Claude Fable 5 vs GPT-5.5: Benchmarks, Pricing, and When Each Wins

Anthropic shipped Claude Fable 5 on June 9, 2026, and the launch framing was not modest. Fable 5 sits above the Opus tier - a new class Anthropic calls Mythos - and the company says it is state-of-the-art on nearly every benchmark it tested. It is also priced at $10/$50 per million tokens, exactly double what GPT-5.5 costs on the API.

That price gap is the central question for developers right now. The benchmarks are real, but benchmark leads do not always translate into per-task value. This post breaks down where each model wins, where the gap closes, and how to pick one given your workload.

Last updated: June 10, 2026

---

Why This Comparison Matters Now

Both models are genuinely new capability tiers - not iterative improvements. Fable 5 is the first Mythos-class model Anthropic has cleared for general use, carrying safeguards that fall back to Opus 4.8 on a small fraction of cybersecurity and biology queries. GPT-5.5 is OpenAI's strongest agentic coding model to date, with a matching per-token latency to GPT-5.4 despite being significantly more capable.

The 22-point gap on SWE-Bench Pro (80.3% vs 58.6%) is large enough that it cannot be handwaved away. But GPT-5.5 runs up to 4x more token-efficient on the same coding tasks, which changes the math considerably once you run real workloads at scale.

The practical decision comes down to: are you optimizing for raw capability on hard, long-horizon problems, or for cost-per-successful-task across a high-volume production pipeline?

---

Benchmark Head-to-Head

Here is the benchmark data, sourced from Anthropic's launch post and OpenAI's release page.

SWE-Bench Pro is the most-cited number. It tests whether an agent can resolve real GitHub issues end-to-end in a single pass across a held-out set of repositories. The 80.3% vs 58.6% gap is measured on the same benchmark at the same time by Anthropic's own testing - Vellum's independent breakdown corroborates these figures.

FrontierCode Diamond is Cognition's hardest coding eval - 50 tasks requiring production-quality diffs that pass automated rubrics. Fable 5 at 29.3% is more than 4x GPT-5.5's 6.3% here. The context: the full Diamond set remains unsaturated at the frontier. Cognition's data also notes that GPT-5.5 uses up to 4x fewer tokens than Opus 4.8 on the same tasks, which is an important efficiency signal even when the absolute scores differ.

Terminal-Bench 2.0 is OpenAI's own CLI-workflow benchmark - planning, iteration, and tool coordination in a terminal environment. GPT-5.5 posts 82.7%. Anthropic has not published Fable 5 results on this specific benchmark, so direct comparison here is not possible.

GDPval is notable because both models score comparably (84.9%) on this knowledge-work index across 44 occupations, suggesting the gap narrows significantly on structured professional tasks.

---

Pricing Math: Is the 2x Premium Justified?

Note that GPT-5.5 and Opus 4.8 have the same input price but different output pricing ($30 vs $25 per million tokens). Fable 5 is double GPT-5.5 on input and 1.67x on output.

The relevant question is not token price in isolation but cost-per-successful-task. If Fable 5 completes a coding task in one pass that GPT-5.5 requires two passes for, the effective cost converges. If GPT-5.5 uses 4x fewer tokens on a task where it succeeds - which Cognition's FrontierCode data supports - the cost advantage flips hard even at lower task success rates.

A rough framework:

• Hard, one-shot agentic work (migrations, complex refactors): Fable 5's higher single-pass rate likely beats GPT-5.5's lower price.
• High-volume, repetitive coding tasks: GPT-5.5's token efficiency and lower price per token probably win once you account for retry rates.
• Knowledge work and document analysis: Pricing difference matters more since benchmark scores are closer. GPT-5.5 is likely the better default.

Anthropic is also running a time-limited window: Fable 5 is included in Pro, Max, Team, and Enterprise subscription plans at no extra cost through June 22, 2026 only. After June 23, using it on those plans requires usage credits until capacity scales. API access via claude-fable-5 is fully available today regardless.

---

Real-World Task Matrix

Stripe's early testing - cited in Anthropic's launch post - is the most striking real-world data point: Fable 5 completed a codebase-wide migration on a 50-million-line Ruby codebase in a day that would have taken a team two months by hand. That is the use case where the premium makes sense. The longer and more complex the task, the more Fable 5's lead compounds.

For vision work, Fable 5 demonstrated reconstructing a web app's full source from screenshots, and completing Pokémon FireRed start-to-finish using only raw game screenshots with no maps or guides - something earlier Claude models needed a complex harness to attempt at all. The GDP.pdf lead (29.8% vs 24.9%) is modest in absolute terms, but it reflects a real capability gap on unstructured document reasoning.

