Gemini 2.5 Pro vs Stable Diffusion 3.5 Medium
At a Glance
| Compare |  Gemini 2.5 ProGoogle DeepMind |  Stable Diffusion 3.5 MediumStability AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.25Google AI ↗ · Aug 29, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $10.00Google AI ↗ · Aug 29, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 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 | Gemini 2.5 Pro | stable-diffusion-3.5-medium |
|---|---|---|
| Developer | Google DeepMind | Stability AI |
| Family | Gemini 2 5 | Stable Diffusion 3 5 Medium |
| Model | Gemini 2.5 Pro | stable-diffusion-3.5-medium |
| Version | Gemini 2.5 Pro | stable-diffusion-3.5-medium |
| Lifecycle | active | active |
| Released | Unknown | 2024-10-29 |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text | Image |
| Context window | 1,049K | 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 | Google AI (Standard), Google Gemini (Standard) | Hugging Face (Standard), Stability AI (Pay as you go) |
| Capabilities | chat, generation, reasoning, tools | generation |
Gemini 2.5 Pro Capabilities
Stable Diffusion 3.5 Medium Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini 2.5 Pro vs Stable Diffusion 3.5 Medium FAQs
Is Gemini 2.5 Pro or Stable Diffusion 3.5 Medium better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 2.5 Pro 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, Gemini 2.5 Pro or Stable Diffusion 3.5 Medium?+
Only Gemini 2.5 Pro has a directly sourced input price: $1.25 per million tokens. Only Gemini 2.5 Pro has a directly sourced output price: $10.00 per million tokens.
Which has a larger context window, Gemini 2.5 Pro or Stable Diffusion 3.5 Medium?+
Gemini 2.5 Pro has the larger sourced context window. Gemini 2.5 Pro supports 1,049K and Stable Diffusion 3.5 Medium supports 0K.
Which performs better in benchmarks, Gemini 2.5 Pro 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 Gemini 2.5 Pro 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. Gemini 2.5 Pro is not marked open weight; Stable Diffusion 3.5 Medium is open weight.
Can Gemini 2.5 Pro and Stable Diffusion 3.5 Medium understand images?+
Gemini 2.5 Pro 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, Gemini 2.5 Pro or Stable Diffusion 3.5 Medium?+
Neither has a larger sourced maximum output. Gemini 2.5 Pro is 66K and Stable Diffusion 3.5 Medium is —.
Do Gemini 2.5 Pro and Stable Diffusion 3.5 Medium support reasoning and tool use?+
Gemini 2.5 Pro: reasoning, 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, Gemini 2.5 Pro or Stable Diffusion 3.5 Medium?+
Gemini 2.5 Pro has 2 sourced provider routes; Stable Diffusion 3.5 Medium has 2, a tie.
Which offers better value, Gemini 2.5 Pro 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.