Mistral Large 3 vs Stable Diffusion 3.5 Medium

At a Glance

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Mistral Large 3Mistral AI
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.25Openrouter · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.75Openrouter · Sep 22, 2026Not reported
Context windowMaximum documented tokens262K0K
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 →

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

FieldMistral Large 3stable-diffusion-3.5-medium
DeveloperMistral AIStability AI
FamilyMistral Large 3Stable Diffusion 3 5 Medium
ModelMistral Large 3stable-diffusion-3.5-medium
VersionMistral Large 3stable-diffusion-3.5-medium
Lifecycleactiveactive
Released2025-12-022024-10-29
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, DocumentText
Output modalitiesTextImage
Context window262K0K
Total parameters675B2.5B
Active parameters41BUnknown
LicenseApache-2.0other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessMistral AI (Standard), Openrouter (Standard)Hugging Face (Standard), Stability AI (Pay as you go)
Capabilitiesagents, chat, generation, structured_outputs, tools, visiongeneration

Mistral Large 3 Capabilities

agentschatgenerationstructured outputstoolsvision
Serving providers2
Canonical IDmistralai/mistral-large-2512

Stable Diffusion 3.5 Medium Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Mistral Large 3 vs Stable Diffusion 3.5 Medium FAQs

Is Mistral Large 3 or Stable Diffusion 3.5 Medium better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 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, Mistral Large 3 or Stable Diffusion 3.5 Medium?+

Only Mistral Large 3 has a directly sourced input price: $0.25 per million tokens. Only Mistral Large 3 has a directly sourced output price: $0.75 per million tokens.

Which has a larger context window, Mistral Large 3 or Stable Diffusion 3.5 Medium?+

Mistral Large 3 has the larger sourced context window. Mistral Large 3 supports 262K and Stable Diffusion 3.5 Medium supports 0K.

Which performs better in benchmarks, Mistral Large 3 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 Mistral Large 3 or Stable Diffusion 3.5 Medium be self-hosted?+

Both models have the same recorded self-hosting status: supported. Mistral Large 3 is open weight; Stable Diffusion 3.5 Medium is open weight.

Can Mistral Large 3 and Stable Diffusion 3.5 Medium understand images?+

Mistral Large 3 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, Mistral Large 3 or Stable Diffusion 3.5 Medium?+

Neither has a larger sourced maximum output. Mistral Large 3 is — and Stable Diffusion 3.5 Medium is —.

Do Mistral Large 3 and Stable Diffusion 3.5 Medium support reasoning and tool use?+

Mistral Large 3: 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, Mistral Large 3 or Stable Diffusion 3.5 Medium?+

Mistral Large 3 has 2 sourced provider routes; Stable Diffusion 3.5 Medium has 2, a tie.

Which offers better value, Mistral Large 3 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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