For a few weeks in mid-2026, the two most capable AI models ever made publicly available sat side by side: OpenAI’s GPT-5.5, shipped on 23 April, and Anthropic’s Claude Fable 5, released seven weeks later on 9 June. Naturally, developers immediately started asking which one wins. The honest answer is the one that rarely makes a headline: it depends on the job — and, right now, on whether you can even access the model at all.
One important caveat before we start. As of mid-June 2026, Claude Fable 5 is temporarily suspended for all customers following a US government export-control directive, with Claude Opus 4.8 acting as the fallback. That makes this comparison part scorecard, part future-planning exercise. We cover the suspension in full in our piece on whether Claude Fable 5 will make a comeback — but it’s worth keeping in mind throughout, because availability is a real part of “which model wins.”
The short version
Across the published benchmarks, Fable 5 is the capability leader — often by wide margins, particularly in coding and reasoning. But GPT-5.5 costs roughly half as much per token, owns a mature coding ecosystem through Codex, leads on long-context retrieval, and — crucially — is actually available today. In one telling convergence, both labs now gate their highest-risk capabilities (cybersecurity, biology) behind vetted-access programmes. Let’s go round by round.
Round 1: Price and availability
This is where GPT-5.5’s practical advantages are clearest. It has been shipping since April, is deeply embedded in real workflows (especially OpenAI’s Codex), and lists at roughly $5 input / $30 output per million tokens.
Claude Fable 5 lists at around $10 / $50 per million tokens — roughly double — and carries a 30-day data-retention requirement on its Mythos-class traffic that some enterprises can’t accept. On top of that, it’s currently offline. For anyone choosing a model today, that combination makes GPT-5.5 the default by necessity as much as by cost.
Edge: GPT-5.5.
Round 2: Coding
This is Fable 5’s headline strength — with an asterisk. On Anthropic’s published benchmarks, Fable 5 leads decisively on end-to-end software engineering: SWE-Bench Pro 80.3% versus GPT-5.5’s 58.6%, and FrontierCode Diamond 29.3% versus 5.7%. It also tops LiveCodeBench, and engineers at Cursor and Stripe have praised its real-world performance on large, messy codebases.
But coding leadership depends heavily on the harness you measure with. GPT-5.5 is formidable at terminal coding through its own Codex CLI (Terminal-Bench 2.0 at 82.7%) and holds a slight edge on isolated, function-level generation (HumanEval, MBPP) and clean competitive-programming problems. Independent testing under matched scaffolds has also shown a far tighter gap than Anthropic’s table suggests. The practical takeaway that keeps recurring: codebase-resolution and multi-file debugging favour Fable 5; the terminal-and-Codex loop favours GPT-5.5. Test both on your real pipeline.
Edge: Fable 5 on raw coding capability; GPT-5.5 on the Codex workflow.
Round 3: Reasoning and knowledge work
Fable 5 holds a consistent advantage on the published reasoning aggregates — for example, it posts a much higher score on Humanity’s Last Exam (around 59% versus GPT-5.5’s ~41%) and edges ahead on graduate-level science reasoning (GPQA Diamond) and maths, with better calibration (fewer confident wrong answers). On the composite Artificial Analysis Intelligence Index, Fable 5 leads roughly 65 to 60.
GPT-5.5 remains an excellent reasoner and is often sharper on clean, self-contained algorithmic problems — but on the aggregate numbers, this round goes to Fable 5.
Edge: Fable 5.
Round 4: Long context
Both models reach a 1-million-token context window, but parity on window size isn’t parity on retrieval. GPT-5.5 has strong, well-documented long-context performance (around 74% on the MRCR retrieval test at the 512K–1M range, where its predecessor collapsed). Anthropic hasn’t published directly comparable retrieval scores for Fable 5, so for workloads that genuinely depend on million-token retrieval — legal review, full-codebase analysis, large research synthesis — GPT-5.5 currently has the stronger evidence base.
Edge: GPT-5.5 (on current published evidence).
Round 5: Multimodal and voice
GPT-5.5 goes wide on input and output formats — it’s the stronger pick for voice-first interfaces and image-heavy products, reflecting OpenAI’s compounding investment in multimodality. Fable 5 performs well on certain visual reasoning benchmarks, but its modality range is narrower; it’s not the model you reach for when the core task is voice or rich image generation.
Edge: GPT-5.5.
Round 6: Real-world agentic workflows (the upset)
Here’s the result that surprised a lot of people. On UC Berkeley’s Agents’ Last Exam (ALE) — a demanding new benchmark built with 300+ domain experts to test economically valuable, multi-step professional work — GPT-5.5, running through the Codex framework, took the top spot with a 24.0% pass rate, beating Fable 5’s 22.0% (which placed third).
The explanation is instructive: ALE rewards orchestration and multi-tool use, not just code generation, and GPT-5.5’s mature Codex harness gives it an agentic layer that turns a strong model into an effective agent. Pure coding may favour Fable 5, but complex autonomous workflows currently favour GPT-5.5 plus Codex. It’s worth noting that instruction-following is genuinely contested between the two — some testers find Fable 5 better at holding multi-part constraints, while ALE found the opposite — and that even the winner passes only a quarter of the tasks. The frontier is still early.
Edge: GPT-5.5, on ecosystem maturity.
The Fable 5 asterisks
Beyond the suspension, two practical quirks matter for anyone planning to deploy Fable 5 when it returns. First, it runs a two-stage classifier system that can silently reroute a flagged request to the less-capable Opus 4.8 — so you don’t always get the model you’re paying for on sensitive topics. Second, the 30-day data-retention requirement can be a non-starter for regulated industries. Neither is a dealbreaker for most users, but both deserve more than a footnote.
The verdict
On paper, Claude Fable 5 is the capability leader — the stronger model for complex, multi-file coding and for reasoning-heavy knowledge work, assuming you can absorb the higher cost and the classifier friction. GPT-5.5 is the value-and-ecosystem play: half the price, broader multimodality, better-documented long-context retrieval, a mature Codex workflow, the winner on the ALE real-world benchmark — and, right now, the one you can actually use.
For teams choosing today, that last point is decisive: with Fable 5 offline, GPT-5.5 (or Claude Opus 4.8) is the practical choice. If and when Fable 5 returns, the calculus shifts back toward task-by-task routing — reach for Fable 5 where its coding and reasoning edge pays for itself, and for GPT-5.5 where cost, multimodality, long-context retrieval, or the Codex ecosystem matter more. As with the broader model-release race, no single model wins every row — the smart move is routing, not standardising, and testing both on the work you actually do.
Last updated: 21 June 2026. Benchmark figures are drawn from Anthropic’s published comparison table and third-party testing, and vary by test harness; treat them as directional. Claude Fable 5 access was suspended on 12 June 2026 and remained offline at the time of writing.