Mistral Large 3 vs stable-diffusion-3.5-large

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
Mistral AI · activeMistral Large 3Verified Aug 29, 2026
Stability AI · activestable-diffusion-3.5-largeVerified Aug 28, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldMistral Large 3stable-diffusion-3.5-large
DeveloperMistral AIStability AI
FamilyMistral Large 3Stable Diffusion 3 5 Large
ModelMistral Large 3stable-diffusion-3.5-large
VersionMistral Large 3stable-diffusion-3.5-large
Lifecycleactiveactive
Released2025-12-022024-10-22
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, DocumentText
Output modalitiesTextImage
Context window262,144256
Total parameters675,000,000,0008,146,280,768
Active parameters41,000,000,000Unknown
LicenseApache-2.0other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessMistral AI (Standard), Openrouter (Standard)Stability AI (Pay as you go)
Capabilitiesagents, chat, generation, structured_outputs, tools, visiongeneration

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

Mistral Large 3 Capabilities

agentschatgenerationstructured outputstoolsvision
Input price$0.50
Output price$1.50
Serving providers2
Canonical IDmistralai/mistral-large-2512

stable-diffusion-3.5-large Capabilities

generation
Input price
Output price
Serving providers1
Canonical IDstabilityai/stable-diffusion-3.5-large

Internal Comparison Graph

Related Comparisons

All image comparisons →
APairBContext
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Primary Evidence

Sources and Freshness

Questions

Mistral Large 3 vs stable-diffusion-3.5-large FAQs

Is Mistral Large 3 or stable-diffusion-3.5-large better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 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, Mistral Large 3 or stable-diffusion-3.5-large?+

Only Mistral Large 3 has a directly sourced input price: $0.50 per million tokens. Only Mistral Large 3 has a directly sourced output price: $1.50 per million tokens.

Which has a larger context window, Mistral Large 3 or stable-diffusion-3.5-large?+

Mistral Large 3 has the larger sourced context window. Mistral Large 3 supports 262,144 and stable-diffusion-3.5-large supports 256.

Which performs better in benchmarks, Mistral Large 3 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 Mistral Large 3 or stable-diffusion-3.5-large be self-hosted?+

Both models have the same recorded self-hosting status: supported. Mistral Large 3 is open weight; stable-diffusion-3.5-large is open weight.

Can Mistral Large 3 and stable-diffusion-3.5-large understand images?+

Mistral Large 3 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, Mistral Large 3 or stable-diffusion-3.5-large?+

Neither has a larger sourced maximum output. Mistral Large 3 is — and stable-diffusion-3.5-large is —.

Do Mistral Large 3 and stable-diffusion-3.5-large support reasoning and tool use?+

Mistral Large 3: 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, Mistral Large 3 or stable-diffusion-3.5-large?+

Mistral Large 3 has 2 sourced provider routes; stable-diffusion-3.5-large has 1, so Mistral Large 3 has broader tracked availability.

Which offers better value, Mistral Large 3 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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