Stable Diffusion 3.5 Large vs GLM 5.1

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.05Deepinfra · Sep 23, 2026
Output priceFrom · USD / 1M tokensNot reported$3.50Deepinfra · Sep 23, 2026
Context windowMaximum documented tokens0K203K
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 →

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

Fieldstable-diffusion-3.5-largeGLM-5.1
DeveloperStability AIZ.ai
FamilyStable Diffusion 3 5 LargeGlm 5 1
Modelstable-diffusion-3.5-largeGLM-5.1
Versionstable-diffusion-3.5-largeGLM-5.1
Lifecycleactiveactive
Released2024-10-22Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesImageText
Context window0K203K
Total parameters8.1B753.9B
Active parametersUnknownUnknown
Licenseothermit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessHugging Face (Standard), Stability AI (Pay as you go)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesgenerationchat, generation, reasoning, tools

Stable Diffusion 3.5 Large Capabilities

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

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

Primary Evidence

Sources and Freshness

Questions

Stable Diffusion 3.5 Large vs GLM 5.1 FAQs

Is Stable Diffusion 3.5 Large or GLM 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Stable Diffusion 3.5 Large and GLM 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Stable Diffusion 3.5 Large or GLM 5.1?+

Only GLM 5.1 has a directly sourced input price: $1.05 per million tokens. Only GLM 5.1 has a directly sourced output price: $3.50 per million tokens.

Which has a larger context window, Stable Diffusion 3.5 Large or GLM 5.1?+

GLM 5.1 has the larger sourced context window. Stable Diffusion 3.5 Large supports 0K and GLM 5.1 supports 203K.

Which performs better in benchmarks, Stable Diffusion 3.5 Large or GLM 5.1?+

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

Can Stable Diffusion 3.5 Large or GLM 5.1 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Stable Diffusion 3.5 Large is open weight; GLM 5.1 is open weight.

Can Stable Diffusion 3.5 Large and GLM 5.1 understand images?+

Stable Diffusion 3.5 Large is not documented with image input; GLM 5.1 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Stable Diffusion 3.5 Large or GLM 5.1?+

Neither has a larger sourced maximum output. Stable Diffusion 3.5 Large is — and GLM 5.1 is —.

Do Stable Diffusion 3.5 Large and GLM 5.1 support reasoning and tool use?+

Stable Diffusion 3.5 Large: none of these features are definitively sourced. GLM 5.1: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Stable Diffusion 3.5 Large or GLM 5.1?+

Stable Diffusion 3.5 Large has 2 sourced provider routes; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.

Which offers better value, Stable Diffusion 3.5 Large or GLM 5.1?+

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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