Claude Fable 5 vs GPT-5.5: Benchmarks, Pricing, and Which to Choose

If you're deciding between Claude Fable 5 and GPT-5.5 for a production workflow, the benchmark tables will tell you a clear story. On paper, Fable 5 is the stronger model by a wide margin on coding and reasoning. But it also costs twice as much per output token, has a classifier system that can silently reroute your request to a weaker model, and imposes a 30-day data retention requirement that blocks some enterprise customers entirely.

In this article, I'll compare Fable 5 and GPT-5.5 across five dimensions: coding and agentic performance, long-context work, safety classifiers and access friction, knowledge work and reasoning, and pricing. You can also check out our standalone guides to Claude Fable 5 and GPT-5.5 for deeper coverage of each model individually.

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What Is Claude Fable 5?

Claude Fable 5 is Anthropic's first Mythos-class model available for general use, launched on June 9, 2026. Mythos is a new capability tier that sits above Opus in Anthropic's model hierarchy. Fable 5 is the same underlying model as Claude Mythos 5, but with safety classifiers active that route certain sensitive queries to Claude Opus 4.8 instead. The name distinction matters: Fable is the publicly accessible version; Mythos is the unrestricted version available only to Project Glasswing partners.

Anthropic positions Fable 5 as state-of-the-art on nearly all tested benchmarks, with particular strength in software engineering, knowledge work, vision, and long-running agentic tasks. The longer and more complex the task, the larger its lead over previous Claude models. Stripe reported that Fable 5 compressed months of engineering work into days on a 50-million-line Ruby codebase migration.

For more on Fable 5's capabilities and benchmark breakdown, see our Claude Fable 5 guide. We also cover the restricted Mythos 5 variant in our Claude Mythos 5 article.

What Is GPT-5.5?

GPT-5.5 is OpenAI's April 2026 model release, described as the company's strongest agentic coding model to date. OpenAI also released a GPT-5.5 Pro variant for higher-accuracy work. The model was co-designed for and served on NVIDIA GB200 and GB300 NVL72 systems, and OpenAI says it matches GPT-5.4 per-token latency in real-world serving while performing at a meaningfully higher intelligence level.

The headline architectural story for GPT-5.5 is long-context reliability. GPT-5.4 collapsed past roughly 128K tokens on the MRCR benchmark; GPT-5.5 holds to 512K-1M tokens (74.0% on MRCR v2 at that range, versus GPT-5.4's 36.6%). That's a qualitative change in what the model can be used for, not a marginal benchmark gain.

For a full breakdown of GPT-5.5's benchmarks and our hands-on findings, see our GPT-5.5 guide. We also compared it directly against Claude Opus 4.8 in our Claude Opus 4.8 vs GPT-5.5 piece.

Claude Fable 5 vs GPT-5.5: Head-to-Head Comparison

Here's a quick summary of where each model stands before we get into the details.

Coding and agentic performance

This is where the gap between the two models is largest and most decision-relevant. On SWE-Bench Pro, the benchmark for real-world GitHub issue resolution, Fable 5 scores 80.3% versus GPT-5.5's 58.6%. That's a 22-point gap. For context, Claude Opus 4.7 already beat GPT-5.5 on this benchmark at 64.3%, so GPT-5.5 was already trailing on repository-level coding before Fable 5 arrived.

On Cognition's FrontierCode evaluation, which tests whether models can pass difficult coding tasks while meeting production codebase standards, Fable 5 scores highest among frontier models even at medium effort. Cursor's CEO, Michael Truell, described it as the highest-scoring model on FrontierBench, excelling at long-horizon reasoning and generalizing to unfamiliar tools out of the box.

Fable 5 also seems to lead Terminal-Bench 2.1 with a reported score of 88.0%*, ahead of GPT-5.5 at 83.4%. The asterisk indicates that the number has to be taken with a grain of salt because of a discrepancy between Fable 5 and Mythos 5. Wherever that's the case, Fable is the lower-performing of the two, so I would assume that Fable 5 ties with GPT-5.5 or leads by a small margin.

