ERNIE X1.1 vs Stable Diffusion 3.5 Medium

At a Glance

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Pricing and Limits
Context windowMaximum documented tokens66K0K
Model facts checkedAug 29, 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

FieldERNIE X1.1stable-diffusion-3.5-medium
DeveloperBaiduStability AI
FamilyErnie X1Stable Diffusion 3 5 Medium
ModelERNIE X1.1stable-diffusion-3.5-medium
VersionERNIE X1.1stable-diffusion-3.5-medium
Lifecycleactiveactive
Released2025-09-262024-10-29
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextImage
Context window66K0K
Total parametersUnknown2.5B
Active parametersUnknownUnknown
LicenseUnknownother
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessBaidu Qianfan (Standard)Hugging Face (Standard), Stability AI (Pay as you go)
Capabilitiesagents, chat, reasoning, search, toolsgeneration

ERNIE X1.1 Capabilities

agentschatreasoningsearchtools
Serving providers1
Canonical IDbaidu/ernie-x1.1

Stable Diffusion 3.5 Medium Capabilities

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

Primary Evidence

Sources and Freshness

Questions

ERNIE X1.1 vs Stable Diffusion 3.5 Medium FAQs

Is ERNIE X1.1 or Stable Diffusion 3.5 Medium better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE X1.1 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, ERNIE X1.1 or Stable Diffusion 3.5 Medium?+

Only ERNIE X1.1 has a directly sourced input price: $1.00 per million tokens. Only ERNIE X1.1 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, ERNIE X1.1 or Stable Diffusion 3.5 Medium?+

ERNIE X1.1 has the larger sourced context window. ERNIE X1.1 supports 66K and Stable Diffusion 3.5 Medium supports 0K.

Which performs better in benchmarks, ERNIE X1.1 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 ERNIE X1.1 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. ERNIE X1.1 is not marked open weight; Stable Diffusion 3.5 Medium is open weight.

Can ERNIE X1.1 and Stable Diffusion 3.5 Medium understand images?+

ERNIE X1.1 is not 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, ERNIE X1.1 or Stable Diffusion 3.5 Medium?+

Neither has a larger sourced maximum output. ERNIE X1.1 is 66K and Stable Diffusion 3.5 Medium is —.

Do ERNIE X1.1 and Stable Diffusion 3.5 Medium support reasoning and tool use?+

ERNIE X1.1: reasoning and tool calling. 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, ERNIE X1.1 or Stable Diffusion 3.5 Medium?+

ERNIE X1.1 has 1 sourced provider route; Stable Diffusion 3.5 Medium has 2, so Stable Diffusion 3.5 Medium has broader tracked availability.

Which offers better value, ERNIE X1.1 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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