Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) | GPT-5.6 Sol (high) |
|---|---|---|
| Input ($/M tokens) | $10 | $5 |
| Output ($/M tokens) | $50 | $30 |
Verdict. Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) wins the overall benchmark matchup 4–3 across 7 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the premium bracket for output-token pricing. At 1.7× the per-million-token cost, GPT-5.6 Sol (high) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-5.6 Sol (high) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) is strongest on GPQA Diamond (93%), Coding Index (76.5), IFBench (63%). GPT-5.6 Sol (high) leads on GPQA Diamond (93%), Coding Index (77.2), IFBench (69%).
Speed. Throughput is comparable — 58 tok/s vs 66 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
Provider. Anthropic and OpenAI sell to overlapping but distinct developer audiences: Anthropic tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) costs $1050.00 ($12600/year); GPT-5.6 Sol (high) costs $600.00 ($7200/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) ≈ $150.00/run, GPT-5.6 Sol (high) ≈ $85.00/run. At agent/realtime scale (200M input / 100M output per million requests): Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) ≈ $7000/run, GPT-5.6 Sol (high) ≈ $4000/run. GPT-5.6 Sol (high) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
GPT-5.6 Sol (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $11.25/M tokens vs $20.00/M for Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback).
Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) wins 4 out of 12 benchmarks compared to 3 for GPT-5.6 Sol (high). See the detailed benchmark chart above for per-category results.
GPT-5.6 Sol (high) generates tokens faster at 66 tok/s vs 58 tok/s. However, GPT-5.6 Sol (high) has lower time-to-first-token (11.26s vs 52.46s).
Choose based on your priorities: GPT-5.6 Sol (high) for lower cost, Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) for stronger benchmark performance, and GPT-5.6 Sol (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.