---

When GPT-5.5 Wins

Token efficiency is GPT-5.5's clearest structural advantage. On Cognition's FrontierCode Diamond, GPT-5.5 uses up to 4x fewer tokens than Opus 4.8 on the tasks where it succeeds. Applied to high-volume pipelines, that efficiency compounds directly into cost savings even before you account for the lower base price.

Latency parity with GPT-5.4. OpenAI specifically built GPT-5.5 to serve at the same per-token latency as its predecessor despite being a significantly more capable model. This matters for interactive applications where response time is user-visible - coding assistants, chat interfaces, anything where the user waits.

Flat subscription availability. GPT-5.5 is fully available for Plus, Pro, Business, and Enterprise ChatGPT and Codex subscribers with no June 22 cutoff. Fable 5's included-in-subscription window closes June 23.

Knowledge work at lower cost. Where the models are comparably capable - GDPval, document reasoning, structured analysis - there is no reason to pay Fable 5 prices. GPT-5.5 covers those workloads well at a lower rate.

Structured, repeatable tasks at scale. The more your workload resembles a pipeline with predictable inputs rather than open-ended agentic loops, the more GPT-5.5's token efficiency and pricing advantage matter.

---

When Fable 5 Wins

The Stripe-style migration. Month-long agentic tasks on large codebases are exactly the scenario where Fable 5 compounds. Higher single-pass success rates on SWE-Bench Pro mean fewer retries, which closes the apparent cost gap. On the hardest problems where GPT-5.5's FrontierCode Diamond score is 6.3% vs Fable 5's 29.3%, you may simply not be able to complete the task with GPT-5.5 at any price.

Long-context autonomy with memory. Anthropic's internal Slay the Spire test is illustrative: giving Fable 5 persistent file-based memory improved its performance three times more than it did for Opus 4.8, and it reached the game's final act three times as often. That asymmetric benefit of memory suggests Fable 5 is meaningfully better at planning across a long task horizon, not just at individual steps.

Vision-intensive agentic work. The document reasoning and screenshot-based reconstruction capabilities are ahead of GPT-5.5 in current benchmarks. For computer-use agents, UI automation, or document-processing pipelines, the gap is real.

Tasks where failure is costly. If a failed agent run burns significant compute, developer time, or downstream data, the higher single-pass success rate on hard tasks justifies Fable 5's premium regardless of per-token price.

---

Migration Notes

API identifiers have changed. The Fable 5 model ID is claude-fable-5. Note that Fable 5 carries one breaking change not present in Opus 4.7 or 4.8: passing thinking: {type: "disabled"} explicitly returns a 400 error - you must omit the thinking parameter entirely rather than disabling it. Review the model migration guide before updating existing integrations.

Fable 5 subscription window closes June 22. If you are on a Claude Pro, Max, Team, or seat-based Enterprise plan, Fable 5 is free to use through June 22 only. After that date, usage requires credits until Anthropic scales capacity. If you are building an integration now, budget for API usage costs or confirm your plan tier. The API via claude-fable-5 is unaffected - that access path continues regardless.

GPT-5.5 Pro is a separate tier. OpenAI also announced gpt-5.5-pro at $30/$180 per million tokens - substantially more expensive than either Fable 5 or standard GPT-5.5. That tier targets maximum accuracy on the most demanding tasks, and is not the baseline GPT-5.5 most developers will use. The comparison in this post is between standard Fable 5 and standard GPT-5.5.

Fable 5's safeguard fallback affects a small but real slice of sessions. On cybersecurity and biology queries, Fable 5 routes silently to Opus 4.8. Anthropic says this triggers in under 5% of sessions. If your application touches those domains - security tooling, bioinformatics, dual-use research - verify whether the fallback will affect your use case before depending on Fable 5 capabilities in production.

---

Verdict by Use Case

Daily coding assistance and PR review: GPT-5.5. Token efficiency, lower price, and comparable knowledge-work scores make it the economical default. See the GPT-5.5 developer guide for integration patterns.

Autonomous agents on hard codebases: Fable 5. The 22-point SWE-Bench Pro gap and the FrontierCode Diamond lead (29.3% vs 6.3%) are decisive for tasks where a single failed run is expensive. The Stripe migration example is the canonical case.

Enterprise batch processing (document extraction, analysis pipelines, knowledge work at scale): GPT-5.5 unless you specifically need Fable 5's vision or document reasoning edge. The GDPval tie at 84.9% suggests both models cover structured knowledge work well.

Vision and computer use: Fable 5 for now. The GDP.pdf lead and the screenshot reconstruction capability give it a meaningful edge on unstructured visual reasoning. Review the AI coding tools comparison matrix for how both fit alongside other coding tools in a multi-model workflow.

Both models represent genuine capability leaps. The decision is almost never about which is objectively better - it is about where the 2x price difference is earned back by higher task success rates on your specific workload.

---

Official Sources