Claude Fable 5 cost & VRAM calculator
What does Claude Fable 5 cost to run for your workload, and can you run it on your own hardware? Set your workload below - we compute per-provider API cost live and tell you honestly whether local hardware can run it.
This is where owning stops making sense
Claude Fable 5 is a large-billion-parameter model. No rig an individual can buy runs it - so unlike a workstation GPU model, there's no break-even to compute. The honest answer for nearly everyone is the per-provider API cost below.
Proprietary dense model from Anthropic, sibling to Mythos 5. Parameter count is undisclosed. Native 1M-token context, 128K max output, knowledge cutoff January 2026. Adaptive thinking is always on and cannot be disabled; raw chain-of-thought is never returned. Safety classifiers are included and can decline requests. Pricing: $10/1M input, $50/1M output. Sampling constraints: temperature must be 1.0 or unset; top_p must be >= 0.99 and < 1.0, or unset. Agents on Rails benchmark (Aug 2026, Le Mans round). 90.5% accuracy on 63 runs under default settings - tied with Kimi K3, with the authors noting it could reach ~95% if it had not refused one task worded like a penetration-test report. The refusal behavior is a concrete data point if you are pointing agents at security-related Rails work.
Your workload
API cost for your workload
| Provider | Rate ($/1M) | Monthly cost |
|---|---|---|
| Anthropic may be stale | $10.00 in · $50.00 out | $0.24 cheapest |
Monthly cost is an estimate from list prices and your workload - verify against the provider before committing. Cached fraction applies the cache rate to that share of input.
Can you run it locally?
Claude Fable 5 has no published quantization that fits a rig one person can buy, so there's no local-hardware recommendation and no break-even to compute. The honest answer is the per-provider API cost above.
Proprietary dense model from Anthropic, sibling to Mythos 5. Parameter count is undisclosed. Native 1M-token context, 128K max output, knowledge cutoff January 2026. Adaptive thinking is always on and cannot be disabled; raw chain-of-thought is never returned. Safety classifiers are included and can decline requests. Pricing: $10/1M input, $50/1M output. Sampling constraints: temperature must be 1.0 or unset; top_p must be >= 0.99 and < 1.0, or unset. Agents on Rails benchmark (Aug 2026, Le Mans round). 90.5% accuracy on 63 runs under default settings - tied with Kimi K3, with the authors noting it could reach ~95% if it had not refused one task worded like a penetration-test report. The refusal behavior is a concrete data point if you are pointing agents at security-related Rails work.
See the model card for the full architecture notes and any cloud subscription plans.