GPT-4.1 vs Stable Diffusion 3.5 Medium
At a Glance
| Compare |  GPT-4.1OpenAI |  Stable Diffusion 3.5 MediumStability AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #44 of 463.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 2.5–35.8 | UnrankedNot in the 46-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.00Openrouter ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $4.00Openrouter ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,048K | 0K |
| Model facts checked | Aug 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
Side-by-Side Facts
| Field | GPT-4.1 | stable-diffusion-3.5-medium |
|---|---|---|
| Developer | OpenAI | Stability AI |
| Family | Gpt 4 1 | Stable Diffusion 3 5 Medium |
| Model | GPT-4.1 | stable-diffusion-3.5-medium |
| Version | GPT-4.1 | stable-diffusion-3.5-medium |
| Lifecycle | active | active |
| Released | Unknown | 2024-10-29 |
| Knowledge cutoff | 2024-06-01 | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 1,048K | 0K |
| Total parameters | Unknown | 2.5B |
| Active parameters | Unknown | Unknown |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Openai (Standard), Openrouter (Standard) | Hugging Face (Standard), Stability AI (Pay as you go) |
| Capabilities | chat, generation, tools | generation |
GPT-4.1 Capabilities
Stable Diffusion 3.5 Medium Capabilities
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Primary Evidence
Sources and Freshness
Questions
GPT-4.1 vs Stable Diffusion 3.5 Medium FAQs
Is GPT-4.1 or Stable Diffusion 3.5 Medium better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both GPT-4.1 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, GPT-4.1 or Stable Diffusion 3.5 Medium?+
Only GPT-4.1 has a directly sourced input price: $1.00 per million tokens. Only GPT-4.1 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, GPT-4.1 or Stable Diffusion 3.5 Medium?+
GPT-4.1 has the larger sourced context window. GPT-4.1 supports 1,048K and Stable Diffusion 3.5 Medium supports 0K.
Which performs better in benchmarks, GPT-4.1 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 GPT-4.1 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. GPT-4.1 is not marked open weight; Stable Diffusion 3.5 Medium is open weight.
Can GPT-4.1 and Stable Diffusion 3.5 Medium understand images?+
GPT-4.1 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, GPT-4.1 or Stable Diffusion 3.5 Medium?+
Neither has a larger sourced maximum output. GPT-4.1 is 33K and Stable Diffusion 3.5 Medium is —.
Do GPT-4.1 and Stable Diffusion 3.5 Medium support reasoning and tool use?+
GPT-4.1: 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, GPT-4.1 or Stable Diffusion 3.5 Medium?+
GPT-4.1 has 2 sourced provider routes; Stable Diffusion 3.5 Medium has 2, a tie.
Which offers better value, GPT-4.1 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.