Claude Fable5 vs Gemini 3.1 Pro — Benchmarks, Pricing & Verdict

This is a dated launch-week analysis. The maintained, updated comparison with sourced benchmarks and current pricing lives on the Claude Fable 5 vs Gemini 3.1 Pro page.

The honest version of this comparison starts with a disclaimer most launch-week posts skip: cross-lab benchmarks are run by the labs themselves, under settings they chose. With that on the table — the published gap between Claude Fable5 and Gemini 3.1 Pro is unusually large, and it points in different directions depending on what you're buying.

The benchmark table, with the honest footnote

Two footnotes the launch posts bury. First, on cybersecurity and biology evals, Fable5's safeguards cap its scores near Opus 4.8 — the headline numbers there belong to the restricted Mythos 5. Second, Gemini's missing FrontierCode entry isn't necessarily damning — Google simply hasn't published one — but absence of evidence is all you get.

Pricing: the gap runs the other way

• Fable5: $10 input / $50 output per million tokens. Prompt caching takes 90% off cached input; Batch API takes 50% off everything.
• Gemini 3.1 Pro: roughly $2 input / $12 output (≤200K-token prompts; long-context pricing is higher). Context caching discounts apply.

On list price, Gemini 3.1 Pro is about 4–5x cheaper. For high-volume production traffic — summarization, extraction, chat — that multiple is the whole story. No benchmark lead survives a 5x price gap on work both models can do. Run the numbers for your own workload in our cost calculator.

Where each model actually wins

Route to Fable5 when:

1. The task is agentic and long. The 26-point SWE-Bench Pro gap widens on multi-hour autonomous runs — this is the workload Fable5 was built for.
2. Retries are the real cost. On frontier-hard problems, cost-per-solved-task matters more than cost-per-token, and the cheaper model's failed attempts aren't free. The same retry math that beats Opus 4.8 applies with more force at a 5x price gap — if the cheap model genuinely can't solve the task.
3. Documents are dense and visual. 29.8% vs 16.7% on GDP.pdf (vision without tools) is the quiet headline of the table.

Route to Gemini 3.1 Pro when:

1. Volume dominates. Production traffic where both models clear the quality bar belongs on the cheaper model, full stop.
2. You live in the Google stack. Native integration with Workspace, Vertex AI pipelines, and Google's grounding/search tooling has real workflow value no benchmark captures.
3. Latency is a feature. Fable5 at high effort thinks long; interactive products often can't wait.

The prompting difference nobody mentions

Fable5 removed temperature and top_p entirely — output style is steered by prompt and the effort parameter. Gemini keeps classic sampling controls. Practical consequence: prompts ported from Gemini to Fable5 should drop sampling-dependent tricks and state desired variation explicitly. Our prompt generator builds Fable5-native prompts; the migration notes cover the rest.

Verdict

By task, not by model:

• Frontier-hard coding, long agent runs, dense-document vision → Fable5
• High-volume production, Google-stack integration, latency-sensitive UX → Gemini 3.1 Pro
• Mixed workloads → Gemini (or Opus 5) as the default lane, Fable5 as the escalation lane for the 10–20% of tasks that justify it. Estimate the split cost.

FAQ

Is Fable5 better than Gemini 3.1 Pro? On published benchmarks, yes — by 26 points on SWE-Bench Pro and 618 points on GDPval-AA. On price and latency, Gemini wins by a similar margin.

Is the comparison fair? Treat it as directional. Lab-published, settings differ, and Gemini has no FrontierCode score to compare.

Can I run both? That's the recommended pattern: cheap default lane, expensive escalation lane, explicit routing rules. Both APIs are OpenRouter-compatible if you want one integration surface.

Benchmark figures from Anthropic's June 9, 2026 announcement and Google's published Gemini 3.1 Pro results, as of June 10, 2026. Independent analysis — see the full Fable5 guide.