Fable 5 Review:
vs Opus 4.8 vs GPT-5.5
The short verdict: Fable 5 leads on capability across the board — but you pay 2× the price and accept slower output. This page helps you decide whether your workload justifies it.
The numbers, side by side
| Fable 5 | Claude Opus 4.8 | GPT-5.5 | |
|---|---|---|---|
| SWE-Bench Pro | 80.3% | 69.2% | 58.6% |
| AA Intelligence Index | 65 | Price-tier median: 36 | |
| API price (in/out, per million) | $10 / $50 | $5 / $25 | $5 input (~half of Fable) |
| Context window | 1M tokens | — | — |
| Max output | 128K tokens | — | — |
| Output speed | 60.3 t/s (tier median 68.7) | Faster | Faster |
| Time to first token (TTFT) | ~81.7s (tier median 2.71s) | Low | Low |
| Bulk offline discount | Batch half price ($5/$25) | Batch half price | Flex $2.50/$15 |
| Safety fallback mechanism | Yes (Opus 4.8 answers when triggered) | No | No |
| Data retention requirement | 30 days (safety monitoring) | None | None |
Speed and intelligence-index figures from Artificial Analysis; SWE-Bench Pro figures published by Anthropic. Independent third-party reviews are still limited this close to launch — numbers will be updated as they land.
When is the 2× price worth it?
✅ Worth it: the longer and harder the task, the bigger the gap
- Long-horizon agent work: multi-day autonomous runs in harnesses like Claude Code — planning across stages, delegating subtasks, self-correcting. This is where Fable 5 pulls furthest ahead.
- Large-codebase engineering: a full-library migration of a 50-million-line Ruby codebase finished in one day (human estimate: two-plus months); Stripe reported "months of engineering compressed into days."
- Very long context: a 1M-token window with sustained focus across it; with file-based memory its improvement is 3× larger than Opus 4.8's.
- Vision tasks: current vision SOTA — reading exact values off scientific charts, reconstructing page source from screenshots, beating Pokémon from raw screenshots alone.
- The hidden cost inversion: on genuinely hard tasks, Fable 5 reaches the same quality with fewer tokens and less rework, so the effective cost can come out lower. True for long-horizon reasoning; not true for short tasks.
❌ Not worth it: short, high-volume, latency-sensitive
- Classification, summarization, templated generation — Opus 4.8 or even Sonnet 4.6 is better economics.
- Real-time interaction that is sensitive to first-response latency (TTFT of ~81.7 seconds is among the highest in its tier).
- Massive offline batch jobs — GPT-5.5's Flex pricing ($2.50/$15) remains the cost king.
The one-line verdict
Use the cheapest model that reliably clears your quality bar: daily work → Sonnet 4.6; workhorse → Opus 4.8; hard problems (long tasks / big codebases / deep reasoning) → Fable 5. Run your own numbers with the cost calculator.
Two things comparisons tend to miss
① The safety fallback affects consistency. Fable 5's classifiers hand requests touching cybersecurity, biology, or chemistry to Opus 4.8 (billed at Opus rates, with a notice). Teams in security research or bioinformatics should estimate the impact of this <5% trigger rate on their pipelines.
② Compliance differs. Fable 5 carries a mandatory 30-day data-retention window (not used for training; human access is logged). GPT-5.5 has no equivalent requirement — put this on the evaluation sheet if you have data-residency constraints.
Want to run the comparison yourself?
OmniaKey: one key to test Fable 5 / GPT-5.5 / Gemini 3.1 Pro side by side · Fable 5 at $3/$15 limited-time · no card required
FAQ
How does Fable 5 compare to Gemini 3.1 Pro?
There's no directly comparable public SWE-Bench Pro score for Gemini 3.1 Pro yet, so this page doesn't put it in the main table. On published results, Fable 5 currently leads every publicly available model on coding and long-horizon agent benchmarks. For your own workload, the honest answer is to run the same prompts through both — one OmniaKey key covers Fable 5, GPT-5.5, and Gemini 3.1 Pro.
Is Fable 5 actually worth it? (Review in one paragraph)
Capability: best public model available, by a clear margin on coding and agentic work. Speed: among the slowest in its tier, with ~80s first-token latency. Price: 2× Opus 4.8. If your work is long, complex, and compounding — agents, big migrations, deep reasoning — it's worth it, and effective cost can even come out lower. For short routine tasks, it isn't.
Why is Fable 5 so slow to start responding?
It's tuned for deep reasoning, and time-to-first-token can reach ~81.7 seconds. Always use streaming and raise client timeouts — engineering notes at fableapi.app.
Fable 5 or Opus 4.8 for coding?
For multi-file refactors, large codebases, and long agent sessions, Fable 5 (SWE-Bench Pro 80.3% vs 69.2%). For routine edits and quick fixes, Opus 4.8 is faster and half the price — many teams route by task difficulty and switch with a one-line model-string change.