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Anthropic shipped Claude Fable 5.1 on 1 September 2026, alongside Claude Mythos 5.1. Same underlying model, different safety posture: Fable 5.1 is generally available as claude-fable-5-1, while Mythos 5.1 goes to vetted US organisations through Project Glasswing.
The benchmark jumps are large and worth walking through. The line that will change more budgets is four words in the pricing section: cache reads cost 75% less.
What moved
| Model | Terminal-Bench 4.0 | Terminal-Bench-Science 0.1 |
|---|---|---|
| Claude Fable 5 | 42% | 24.7% |
| Claude Fable 5.1 | 55.8% | 52.6% |
Terminal-Bench-Science 0.1 went from 24.7% to 52.6%, and Terminal-Bench 4.0 from 42.0% to 55.8%. Doubling on a benchmark built to be hard is a genuine result, and Terminal-Bench-Science is one of the harder ones, Opus 5 sits at 29.0% on it.
The rest of the card, for completeness:
| Benchmark | Fable 5.1 |
|---|---|
| Terminal-Bench-Science 0.1 | 52.6% |
| Terminal-Bench 4.0 | 55.8% |
| CursorBench 3.2.0 | 73.4% |
| Humanity's Last Exam, no tools | 60.9% |
| Humanity's Last Exam, with tools | 65.0% |
| AutomationBench | 31.4% |
| OSWorld 2.0, strict | 41.7% |
| GDPval-AA v2 | 1853 |
A note on reading these, because it applies to every model this season: Terminal-Bench 4.0 and Terminal-Bench-Science 0.1 are separate instruments from the 2.1 that most open models still report. Lining a 55.8 up against an 88.2 from another card produces a number and no meaning. We wrote about why the version number is part of the score.
The pricing change, and why it lands harder
Base pricing is unchanged at $10 per million input tokens and $50 per million output. What changed is the cache read: $1.00 down to $0.25.
Anthropic puts the effect at roughly 25% off typical workloads and up to 45% off highly agentic ones. That second figure is the interesting one, and the reason for the spread is structural.
An agentic coding run re-sends the same repository context on every turn. Turn one writes that context to cache. Turns two through forty read it back. On a long run, cache reads are not a rounding error on the bill, they are most of the input line.
Cutting that line by three quarters is worth more to an agent that runs for an hour than any benchmark point, because it applies to every turn.
What it costs to finish a task
Take the same profile we use across these comparisons — 250K input tokens of accumulated repository context and 30K output tokens — and assume a well-cached agentic run where 90% of input comes from cache:
| Fable 5 | Fable 5.1 | |
|---|---|---|
| Input, 225K cached | $0.225 | $0.056 |
| Input, 25K fresh | $0.250 | $0.250 |
| Output, 30K | $1.500 | $1.500 |
| One attempt | $1.98 | $1.81 |
On a single attempt the saving looks modest, because output dominates. The saving grows with the number of turns that reuse the cache, which is exactly the workload Anthropic is naming: long, tool-heavy, many-turn runs where the same context is read back dozens of times.
So the practical guidance is narrow and useful. If your agent makes a handful of calls with fresh context each time, this release is a benchmark upgrade. If it runs long sessions over a stable repository, it is a real price cut, and the more agentic your setup the more of that 45% you will see.
Where it sits against the open field
Direct comparison is limited by the version problem above: the open models report Terminal-Bench 2.1 and 3.0, while Fable 5.1 reports 4.0 and Science 0.1. On the metrics that do line up:
| Metric | Claude Fable 5.1 | GLM-5.3 | GLM-5.3-Flash |
|---|---|---|---|
| Context window | 1M tokens | 1M tokens | 1M tokens |
| Native vision | Yes | No | Yes |
| License | Proprietary | Open weights (GLM-5.3 licence) | Open weights (MIT) |
| Input $/1M | $10 | $1.40 | $0.15 |
| Output $/1M | $50 | $4.40 | $0.50 |
| HLE (with tools) | 65%† | 62.5%† | 55.3%†‡ |
| AutomationBench | 31.4%† | 48.2%†‡ | 48.8%† |
| GDPval-AA v2 | 1853† | 1769† | — |
† vendor-reported (self-reported by the model's vendor, not an independent harness). ‡ vendor labels 'HLE'; tools qualifier not stated. ‡ AutomationBench v1.0.6.
Sources: MarkTechPost: Claude Fable 5.1 and Mythos 5.1, GLM-5.3 model card (Hugging Face), MarkTechPost: GLM-5.3-Flash release, Z.ai GLM-5.3-Flash docs
Two things stand out. On Humanity's Last Exam with tools, Fable 5.1's 65.0% leads GLM-5.3's 62.5%, a real but narrow margin on a genuinely hard reasoning test. On AutomationBench, GLM-5.3 reports 48.2% and GLM-5.3-Flash 48.8% against Fable 5.1's 31.4%, with the caveat that GLM's figures come from AutomationBench v1.0.6 and the harnesses differ.
The price columns are the ones to sit with. Fable 5.1 lists at $10 and $50; GLM-5.3-Flash at $0.15 and $0.50, with open MIT weights and a 1M context.
How we would use it
Fable 5.1 has earned a specific job rather than a general one. It is the model for work where the difficulty is real and the cost of a wrong answer is high: the hard debugging session, the migration nobody has done before, the reasoning task that a cheaper model has already failed twice.
Put it at the end of a routing chain rather than the front. Let a cheap open model take the first pass at the routine majority, keep a verifier between the model and the merge, and escalate to Fable 5.1 for the cases that survive. That way you pay $10 per million input only on work that has demonstrated it needs the money, and the cache discount applies to the long sessions where it compounds.
For the arithmetic behind that pattern, see cost per solved task. For the setup itself, see running open models at frontier level.
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Written by
Cho Yin Yong
Principal AI Solutions Engineer, XY Space
Principal AI Solutions Engineer at XY Space. University of Toronto lecturer for five years, co-author of two patents, winner of two competitive AI awards, and nine years of regulated engineering leadership.
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