Head to head

Fable 5 vs GPT-5.5

Day-two honesty: the cross-vendor evidence is young. Here's what actually exists, what doesn't yet, and how to settle it for your own work.

What the evidence shows so far

  • Frontier physics research — the most concrete cross-vendor data point. Notation Capital: Fable was "the strongest model on frontier physics research while using a third of the reasoning tokens. In 36 hours it got nearly to where GPT-5.5 landed after four days."
  • Anthropic's benchmark table — the announcement's comparison chart puts Fable at state-of-the-art on nearly all tested benchmarks against other leading models. Vendor-published — weigh accordingly.
  • Hands-on community tests — early side-by-sides on hard coding prompts lean Fable ("makes GPT 5.5 feel like a toy", 6-surprising-uses comparison) — enthusiast-grade evidence, ranked on our charts.

What doesn't exist yet

Independent, methodical cross-vendor evals — LMArena-style rankings with Fable included, third-party SWE-bench runs, cost-normalized agentic comparisons. It's been one day. The evidence wall adds them as they publish; claims without receipts don't go up, and that cuts in both directions.

The structural differences you can compare today

Fable 5GPT-5.5
PositioningHighest-capability tier, safeguarded Mythos-classOpenAI's frontier flagship
Sampling controlsNone — prompting onlyVendor-specific
Reasoning controlAdaptive thinking + 5 effort levelsVendor-specific
Context1M tokens, no long-context premiumSee OpenAI's current docs
Safety architectureRuntime classifiers + Opus 4.8 fallback, published red-team numbersDifferent approach; see OpenAI's system card

We deliberately don't quote GPT-5.5 specs from memory — vendor docs change. Check OpenAI's current documentation for their side of the table.

Run your own bake-off (an afternoon, ~$20)

  1. Pick your three hardest real tasks from the last month — not puzzles, your actual work.
  2. Write each as one complete brief: goal, constraints, what "done" looks like. (This favors no one — both frontier models reward specification.)
  3. Run each on both models. For Fable use our recommended defaults: adaptive thinking, effort high.
  4. Score on: did it finish without intervention; correctness; total cost from the usage fields — not tokens, completed-task cost.

Moral: benchmarks are other people's tasks. The only chart that matters is yours.