Granite Vision 4.1 4B vs Stable Diffusion 3.5 Large

At a Glance

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Pricing and Limits
Context windowMaximum documented tokens131K0K
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

Fieldgranite-vision-4.1-4bstable-diffusion-3.5-large
DeveloperIBMStability AI
FamilyGranite Vision 4 1 4bStable Diffusion 3 5 Large
Modelgranite-vision-4.1-4bstable-diffusion-3.5-large
Versiongranite-vision-4.1-4bstable-diffusion-3.5-large
Lifecycleactiveactive
Released2026-04-292024-10-22
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText
Output modalitiesTextImage
Context window131K0K
Total parameters4B8.1B
Active parametersUnknownUnknown
Licenseapache-2.0other
Open weightsYesYes
API availableUnknownYes
Self-hostableYesYes
Provider accessUnknownHugging Face (Standard), Stability AI (Pay as you go)
Capabilitieschat, generation, toolsgeneration

Granite Vision 4.1 4B Capabilities

chatgenerationtools
Serving providers0
Canonical IDibm-granite/granite-vision-4.1-4b

Stable Diffusion 3.5 Large Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Granite Vision 4.1 4B vs Stable Diffusion 3.5 Large FAQs

Is Granite Vision 4.1 4B or Stable Diffusion 3.5 Large better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite Vision 4.1 4B and Stable Diffusion 3.5 Large, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

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, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

Granite Vision 4.1 4B has the larger sourced context window. Granite Vision 4.1 4B supports 131K and Stable Diffusion 3.5 Large supports 0K.

Which performs better in benchmarks, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

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

Can Granite Vision 4.1 4B or Stable Diffusion 3.5 Large be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite Vision 4.1 4B is open weight; Stable Diffusion 3.5 Large is open weight.

Can Granite Vision 4.1 4B and Stable Diffusion 3.5 Large understand images?+

Granite Vision 4.1 4B is documented with image input; Stable Diffusion 3.5 Large is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

Neither has a larger sourced maximum output. Granite Vision 4.1 4B is — and Stable Diffusion 3.5 Large is —.

Do Granite Vision 4.1 4B and Stable Diffusion 3.5 Large support reasoning and tool use?+

Granite Vision 4.1 4B: tool calling and image input. Stable Diffusion 3.5 Large: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

Granite Vision 4.1 4B has 0 sourced provider routes; Stable Diffusion 3.5 Large has 2, so Stable Diffusion 3.5 Large has broader tracked availability.

Which offers better value, Granite Vision 4.1 4B or Stable Diffusion 3.5 Large?+

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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