SmolVLA 450M vs phi-4

At a Glance

Compare
SmolVLA 450MHugging Face LeRobot
phi-4Microsoft
Pricing and Limits
Context windowMaximum documented tokensNot reported16K
Model facts checkedAug 29, 2026View model evidence →Aug 28, 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

FieldSmolVLA 450Mphi-4
DeveloperHugging Face LeRobotMicrosoft
FamilySmolVLAPhi 4
ModelSmolVLA 450Mphi-4
Version450Mphi-4
Lifecycleactiveactive
Released2025-06-032024-12-12
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Robot stateText
Output modalitiesRobot actionText
Context windowUnknown16K
Total parameters450M14.7B
Active parametersUnknownUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableNoYes
Self-hostableYesYes
Provider accessUnknownDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)
Capabilitiesasynchronous-inference, fine-tuning, low-cost-hardware, manipulationchat, generation
Robotics model typeVision-language-action modelUnknown
Action representationContinuous action chunks from a flow-matching action expertUnknown
Control architectureSmolVLM2 backbone with flow-matching action expertUnknown
Inference locationOn deviceUnknown
Native control rate (Hz)UnknownUnknown
Supported embodimentsSO-100, SO-101, LeKiwi, LIBERO FrankaUnknown
Training dataCompatibly licensed LeRobot community datasets totaling fewer than 30,000 episodes in the cited release.Unknown

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

phi-4 Capabilities

chatgeneration
Model typeUnknown
InferenceUnknown
Action representationUnknown
Supported embodimentsUnknown
Canonical IDmicrosoft/phi-4

Primary Evidence

Sources and Freshness

Questions

SmolVLA 450M vs phi-4 FAQs

Is SmolVLA 450M or phi-4 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SmolVLA 450M and phi-4, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, SmolVLA 450M or phi-4?+

Only phi-4 has a directly sourced input price: $0.070 per million tokens. Only phi-4 has a directly sourced output price: $0.14 per million tokens.

Which has a larger context window, SmolVLA 450M or phi-4?+

Neither model has a larger sourced context window in this comparison. SmolVLA 450M is — and phi-4 is 16K.

Which performs better in benchmarks, SmolVLA 450M or phi-4?+

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

Can SmolVLA 450M or phi-4 be self-hosted?+

Both models have the same recorded self-hosting status: supported. SmolVLA 450M is open weight; phi-4 is open weight.

Can SmolVLA 450M and phi-4 understand images?+

SmolVLA 450M is documented with image input; phi-4 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, SmolVLA 450M or phi-4?+

Neither has a larger sourced maximum output. SmolVLA 450M is — and phi-4 is —.

Do SmolVLA 450M and phi-4 support reasoning and tool use?+

SmolVLA 450M: image input. phi-4: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, SmolVLA 450M or phi-4?+

SmolVLA 450M has 0 sourced provider routes; phi-4 has 3, so phi-4 has broader tracked availability.

Which offers better value, SmolVLA 450M or phi-4?+

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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