Stable Diffusion 3.5 Large vs Jev
At a Glance
| Compare |  Stable Diffusion 3.5 LargeStability AI |  JevTypeSafe |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.042TypeSafe ↗ · Sep 17, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.000TypeSafe ↗ · Sep 17, 2026 |
| Context windowMaximum documented tokens | 0K | 64K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 17, 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 | stable-diffusion-3.5-large | Jev |
|---|---|---|
| Developer | Stability AI | TypeSafe |
| Family | Stable Diffusion 3 5 Large | Jev |
| Model | stable-diffusion-3.5-large | Jev |
| Version | stable-diffusion-3.5-large | Jev |
| Lifecycle | active | active |
| Released | 2024-10-22 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Model-specific input |
| Output modalities | Image | Model-specific input |
| Context window | 0K | 64K |
| Total parameters | 8.1B | Unknown |
| Active parameters | Unknown | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard), Stability AI (Pay as you go) | TypeSafe (Standard) |
| Capabilities | generation | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Maximum Choice cardinality | Unknown | 255 options |
| Default request rate limit | Unknown | 1200 requests per minute |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
Stable Diffusion 3.5 Large Capabilities
Jev Capabilities
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Primary Evidence
Sources and Freshness
Questions
Stable Diffusion 3.5 Large vs Jev FAQs
Is Stable Diffusion 3.5 Large or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Stable Diffusion 3.5 Large and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Stable Diffusion 3.5 Large or Jev?+
Only Jev has a directly sourced input price: $0.042 per million tokens. Only Jev has a directly sourced output price: $0.000 per million tokens.
Which has a larger context window, Stable Diffusion 3.5 Large or Jev?+
Jev has the larger sourced context window. Stable Diffusion 3.5 Large supports 0K and Jev supports 64K.
Which performs better in benchmarks, Stable Diffusion 3.5 Large or Jev?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Stable Diffusion 3.5 Large or Jev be self-hosted?+
Stable Diffusion 3.5 Large is the only model in this pair currently marked as self-hostable. Stable Diffusion 3.5 Large is open weight; Jev is not marked open weight.
Can Stable Diffusion 3.5 Large and Jev understand images?+
Stable Diffusion 3.5 Large is not documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Stable Diffusion 3.5 Large or Jev?+
Neither has a larger sourced maximum output. Stable Diffusion 3.5 Large is — and Jev is —.
Do Stable Diffusion 3.5 Large and Jev support reasoning and tool use?+
Stable Diffusion 3.5 Large: none of these features are definitively sourced. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Stable Diffusion 3.5 Large or Jev?+
Stable Diffusion 3.5 Large has 2 sourced provider routes; Jev has 1, so Stable Diffusion 3.5 Large has broader tracked availability.
Which offers better value, Stable Diffusion 3.5 Large or Jev?+
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.