Claude Fable 5.1 vs Gemini 3.1 Pro
Why this pair: Current Anthropic and Google frontier multimodal models
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Claude Fable 5.1 | Gemini 3.1 Pro |
|---|---|---|
| Developer | Anthropic | Google DeepMind |
| Family | Claude 5 1 | Gemini 3 |
| Model | Claude Fable 5.1 | Gemini 3.1 Pro |
| Version | Claude Fable 5.1 | Gemini 3.1 Pro |
| Lifecycle | active | preview |
| Released | 2026-09-01 | Unknown |
| Knowledge cutoff | 2026-06-01 | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 1,000,000 | 1,048,576 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Google AI (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
12 comparable fields · 7 material differences · Editorially curated pair passes the primary-source comparison gate
Claude Fable 5.1 Capabilities
Gemini 3.1 Pro Capabilities
Internal Comparison Graph
Related Comparisons
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|---|---|---|---|
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Claude Fable 5.1Anthropic | vs | GPT-5.6 SolOpenAI | cross-developer peersimage, text |
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Claude Fable 5.1Anthropic | vs | Kimi-K3Moonshot AI | cross-developer peersimage, text |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4-Flash-Vision-ExpDeepSeek | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | Hy4 previewTencent | cross-developer peerstext |
Gemini 3.1 ProGoogle DeepMind | vs | Hy4 previewTencent | cross-developer peerstext |
Gemini 2.5 ProGoogle DeepMind | vs | Gemini 3.1 ProGoogle DeepMind | family variantstext |
Primary Evidence
Sources and Freshness
Questions
Claude Fable 5.1 vs Gemini 3.1 Pro FAQs
Is Claude Fable 5.1 or Gemini 3.1 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Fable 5.1 and Gemini 3.1 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Fable 5.1 or Gemini 3.1 Pro?+
Only Gemini 3.1 Pro has a directly sourced input price: $2.00 per million tokens. Only Gemini 3.1 Pro has a directly sourced output price: $12.00 per million tokens.
Which has a larger context window, Claude Fable 5.1 or Gemini 3.1 Pro?+
Gemini 3.1 Pro has the larger sourced context window. Claude Fable 5.1 supports 1,000,000 and Gemini 3.1 Pro supports 1,048,576.
Which performs better in benchmarks, Claude Fable 5.1 or Gemini 3.1 Pro?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Fable 5.1 or Gemini 3.1 Pro be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Fable 5.1 is not marked open weight; Gemini 3.1 Pro is not marked open weight.
Can Claude Fable 5.1 and Gemini 3.1 Pro understand images?+
Claude Fable 5.1 is documented with image input; Gemini 3.1 Pro is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Fable 5.1 or Gemini 3.1 Pro?+
Claude Fable 5.1 has the larger sourced maximum output: Claude Fable 5.1 supports 128,000 and Gemini 3.1 Pro supports 65,536 output tokens.
Do Claude Fable 5.1 and Gemini 3.1 Pro support reasoning and tool use?+
Claude Fable 5.1: reasoning, tool calling, and image input. Gemini 3.1 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Fable 5.1 or Gemini 3.1 Pro?+
Claude Fable 5.1 has 0 sourced provider routes; Gemini 3.1 Pro has 1, so Gemini 3.1 Pro has broader tracked availability.
Which offers better value, Claude Fable 5.1 or Gemini 3.1 Pro?+
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.