Claude Mythos 5.1 vs Stable Diffusion 3.5 Large
At a Glance
| Compare | Claude Mythos 5.1Anthropic | Stable Diffusion 3.5 LargeStability AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $10.00Anthropic ↗ · Sep 2, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $50.00Anthropic ↗ · Sep 2, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,000K | 0K |
| Model facts checked | Sep 2, 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
Side-by-Side Facts
| Field | Claude Mythos 5.1 | stable-diffusion-3.5-large |
|---|---|---|
| Developer | Anthropic | Stability AI |
| Family | Claude 5 1 | Stable Diffusion 3 5 Large |
| Model | Claude Mythos 5.1 | stable-diffusion-3.5-large |
| Version | Claude Mythos 5.1 | stable-diffusion-3.5-large |
| Lifecycle | active | active |
| Released | 2026-09-01 | 2024-10-22 |
| Knowledge cutoff | 2026-06-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 1,000K | 0K |
| Total parameters | Unknown | 8.1B |
| Active parameters | Unknown | Unknown |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Project Glasswing) | Hugging Face (Standard), Stability AI (Pay as you go) |
| Capabilities | chat, generation, reasoning, tools | generation |
Claude Mythos 5.1 Capabilities
Stable Diffusion 3.5 Large Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Mythos 5.1 vs Stable Diffusion 3.5 Large FAQs
Is Claude Mythos 5.1 or Stable Diffusion 3.5 Large better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Mythos 5.1 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, Claude Mythos 5.1 or Stable Diffusion 3.5 Large?+
Only Claude Mythos 5.1 has a directly sourced input price: $10.00 per million tokens. Only Claude Mythos 5.1 has a directly sourced output price: $50.00 per million tokens.
Which has a larger context window, Claude Mythos 5.1 or Stable Diffusion 3.5 Large?+
Claude Mythos 5.1 has the larger sourced context window. Claude Mythos 5.1 supports 1,000K and Stable Diffusion 3.5 Large supports 0K.
Which performs better in benchmarks, Claude Mythos 5.1 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 Claude Mythos 5.1 or Stable Diffusion 3.5 Large be self-hosted?+
Stable Diffusion 3.5 Large is the only model in this pair currently marked as self-hostable. Claude Mythos 5.1 is not marked open weight; Stable Diffusion 3.5 Large is open weight.
Can Claude Mythos 5.1 and Stable Diffusion 3.5 Large understand images?+
Claude Mythos 5.1 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, Claude Mythos 5.1 or Stable Diffusion 3.5 Large?+
Neither has a larger sourced maximum output. Claude Mythos 5.1 is 128K and Stable Diffusion 3.5 Large is —.
Do Claude Mythos 5.1 and Stable Diffusion 3.5 Large support reasoning and tool use?+
Claude Mythos 5.1: reasoning, 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, Claude Mythos 5.1 or Stable Diffusion 3.5 Large?+
Claude Mythos 5.1 has 1 sourced provider route; Stable Diffusion 3.5 Large has 2, so Stable Diffusion 3.5 Large has broader tracked availability.
Which offers better value, Claude Mythos 5.1 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.