GPT-5.5 is still the best choice for terminal-heavy DevOps and shell automation, but the SWE-Bench Pro gap is a real signal. If your primary use case is repository-level engineering, Fable 5 is the clear choice on capability alone. The question is whether the 2x output token cost and classifier friction are worth it for your specific workload.

Long-context performance

This is GPT-5.5's genuine differentiator, and it's worth treating seriously. GPT-5.4 fell apart past roughly 128K tokens on the MRCR v2 benchmark. GPT-5.5 doesn't. At 512K-1M tokens, GPT-5.5 scores 74.0% on MRCR v2, compared to GPT-5.4's 36.6% at the same range. That's not a marginal improvement; it's a different capability class.

Anthropic claims Fable 5 stays focused across millions of tokens in long-running tasks and improves its outputs using its own notes. The Slay the Spire memory test showed that file-based persistent memory improved Fable 5's performance three times more than it improved Opus 4.8's. But Anthropic has not published MRCR-style scores for Fable 5 at the 512K-1M range, so a direct apples-to-apples comparison isn't possible here.

For users running million-token contexts, such as legal document review, large codebase analysis, or scientific literature synthesis, GPT-5.5's published long-context scores are the stronger evidence base. In our own testing of GPT-5.5, we found it passed a 300K-token needle test and that MRCR scores held past 256K, where GPT-5.4 had collapsed. Fable 5 may be equally strong here, but the data isn't published in a comparable format.

Safety classifiers and access friction

This is the most underreported practitioner issue with Fable 5, and it deserves more than a footnote. Fable 5 runs a two-stage classifier system: a probe monitors internal activations across all traffic, and flagged requests are escalated to a separate trained LLM classifier that makes the final call. When a request is blocked, it gets rerouted to Claude Opus 4.8, and the user is notified which model handled the query.

Anthropic says the classifiers trigger in less than 5% of sessions on average. Three domains are covered:

• Cybersecurity: Exploit development, offensive cyber tasks, and agentic hacking workflows are blocked. Fable 5 scored 0.0% across all four cyber benchmarks when classifiers were active, down from the underlying Mythos model's 88.4% on Firefox exploit development.
• Biology and chemistry: Most requests in this domain fall back to Opus 4.8. Anthropic's own evaluations showed the underlying model approaching expert-level performance on adeno-associated virus design tasks, which is why the coverage is broad.
• Distillation: Requests flagged as attempts to extract Claude's capabilities for training competing models are rerouted.

The fallback mechanic is not just a capability concern; it's a reliability concern for agentic pipelines. When Fable 5 routes to Opus 4.8, you're billed at Opus 4.8 rates, but you're also getting a different (still very good!) model mid-task. For a pipeline that expects Fable 5's reasoning depth throughout, a silent mid-session switch to Opus 4.8 can break assumptions about output quality.

GPT-5.5 has its own cyber safeguards, described as stricter classifiers for potential cyber risk. But there is no silent fallback to a weaker model. OpenAI's approach is tiered trusted access: verified defenders can apply at chatgpt.com/cyber for expanded access with fewer restrictions. That path is more accessible than Anthropic's Project Glasswing, which is still limited to a small set of approved partners.

There's one more blocker worth naming directly. Fable 5 and Mythos 5 are classified as Covered Models, which means Anthropic requires 30-day data retention for all traffic, even for enterprise customers previously on zero-retention plans. Anthropic states that the data is not used for training, but the retention requirement itself is a hard blocker for regulated industries. Some enterprise customers cannot use Fable 5 at all because of this policy.

Knowledge work and reasoning

Both models are strong here, and the differences are narrower than in coding. Fable 5 leads on Hebbia's Finance Benchmark for senior-level reasoning, scoring highest of any model on document-based reasoning, chart interpretation, and problem solving. IMC reported that Fable 5 exceeded their trading-analysis evaluations across the board, including root-cause analysis and expected-value analysis.

