Anthropic says Fable 5.1 is cheaper than Fable 5, by 25% and sometimes 45%. Artificial Analysis measured it costing 18% more. Both are correct, and the reason is worth understanding before you switch models.
The rate card moved in one place
Fable 5.1 shipped September 1, 2026, less than three months after Fable 5’s June 9 release. It is a tuning pass over the same architecture, so the context window, max output, and headline input and output prices are unchanged. Input stays at $10 per million tokens and output at $50.
The one change is cache reads: $1.00 per million tokens on Fable 5, $0.25 on Fable 5.1. That is a 75% cut. Cache writes still cost $20 per million and are not discounted, which caps how much of the saving reaches your invoice.
If you are already thinking about how provider-side caching interacts with your routing and privacy choices, the same economics apply here: Zero Data Retention: Enforcing Provider-Side Privacy on AI API Calls covers what gets stored and where.
Why two measurements disagree
Firecrawl’s 57-run test on a Claude Team API account, billed at $26.00, found Fable 5.1 used more output tokens at every effort level. Ratios were 1.37x at low, 1.12x at high, and 1.30x at max. At max effort the extra tokens are reasoning, not text: reasoning went from 4,205 to 6,725 while visible output fell from 3,522 to 3,301. Fable 5.1 wrote 6% less text for 30% more billed tokens.
Artificial Analysis prices models by cost per completed task. Fable 5.1 needed 140 million output tokens to finish their Intelligence Index against Fable 5’s 83 million, and their evaluations use almost no caching. Cost per task rose from $3.14 to $3.69.
Anthropic’s figures come from production traffic at default effort, where cache-heavy agent sessions dominate. The two numbers measure different workloads, not different arithmetic.
What the discount looks like on a real bill
On six sandboxed agentic builds, the cache-read cut showed up. One Fable 5.1 build re-read 1.69M cached tokens for $0.42; a Fable 5 build re-read 1.34M and paid $1.34. The saving was 15% to 30% of the bill, not the headline 25% to 45%, because cache writes are charged at full price.
The discount scales with how many times a context gets re-read, not with how large the context is. Build a large cache and read it twice, and almost none of the saving reaches you.
Effort level is the lever most teams forget
Effort levels run from low to max, set with output_config: {effort: "low" | "medium" | "high" | "xhigh" | "max"}. Artificial Analysis prices Fable 5.1 at $0.77 per task on low and $3.69 on max, a 4.8x range for eight points of Intelligence Index score.
Defaults differ by product. Claude Code defaults to High. Claude Cowork and Claude.ai default to Medium. Anthropic’s savings figures were measured at default effort; Artificial Analysis measured at max. Most cost surprises come from an inherited default, not a model choice.
Reasoning tokens bill at the same $50 per million as visible text and never appear in the response. On Fable 5.1 thinking is permanently enabled, the thinking parameter should be omitted, and budget_tokens is rejected with a 400 error. Skills and a trimmed global CLAUDE.md cut visible output, but neither changes how much the model thinks.
How to decide for your workload
Pull one recent run and compare cache_read_input_tokens against output_tokens in the usage fields the API returns. If cache reads dominate, Fable 5.1 is likely cheaper for you. If output dominates, the extra reasoning tokens will cost more per finished task.
The variable neither party measured is how tightly you specify the task. Tight spec, Opus 5 won on cost every time in the Firecrawl runs. Open brief, Opus 5 averaged $11.83 against Fable 5.1’s $7.00. That suggests the model choice matters less than how much room you leave for the model to think.
One caveat: the benchmark figures cited are vendor-run with production safeguards on, and Anthropic reports a standard error wide enough that any single row inside that band is a wash. Treat the direction as the signal, not the decimal.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.