Llama 4 Maverick 17B 128E vs Stable Diffusion 3.5 Medium

At a Glance

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Pricing and Limits
Context windowMaximum documented tokens1,000K0K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

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

FieldLlama-4-Maverick-17B-128Estable-diffusion-3.5-medium
DeveloperMetaStability AI
FamilyLlama 4 Maverick 17b 128eStable Diffusion 3 5 Medium
ModelLlama-4-Maverick-17B-128Estable-diffusion-3.5-medium
VersionLlama-4-Maverick-17B-128Estable-diffusion-3.5-medium
Lifecycleactiveactive
Released2025-04-052024-10-29
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText
Output modalitiesTextImage
Context window1,000K0K
Total parameters401.6B2.5B
Active parameters17BUnknown
Licenseotherother
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownHugging Face (Standard), Stability AI (Pay as you go)
Capabilitieschat, generation, toolsgeneration

Llama 4 Maverick 17B 128E Capabilities

chatgenerationtools
Serving providers0
Canonical IDmeta-llama/Llama-4-Maverick-17B-128E

Stable Diffusion 3.5 Medium Capabilities

generation
Serving providers2
Canonical IDstabilityai/stable-diffusion-3.5-medium

Primary Evidence

Sources and Freshness

Questions

Llama 4 Maverick 17B 128E vs Stable Diffusion 3.5 Medium FAQs

Is Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E and Stable Diffusion 3.5 Medium, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

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, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

Llama 4 Maverick 17B 128E has the larger sourced context window. Llama 4 Maverick 17B 128E supports 1,000K and Stable Diffusion 3.5 Medium supports 0K.

Which performs better in benchmarks, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

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

Can Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 4 Maverick 17B 128E is open weight; Stable Diffusion 3.5 Medium is open weight.

Can Llama 4 Maverick 17B 128E and Stable Diffusion 3.5 Medium understand images?+

Llama 4 Maverick 17B 128E is documented with image input; Stable Diffusion 3.5 Medium is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

Neither has a larger sourced maximum output. Llama 4 Maverick 17B 128E is — and Stable Diffusion 3.5 Medium is —.

Do Llama 4 Maverick 17B 128E and Stable Diffusion 3.5 Medium support reasoning and tool use?+

Llama 4 Maverick 17B 128E: tool calling and image input. Stable Diffusion 3.5 Medium: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

Llama 4 Maverick 17B 128E has 0 sourced provider routes; Stable Diffusion 3.5 Medium has 2, so Stable Diffusion 3.5 Medium has broader tracked availability.

Which offers better value, Llama 4 Maverick 17B 128E or Stable Diffusion 3.5 Medium?+

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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