Claude Fable 5 is the strongest coding model available: 80.3% SWE-Bench Pro vs GPT-5.5's 58.6% — a 21.7-point gap, and 1932 vs 1769 on GDPval-AA. GPT-5.5 is cheaper at $5/$30 vs $10/$50 and is the better fit when your stack is OpenAI-native (Codex, ChatGPT, computer-use). For raw coding and reasoning quality on new work, Fable 5 is the clear pick.
AnthropicPremium
Claude Fable 5
New global #1 — 80.3% SWE-Bench Pro, the most capable model generally available.
Winner
VS
OpenAIPremium
GPT-5.5
Best OpenAI flagship for agentic coding, research, and computer-use work.
At a glance
Claude Fable 5
GPT-5.5
Input cost / 1M tokens
$$10.00/1M
$$5.00/1M
Output cost / 1M tokens
$$50.00/1M
$$30.00/1M
Context window
1M tokens
1M tokens
Speed
Deliberate
Balanced
Price tier
Premium
Premium
Benchmarks
SWE-bench (coding)
95%
—
Arena Elo
1,932
—
MMLU
94.2%
—
How they compare
Which model wins for each use case — and why.
Coding ceilingClaude Fable 5 wins
Fable 5 leads SWE-Bench Pro 80.3% vs 58.6% — the widest gap between any two frontier coding models today.
PriceGPT-5.5 wins
GPT-5.5 is cheaper on output ($5/$30 vs $10/$50) and a better value when you don't need Fable 5's ceiling.
OpenAI ecosystemGPT-5.5 wins
GPT-5.5 wins when you depend on Codex, ChatGPT rollout, OpenAI APIs, or OpenAI-native computer-use agents.
Human preferenceClaude Fable 5 wins
Fable 5 scores 1932 on GDPval-AA vs GPT-5.5's 1769 — a large head-to-head preference margin.
Context windowTie
Both support a 1M-token API context window.
Agentic codingClaude Fable 5 wins
Fable 5's Mythos-class reasoning and native parallel subagents handle longer autonomous loops more reliably.
Which should you pick?
Pick Claude Fable 5 if…
You want the highest coding and reasoning quality available (80.3% SWE-Bench Pro)
You run demanding autonomous coding agents where capability drives ROI
You're not locked into OpenAI tooling and can choose the best model
For most workflows, Claude Fable 5 is the stronger choice.
The strongest coding and reasoning model you can actually use today. 80.3% SWE-Bench Pro is an 11-point leap over Opus 4.8 — the biggest single-release jump of 2026. It costs 2× Opus 4.8, so use it for the hardest agentic and engineering work and keep Opus 4.8 or Sonnet for everyday volume.
The case for each model
What each one is genuinely good at, where it falls down, and when we would steer you away from it.
Our overall pick in this comparison. Against GPT-5.5 it costs about 42% more per token.
Anthropic's new Mythos-class flagship and the most capable coding model anyone can use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8. 1M context, 128K output, native parallel subagents. Released June 9, 2026.
Input
$10.00/1M
Output
$50.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Autonomous agents that plan, write, run, and debug across an entire codebase with minimal supervision
Whole-repo refactors and PR review where accuracy outranks latency or cost
80.3% SWE-Bench Pro — the new #1, up from Opus 4.8's 69.2% and GPT-5.5's 58.6%
1932 on GDPval-AA, ahead of Opus 4.8 (1890) and GPT-5.5 (1769)
1M-token context at standard pricing, 128K max output per request
Mythos-class capability released for general use with new cyber-risk safeguards
Where it falls down
Priced at $10/$50 per 1M tokens — double Opus 4.8 ($5/$25)
Deliberate pace; not for latency-sensitive interactive apps
Standard-use safeguards block some high-risk security workloads (use Mythos 5 with partner access)
Skip it if
You are latency- or cost-sensitive, or your tasks don't need frontier-level reasoning — Opus 4.8 at half the price is plenty.
Our verdict
The strongest coding and reasoning model you can actually use today. 80.3% SWE-Bench Pro is an 11-point leap over Opus 4.8 — the biggest single-release jump of 2026. It costs 2× Opus 4.8, so use it for the hardest agentic and engineering work and keep Opus 4.8 or Sonnet for everyday volume.
The runner-up here, but not by a wide margin. Against Claude Fable 5 it costs about 42% less per token and answers faster.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
Input
$5.00/1M
Output
$30.00/1M
Context
1M tokens
Speed
Balanced
What people actually use it for
Running multi-file implementation and debugging loops in Codex
Building agents that research, operate tools, and verify work over long tasks
Analyzing large business, scientific, or technical documents with 1M context
Where it wins
58.6% on SWE-Bench Pro, ahead of GPT-5.4 on the same public coding benchmark
82.7% on Terminal-Bench 2.0 for complex command-line workflows
1M token API context window for large-codebase and document-heavy workflows
Where it falls down
Claude Opus 4.7 leads GPT-5.5 on SWE-Bench Pro for pure coding ceiling
Premium API pricing makes it less attractive for high-volume low-risk work
Skip it if
You only care about the highest public coding benchmark score or need a cheaper high-volume model.
Our verdict
The strongest OpenAI pick for agentic coding and knowledge work. Claude Opus 4.7 still wins on the public SWE-Bench Pro coding number, but GPT-5.5 is the better OpenAI default when ecosystem, Codex, or computer-use workflows matter.
Full pricing, benchmark table and release notes on the GPT-5.5 page.
Frequently asked questions
Is Claude Fable 5 or GPT-5.5 better for coding?
Claude Fable 5, decisively. It scores 80.3% on SWE-Bench Pro vs GPT-5.5's 58.6% — a 21.7-point lead, the largest gap between any two frontier coding models currently available.
Which is cheaper, Fable 5 or GPT-5.5?
GPT-5.5 is cheaper at $5 input / $30 output per 1M tokens vs Fable 5's $10 / $50. If cost matters more than the coding ceiling, GPT-5.5 — or Claude Opus 4.8 at $5/$25 — is the better value.
When should I pick GPT-5.5 over Fable 5?
Choose GPT-5.5 when your stack is OpenAI-native: Codex integrations, ChatGPT deployment, OpenAI function-calling agents, or computer-use workflows built on OpenAI's tooling.
Do both models have a 1M-token context window?
Yes. Both Claude Fable 5 and GPT-5.5 support a 1M-token API context window, so either can handle large codebases and document sets in a single request.