When Claude Fable 5 launched on June 9, 2026, it didn't just join the race — it lapped the field. But how meaningful is that lead, and where do GPT-5.5 and Gemini 3.1 Pro actually compete? This comparison goes beyond the headline numbers to give you a clear picture of what each model is best at — and what they cost.
Note: As of June 13, 2026, Fable 5 is no longer accessible due to a US export control order. This comparison reflects the performance landscape as it stood at launch and remains relevant for understanding the state of frontier AI.
Head-to-Head Benchmarks
The table below uses Vellum's independent benchmark analysis, which normalizes scores across models tested under identical conditions. These are the most reliable cross-model numbers available.
| Benchmark | Claude Fable 5 | GPT-5.5 | Gemini 3.1 Pro | Best |
|---|---|---|---|---|
| SWE-Bench Pro (Agentic Code) | 80.3% | 58.6% | 54.2% | Fable 5 (+21.7) |
| FrontierCode Diamond (Competitive Programming) | 29.3% | 5.7% | N/A | Fable 5 (+23.6) |
| GDP.pdf (Vision, No Tools) | 29.8% | 24.9% | 16.7% | Fable 5 (+4.9) |
| GPQA Diamond (Graduate-Level Q&A) | 92.1% | 94.3% | 94.3% | GPT-5.5 / Gemini 3.1 |
| MMLU-Pro (Broad Knowledge) | 89.7% | 91.2% | 92.8% | Gemini 3.1 Pro |
Two things jump out immediately. First, Fable 5's lead in coding benchmarks is enormous — not just a few points, but 20+ percentage points over the next-best model. This is a genuinely unprecedented gap at the frontier. Second, on knowledge and reasoning benchmarks, the three models are much closer, with Gemini 3.1 Pro actually leading on MMLU-Pro.
Pricing Comparison
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window |
|---|---|---|---|
| Claude Fable 5 | $10 | $50 | 2M tokens |
| Claude Opus 4.8 | $5 | $25 | 2M tokens |
| GPT-5.5 | $15 | $60 | 1M tokens |
| Gemini 3.1 Pro | $7 | $28 | 2M tokens |
Fable 5 sits in the middle of the pack on price — more expensive than Gemini 3.1 Pro and Opus 4.8, but cheaper than GPT-5.5. Given the performance gap, Fable 5 offers by far the best price-to-performance ratio in coding tasks. For general knowledge work where the models are closer in quality, Gemini 3.1 Pro at $7/$28 is the budget winner.
Model-by-Model Deep Dive
Claude Fable 5 — The Coding King
Strengths: Software engineering, autonomous task completion, long-horizon reasoning, vision-based interaction. The model's ability to sustain coherent work over millions of tokens is unmatched. Stripe's 50-million-line migration and the Pokémon playthrough are real-world validations, not just benchmark numbers.
Weaknesses: Pure knowledge recall (MMLU-Pro, GPQA) is competitive but not leading. The safety classifier creates an unpredictable user experience — you might be talking to Opus 4.8 without knowing it. And, of course, it's currently unavailable.
Best for: Developers, engineering teams, anyone doing complex multi-step work that requires sustained reasoning. If your workflow involves debugging across multiple files, refactoring large codebases, or autonomous research, nothing else comes close.
GPT-5.5 — The Generalist
Strengths: Broad competence, strong at graduate-level reasoning (GPQA Diamond 94.3%), excellent tool-use integration, massive ecosystem (ChatGPT, Copilot integration, plugin marketplace). GPT-5.5 is arguably the safest choice if you need a model that does everything reasonably well and you value ecosystem maturity.
Weaknesses: Coding lags badly behind Fable 5 — 58.6% vs. 80.3% on SWE-Bench Pro is not a small gap. On FrontierCode Diamond (the hardest programming problems), GPT-5.5 scores just 5.7%. For serious software engineering, it's simply not in the same league.
Best for: General-purpose use, content creation, business analytics, and workflows that need reliable integration with existing tools. If you're not doing hardcore coding, GPT-5.5 is still excellent.
Gemini 3.1 Pro — The Value Play
Strengths: Strongest knowledge benchmarks (MMLU-Pro 92.8%), cheapest pricing ($7/$28), best context window value, deep Google ecosystem integration (Gmail, Drive, Search grounding). Gemini 3.1 Pro is the model you choose when you want strong performance without the premium price tag.
Weaknesses: Coding performance is the weakest of the three at 54.2% on SWE-Bench Pro. Vision benchmarks (GDP.pdf at 16.7%) lag significantly. For software engineering tasks, Gemini 3.1 Pro is not competitive with Fable 5 or even GPT-5.5.
Best for: Knowledge workers, researchers, anyone embedded in the Google ecosystem, and cost-sensitive deployments. If your work is more about understanding and synthesizing information than writing complex code, Gemini 3.1 Pro is the practical choice.
Which Model Should You Use?
| Use Case | Winner | Reason |
|---|---|---|
| Complex software engineering | Fable 5 | 80.3% SWE-Bench Pro, 5x+ GPT-5.5 on FrontierCode |
| Code review & debugging | Fable 5 | Stripe 50M-line migration in 1 day |
| Academic reasoning | GPT-5.5 | 94.3% GPQA Diamond, strong across all knowledge tests |
| Broad knowledge tasks | Gemini 3.1 Pro | 92.8% MMLU-Pro, Google Search grounding |
| Budget-constrained deployment | Gemini 3.1 Pro | $7/$28 — cheapest frontier model by a wide margin |
| Autonomous long-horizon tasks | Fable 5 | 9-hour zero-intervention research (Mollick test) |
| Vision-heavy workflows | Fable 5 | 29.8% GDP.pdf, Pokémon playthrough proof |
| Ecosystem & tooling | GPT-5.5 | ChatGPT ecosystem, plugin marketplace, broad integrations |
The Bottom Line
Before the ban, the choice was clear for anyone doing serious software engineering: Fable 5 was in a league of its own. The 20+ point gap on SWE-Bench Pro and the real-world Stripe validation made it the unambiguous leader for coding work. GPT-5.5 remained the best generalist, and Gemini 3.1 Pro was the smart budget pick.
Now, with Fable 5 offline, the landscape has shifted. GPT-5.5 is the de facto strongest available model for coding, though the gap between it and the now-inaccessible Fable 5 is stark. The question becomes: will Anthropic get Fable 5 back online? And if so, when — and with what restrictions?
One thing the comparison makes clear: the AI frontier moves fast, but regulatory action can move faster. For teams building on frontier models, the lesson of June 2026 is that model availability risk is now a first-class engineering concern.