GPT-5.6 Sol vs Claude Fable 5: Agentic Coding, Cost & Capability (2026)
GPT-5.6 Sol vs Claude Fable 5, decided on the numbers both vendors published. Sol leads the AA Coding Agent Index (80 vs 77.2) at ~40% less per task and half the list price; Fable 5 still leads raw intelligence, SWE-Bench Pro and analytical depth. Route by workload.
Both companies' own benchmark tables agree on the split. Claude Fable 5 (max) leads the Artificial Analysis Intelligence Index (by a single point), SWE-Bench Pro, GDPval-AA Elo, HealthBench Professional, and the AA-Briefcase knowledge-work benchmark — its Rubric score of 56% against Sol's 42%, and an Analytical Quality Elo of 1764 versus 1592, show it is meaningfully deeper on hard, quality-graded work. GPT-5.6 Sol (max) leads exactly one index — the AA Coding Agent Index, at 80 versus Fable's 77.2 in Claude Code — but it wins it while costing roughly 40% less per coding task, about one third the cost per Intelligence Index task, and using fewer output tokens than Claude Opus 4.8. So the honest question is not 'which is smarter' (Fable, narrowly) but 'is the last point of intelligence worth twice the list price for your agentic workload.' For high-volume, unattended coding where per-task cost compounds across thousands of runs, Sol's economics win outright. For the hardest analytical and architecture work where a rubric grader rewards depth over throughput, Fable 5 still earns its premium. Route by workload, not by launch-week headline — and factor in Fable's billing change — now live as of July 12, 2026 — if cost is already tight.
Detailed Comparison
A side-by-side analysis of key factors to help you make the right choice.
| Factor | GPT-5.6 SolRecommended | Claude Fable 5 | Winner |
|---|---|---|---|
| Agentic coding throughput (AA Coding Agent Index) | Leads: 80 in Codex, top score in all three sub-evals (DeepSWE, Terminal-Bench v2, SWE-Atlas-QnA) | 77.2 in Claude Code (max reasoning) | |
| Peak raw intelligence (AA Intelligence Index v4.1) | 58.9 (Sol max) — one point behind | 59.9 (max) — highest of any model | |
| Cost per solved task | ~1/3 the cost per Intelligence Index task ($1.04); ~40% cheaper per coding task | Baseline — highest cost per task of the frontier models | |
| Analytical & knowledge-work depth (AA-Briefcase) | Rubric 42%; Analytical Quality Elo 1592; highest Presentation Elo of any model | Leads: Rubric 56%; Analytical Quality Elo 1764 | |
| Output token efficiency | ~15k output tokens per Intelligence task; fewer than Opus 4.8 (max) while more intelligent | Higher token use per task at the top of the intelligence range | |
| Reliability & hallucination resistance | AA-Omniscience: small accuracy gain over GPT-5.5 but a higher hallucination rate | Higher rubric-graded analytical rigor on complex reasoning | |
| List price (per million input/output tokens) | $5 / $30, plus new cache-write pricing (1.25x input) and a 90% cache-read discount | $10 / $50 — roughly double the input and output rates | |
| Economically valuable task completion (GDPval-AA v2) | Elo 1,747.8 — 'similar ability to complete economically valuable tasks' | Elo 1,759.6 — marginally ahead | |
| Total Score | 4/ 8 | 3/ 8 | 1 ties |
Key Statistics
Real data from verified industry sources to support your decision.
Artificial Analysis
Artificial Analysis
Artificial Analysis
Artificial Analysis / Anthropic
Artificial Analysis
Artificial Analysis
DigitalApplied (citing OpenAI GA page)
All statistics come from verified third-party sources. Source, year, and direct link are shown on each metric.
When to Choose Each Option
Clear guidance based on your specific situation and needs.
Choose GPT-5.6 Sol when...
- You run high-volume, unattended agentic coding where per-task cost compounds across thousands of runs
- Your workflow is built on Codex, or you want frontier-adjacent intelligence at roughly one third the cost
- Output-token and latency budgets matter, and you want the leading coding-agent score per dollar
- You want a range of reasoning-effort tiers (Sol / Terra / Luna) to tune cost against capability
Choose Claude Fable 5 when...
- You need the single highest raw intelligence and the deepest analytical, rubric-graded output
- Your work is heavy on SWE-Bench Pro-style engineering, GDPval tasks, or complex reasoning where hallucination is costly
- You are already invested in Claude Code and the analytical-quality gap justifies the higher price
- Output quality and knowledge-work depth matter more to you than cost per task or raw throughput
Our Recommendation
Both companies' own benchmark tables agree on the split. Claude Fable 5 (max) leads the Artificial Analysis Intelligence Index (by a single point), SWE-Bench Pro, GDPval-AA Elo, HealthBench Professional, and the AA-Briefcase knowledge-work benchmark — its Rubric score of 56% against Sol's 42%, and an Analytical Quality Elo of 1764 versus 1592, show it is meaningfully deeper on hard, quality-graded work. GPT-5.6 Sol (max) leads exactly one index — the AA Coding Agent Index, at 80 versus Fable's 77.2 in Claude Code — but it wins it while costing roughly 40% less per coding task, about one third the cost per Intelligence Index task, and using fewer output tokens than Claude Opus 4.8. So the honest question is not 'which is smarter' (Fable, narrowly) but 'is the last point of intelligence worth twice the list price for your agentic workload.' For high-volume, unattended coding where per-task cost compounds across thousands of runs, Sol's economics win outright. For the hardest analytical and architecture work where a rubric grader rewards depth over throughput, Fable 5 still earns its premium. Route by workload, not by launch-week headline — and factor in Fable's billing change — now live as of July 12, 2026 — if cost is already tight.
Frequently Asked Questions
Common questions about this comparison answered.
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