GPT-5.5 leads on FrontierMath Tier 4 at 35.4%, ahead of Fable 5's published scores. On GDPval, which tests agents across 44 occupations, GPT-5.5 scores 84.9%. On Humanity's Last Exam with tools, Fable 5 leads at 64.5% versus GPT-5.5's 52.2%, a meaningful gap for multidisciplinary reasoning tasks.

Pricing and availability

The pricing gap is real and compounds at scale. Fable 5 is priced at $10 per million input tokens and $50 per million output tokens. GPT-5.5 is $5 per million input tokens and $30 per million output tokens. For high-volume workloads, that 100%/67% increase adds up quickly.

Subscription access adds another wrinkle for Fable 5. Pro, Max, Team, and Enterprise subscribers had free access until June 22. After that date, using Fable 5 requires usage credits on top of the existing subscription. Anthropic says it intends to restore Fable 5 as a standard subscription feature when capacity allows, but there's no firm timeline. GPT-5.5 rolled out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex on day one, with API access following shortly after.

One pricing nuance worth knowing: when a Fable 5 query falls back to Opus 4.8 due to the classifiers, you're billed at Opus 4.8 rates ($5 input / $25 output), not Fable 5 rates.

When to Choose Claude Fable 5 vs GPT-5.5

The decision comes down to three variables: how much the SWE-Bench Pro gap matters for your work, whether your domain triggers Fable 5's classifiers, and whether you need reliable performance past 256K tokens.

Choose Claude Fable 5 if...

• Your primary use case is repository-level software engineering, and the 22-point SWE-Bench Pro gap justifies the 2x output token cost.
• Your work is not adjacent to cybersecurity, biology, or chemistry domains, so the classifiers are unlikely to trigger in your sessions.
• You need the highest ceiling on complex analytical tasks, including finance benchmarks and multidisciplinary reasoning, where Fable 5 leads by double digits.
• You're on the API and can absorb $50 per million output tokens for the capability gain.

Choose GPT-5.5 if...

• You're building in security-adjacent domains and need a model that won't silently reroute your requests mid-pipeline.
• Your enterprise data policy requires zero retention, which Fable 5's Covered Model status makes impossible.
• You need predictable API access without a subscription cliff or usage credit system on top of your plan.
• Cost efficiency matters, and the $30 vs $50 output token gap is meaningful at your usage volume.

Final Thoughts

Fable 5 is the more capable model on the benchmarks that matter most. The SWE-Bench Pro gap (80.3% vs 58.6%) is not noise, and the Humanity's Last Exam lead (64.5% vs 52.2% with tools) reflects a genuine difference in reasoning depth. If raw capability is the only variable, Fable 5 wins.

But the asterisk on Fable 5's scores is real. Those numbers reflect the underlying Mythos model. Fable 5 is Mythos with classifiers on top, and for cybersecurity, biomedical, and certain dual-use queries, you get Opus 4.8 instead. For agentic pipelines, that's not just a capability concern; it's a reliability concern. A pipeline that expects Fable 5's reasoning depth throughout can break when the model silently switches mid-task. Add the 30-day mandatory data retention requirement, and Fable 5 is simply not (yet) an option for some enterprise customers.

There's a third option worth naming. If Fable 5's price is prohibitive and GPT-5.5's long-context gains don't matter for your use case, Claude Opus 4.8 is not a consolation prize. It already beats GPT-5.5 on SWE-Bench Pro at 69.2% versus 58.6%, costs $5/$25 per million tokens, and doesn't have Fable 5's classifier friction. We cover the Opus 4.8 vs GPT-5.5 decision in detail in our Claude Opus 4.8 article.

If you want to get up to speed on working with frontier models in production, I'd recommend starting with our AI Fundamentals skill track.