool use?+
SmolVLA 450M: image input. Mistral Medium 3.5 128B: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, SmolVLA 450M or Mistral Medium 3.5 128B?+
SmolVLA 450M has 0 sourced provider routes; Mistral Medium 3.5 128B has 0, a tie.
Which offers better value, SmolVLA 450M or Mistral Medium 3.5 128B?+
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.