Claude Fable5 vs GPT-5.5 — Benchmarks, Pricing & Which to Pick

This is a dated analysis. The maintained, updated comparison with sourced benchmarks and current pricing lives on the Claude Fable 5 vs GPT-5.5 page.

This is the comparison everyone asks first, and the one where the published numbers are most lopsided — in both directions. Claude Fable5 posts the largest benchmark leads we've seen between frontier models; GPT-5.5 posts a price that makes those leads irrelevant for most production traffic. Both things are true.

The benchmark table

Three honest notes. First — these are lab-published numbers, settings chosen by the labs; treat them as directional. Second, GPT-5.5's vision score (24.9%) beats Claude Opus 4.8 (22.5%) — if your workload is vision-heavy and you're choosing between mid-tier models, that row matters more than the headline. Third, on cybersecurity and biology evals Fable5's safeguards cap its scores near Opus 4.8 — the unrestricted numbers belong to Mythos 5, which you can't buy.

FrontierCode is the real story

A 22-point lead on SWE-Bench Pro is large. A 5x lead on FrontierCode (29.3% vs 5.7%) is a different category of gap. FrontierCode Diamond is the closest public proxy for "engineering tasks that currently fail" — multi-repo refactors, novel debugging, long-horizon autonomous work. At 5.7%, GPT-5.5 effectively can't play in that tier; at 29.3%, Fable5 sometimes can. If your roadmap contains tasks you've already watched frontier models fail, this single row is the comparison.

Pricing: GPT-5.5's counterpunch

• GPT-5.5: $1.25 input / $10 output per million tokens, 90% off cached input.
• Fable5: $10 input / $50 output, 90% off cached input, 50% off via Batch API.

That's 8x on input, 5x on output. For chat products, extraction pipelines, summarization — anything both models do well — GPT-5.5's price ends the conversation. The benchmark gap only earns its premium on tasks where GPT-5.5 actually fails and retries (or humans) cost real money. The cost calculator lets you model both, including caching.

Where each model wins

Fable5:

1. Frontier-hard engineering. The FrontierCode tier — one expensive attempt beats N cheap failures. The retry math gets stronger as the price gap grows, but only where the cheap model genuinely fails.
2. Multi-hour autonomous agents. Fable5 holds coherence across millions of tokens and self-corrects with progress notes; this is where the SWE-Bench Pro gap compounds.
3. High-stakes knowledge work. 163 points on GDPval-AA, plus literal instruction-following that makes output contracts actually bind.

GPT-5.5:

1. Everything high-volume. At 1/8th the input price, the default lane belongs to it (or to Opus 5 at $5/$25 if you want to stay in the Claude ecosystem).
2. Vision on a budget. The one row it wins against models above its price class.
3. OpenAI-stack products. Structured outputs, the Assistants tooling, and existing GPT integrations carry switching costs no benchmark offsets.

Prompting: don't paste, port

GPT-5.5 keeps temperature/top_p and forgives loose prompts. Fable5 removed both knobs and executes instructions literally — vague prompts produce literal-minded results, precise prompts produce exactly what you asked. Moving a GPT-5.5 prompt to Fable5: strip sampling tricks, state the role/task/format/constraints explicitly, and put the full spec in the first message. The prompt generator scaffolds this structure; the prompting guide explains the migration.

Verdict

• Default lane: GPT-5.5 (or Opus 5) — price wins wherever quality clears the bar.
• Escalation lane: Fable5 — frontier-hard coding, long autonomous runs, expensive-to-get-wrong work.
• Vision-heavy mid-tier: give GPT-5.5 a real evaluation before paying Claude prices.

The teams getting this right aren't picking a winner — they're writing routing rules. Model the split.

FAQ

Is Fable5 better than GPT-5.5? On published benchmarks, yes — every row except none, with the FrontierCode gap (5x) the most consequential. GPT-5.5 wins decisively on price.

Is GPT-5.5 good enough for coding? For routine tasks, yes, at a fraction of the cost. For the hardest tier, the published 5.7% FrontierCode score says no.

Can I A/B them through one API? Yes — both are on OpenRouter (anthropic/claude-fable-5), which is the lowest-friction way to run the same eval set against both.

Benchmark figures from Anthropic's June 9, 2026 announcement and OpenAI's published GPT-5.5 results, as of June 10, 2026. Independent analysis — see the full Fable5 guide and the Gemini comparison.