Gemma 4 31B vs Stable Diffusion 3.5 Medium
At a Glance
| Compare |  Gemma 4 31BGoogle DeepMind |  Stable Diffusion 3.5 MediumStability AI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.090Openrouter ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.34Openrouter ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 0K |
| Model facts checked | Sep 3, 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 | Gemma 4 31B | stable-diffusion-3.5-medium |
|---|---|---|
| Developer | Google DeepMind | Stability AI |
| Family | Gemma 4 | Stable Diffusion 3 5 Medium |
| Model | Gemma 4 31B | stable-diffusion-3.5-medium |
| Version | Gemma 4 31B | stable-diffusion-3.5-medium |
| Lifecycle | active | active |
| Released | 2026-03-11 | 2024-10-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Image |
| Context window | 262K | 0K |
| Total parameters | 31B | 2.5B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Cerebras (Standard), Deepinfra (Standard), Google Gemini (Standard), Openrouter (Standard), Together Ai (Standard) | Hugging Face (Standard), Stability AI (Pay as you go) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | generation |
Gemma 4 31B Capabilities
Stable Diffusion 3.5 Medium Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemma 4 31B vs Stable Diffusion 3.5 Medium FAQs
Is Gemma 4 31B or Stable Diffusion 3.5 Medium better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 31B 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, Gemma 4 31B or Stable Diffusion 3.5 Medium?+
Only Gemma 4 31B has a directly sourced input price: $0.090 per million tokens. Only Gemma 4 31B has a directly sourced output price: $0.34 per million tokens.
Which has a larger context window, Gemma 4 31B or Stable Diffusion 3.5 Medium?+
Gemma 4 31B has the larger sourced context window. Gemma 4 31B supports 262K and Stable Diffusion 3.5 Medium supports 0K.
Which performs better in benchmarks, Gemma 4 31B 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 Gemma 4 31B or Stable Diffusion 3.5 Medium be self-hosted?+
Both models have the same recorded self-hosting status: supported. Gemma 4 31B is open weight; Stable Diffusion 3.5 Medium is open weight.
Can Gemma 4 31B and Stable Diffusion 3.5 Medium understand images?+
Gemma 4 31B 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, Gemma 4 31B or Stable Diffusion 3.5 Medium?+
Neither has a larger sourced maximum output. Gemma 4 31B is — and Stable Diffusion 3.5 Medium is —.
Do Gemma 4 31B and Stable Diffusion 3.5 Medium support reasoning and tool use?+
Gemma 4 31B: 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, Gemma 4 31B or Stable Diffusion 3.5 Medium?+
Gemma 4 31B has 5 sourced provider routes; Stable Diffusion 3.5 Medium has 2, so Gemma 4 31B has broader tracked availability.
Which offers better value, Gemma 4 31B 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.