Qwen3.8 27B vs SmolVLA 450M

At a Glance

Compare
SmolVLA 450MHugging Face LeRobot
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4UnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#16 of 44$0.072 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5UnrankedNot in the 38-model eligible cohort
Pricing and Limits
Context windowMaximum documented tokens262KNot reported
Model facts checkedAug 28, 2026View model evidence →Aug 29, 2026View model evidence →
Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched evidence.

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldQwen3.8-27BSmolVLA 450M
DeveloperQwenHugging Face LeRobot
FamilyQwen3 8 27bSmolVLA
ModelQwen3.8-27BSmolVLA 450M
VersionQwen3.8-27B450M
Lifecycleactiveactive
ReleasedUnknown2025-06-03
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Robot state
Output modalitiesTextRobot action
Context window262KUnknown
Total parameters27.8B450M
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableYesNo
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)Unknown
Capabilitieschat, generation, reasoning, toolsasynchronous-inference, fine-tuning, low-cost-hardware, manipulation
Robotics model typeUnknownVision-language-action model
Action representationUnknownContinuous action chunks from a flow-matching action expert
Control architectureUnknownSmolVLM2 backbone with flow-matching action expert
Inference locationUnknownOn device
Native control rate (Hz)UnknownUnknown
Supported embodimentsUnknownSO-100, SO-101, LeKiwi, LIBERO Franka
Training dataUnknownCompatibly licensed LeRobot community datasets totaling fewer than 30,000 episodes in the cited release.

Qwen3.8 27B Capabilities

chatgenerationreasoningtools
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDQwen/Qwen3.8-27B

SmolVLA 450M Capabilities

asynchronous-inferencefine-tuninglow-cost-hardwaremanipulation
Model typeVision-language-action model
InferenceOn device
Action representationContinuous action chunks from a flow-matching action expert
Supported embodiments4
Canonical IDlerobot/smolvla_base

Primary Evidence

Sources and Freshness

Questions

Qwen3.8 27B vs SmolVLA 450M FAQs

Is Qwen3.8 27B or SmolVLA 450M better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and SmolVLA 450M, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.8 27B or SmolVLA 450M?+

Only Qwen3.8 27B has a directly sourced input price: $0.20 per million tokens. Only Qwen3.8 27B has a directly sourced output price: $2.50 per million tokens.

Which has a larger context window, Qwen3.8 27B or SmolVLA 450M?+

Neither model has a larger sourced context window in this comparison. Qwen3.8 27B is 262K and SmolVLA 450M is —.

Which performs better in benchmarks, Qwen3.8 27B or SmolVLA 450M?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Qwen3.8 27B or SmolVLA 450M be self-hosted?+

Both models have the same recorded self-hosting status: supported. Qwen3.8 27B is open weight; SmolVLA 450M is open weight.

Can Qwen3.8 27B and SmolVLA 450M understand images?+

Qwen3.8 27B is documented with image input; SmolVLA 450M is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.8 27B or SmolVLA 450M?+

Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and SmolVLA 450M is —.

Do Qwen3.8 27B and SmolVLA 450M support reasoning and tool use?+

Qwen3.8 27B: reasoning, tool calling, and image input. SmolVLA 450M: image input. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.8 27B or SmolVLA 450M?+

Qwen3.8 27B has 3 sourced provider routes; SmolVLA 450M has 0, so Qwen3.8 27B has broader tracked availability.

Which offers better value, Qwen3.8 27B or SmolVLA 450M?+

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.

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