Seed 2.1 Pro vs stable-diffusion-3.5-medium

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
ByteDance Seed · activeSeed 2.1 ProVerified Sep 3, 2026
Stability AI · activestable-diffusion-3.5-mediumVerified Aug 28, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldSeed 2.1 Prostable-diffusion-3.5-medium
DeveloperByteDance SeedStability AI
FamilySeed 2 1Stable Diffusion 3 5 Medium
ModelSeed 2.1 Prostable-diffusion-3.5-medium
VersionSeed 2.1 Prostable-diffusion-3.5-medium
Lifecycleactiveactive
Released2026-06-232024-10-29
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, VideoText
Output modalitiesTextImage
Context windowUnknown256
Total parametersUnknown2,469,663,936
Active parametersUnknownUnknown
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessUnknownHugging Face (Standard), Stability AI (Pay as you go)
Capabilitiesagents, chat, computer-use, generation, reasoning, structured_outputs, toolsgeneration

12 comparable fields · 10 material differences · Pair passes the primary-source comparison gate

Seed 2.1 Pro Capabilities

agentschatcomputer-usegenerationreasoningstructured outputstools
Input price
Output price
Serving providers0
Canonical IDbytedance-seed/seed-2.1-pro

stable-diffusion-3.5-medium Capabilities

generation
Input price
Output price
Serving providers2
Canonical IDstabilityai/stable-diffusion-3.5-medium

Internal Comparison Graph

Related Comparisons

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vscross-developer peersimage, text, video
vscross-developer peersimage, text, video
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vscross-developer peersimage, text
vscross-developer peersimage, text
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Primary Evidence

Sources and Freshness

Questions

Seed 2.1 Pro vs stable-diffusion-3.5-medium FAQs

Is Seed 2.1 Pro or stable-diffusion-3.5-medium better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Seed 2.1 Pro 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, Seed 2.1 Pro 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, Seed 2.1 Pro or stable-diffusion-3.5-medium?+

Neither model has a larger sourced context window in this comparison. Seed 2.1 Pro is — and stable-diffusion-3.5-medium is 256.

Which performs better in benchmarks, Seed 2.1 Pro 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 Seed 2.1 Pro or stable-diffusion-3.5-medium be self-hosted?+

stable-diffusion-3.5-medium is the only model in this pair currently marked as self-hostable. Seed 2.1 Pro is not marked open weight; stable-diffusion-3.5-medium is open weight.

Can Seed 2.1 Pro and stable-diffusion-3.5-medium understand images?+

Seed 2.1 Pro 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, Seed 2.1 Pro or stable-diffusion-3.5-medium?+

Neither has a larger sourced maximum output. Seed 2.1 Pro is — and stable-diffusion-3.5-medium is —.

Do Seed 2.1 Pro and stable-diffusion-3.5-medium support reasoning and tool use?+

Seed 2.1 Pro: reasoning, 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, Seed 2.1 Pro or stable-diffusion-3.5-medium?+

Seed 2.1 Pro 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, Seed 2.1 Pro 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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