Claude Fable 5.2 at high effort beats Opus 5.5 at max

Claude Fable 5.2 came out on 9 November, and within hours the chart going round r/ClaudeAI showed it costing $26.54 per task on Artificial Analysis, against $10.04 for Opus 5.5. Both numbers are right, and both are at max effort, the setting Anthropic itself reserves for measuring a model's ceiling. "We literally need to put a sticky on this sub whenever new Claude models come out" to stop max-only cost charts, as the top reply (opens in a new tab) put it. Compare the two models at different effort levels and the answer changes. If your coding agent runs Opus 5.5 at xhigh or max, Claude Fable 5.2 two levels lower scores higher, or a point lower, for less money and less time. If it runs Opus 5.5 at medium, stay where you are.

TL;DR: Claude Fable 5.2 at high effort scores 62 on the Artificial Analysis Intelligence Index for $9.08 a task, while Opus 5.5 at max scores 59 for $10.04. If you run Opus 5.5 at xhigh or max, switch to Fable 5.2 two effort levels lower. For cheap, quick turns, Opus 5.5 at medium stays the better buy.

Is Claude Fable 5.2 better than Opus 5.5?

Claude Fable 5.2 has the top score on the Artificial Analysis Intelligence Index (opens in a new tab) right now: 65 at max effort, against 59 for Opus 5.5 at max. The index runs nine evaluations on Artificial Analysis's own harness, including FrontierCode 2.0 (code changes that open-source maintainers would merge) and Terminal-Bench 4.0 (real terminal tasks). Its sibling Mythos 5.2 is the same model with fewer cyber and biology safeguards, open only to verified organisations, so Fable is the one you can call.

The price per token is where the two split. Fable 5.2 costs $8 per million input tokens and $40 per million output, down from $10 and $50 on Fable 5.1 (Anthropic (opens in a new tab)). Opus 5.5 costs $4 and $20. At max effort the gap widens: $26.54 and 44.6 minutes per index task, against $10.04 and 25.1 minutes for Opus 5.5. That's 2.6 times the bill for six more points, and it's the comparison behind the advice in the launch comparisons I read: start on Opus, move up for hard work. It compares the two models at a setting few agents should run.

How do Fable 5.2 and Opus 5.5 compare at each effort level?

Artificial Analysis runs every effort level as its own model, which makes the trade visible. Here are both models from the slowest setting down:

Read it diagonally. Fable 5.2 at high beats Opus 5.5 at max on all three columns: three points higher, $0.96 cheaper per task and 7.7 minutes faster. On FrontierCode 2.0 alone, Artificial Analysis has Fable at high on 49.1% against 46.4% for Opus at max. Anthropic's launch post (opens in a new tab) puts Fable at High "about level with" Opus 5.5's best on the same benchmark for roughly half the cost per task. Artificial Analysis measures a much smaller cost gap, so plan on the independent number.

The same two-level step works one row down. Fable 5.2 at medium lands a point under Opus 5.5 at xhigh for 63% of the cost and about half the time. One level is the wrong step: Fable at high beats Opus at xhigh by five points but costs half as much again. Opus 5.5 at max is the one setting in the table that another model beats on score, cost and time at once. If that's your default, it's the setting to change first.

Fable's own xhigh is worth a look too. It scores the same 62 as high on the index for $5.75 more per task, which matches Anthropic's advice to step up to xhigh only where you've measured a gain.

When should you keep Opus 5.5 instead?

Keep Opus 5.5 when the work is short and you care about the bill per turn. Opus 5.5 at medium costs $2.45 a task and at low $0.98, and nothing in Fable's lineup goes that low: its cheapest setting, low, is $3.10. For quick edits, renames, test fixes and questions about the codebase, that floor matters more than the extra points.

Fable 5.2 also spends thinking on turns that don't need it. Anthropic's notes on the model (opens in a new tab) say that at high and above it "thinks before almost every reply", and that a prompt line asking it to skip deliberation doesn't stop it at medium and above. The only lever is the effort setting. A session full of small requests at high pays for thinking on every one.

That's why Anthropic's own advice still starts on Opus: "start with the latest Claude Opus model … and move to Claude Fable 5.2 when your evals on a Claude Opus model at higher effort still don't meet your requirements." I'd read that sentence alongside the table. The moment you've raised Opus to xhigh or max to get the work right, you've reached the condition Anthropic describes, and Fable two levels lower is the cheaper answer.

I'd stop running Opus 5.5 at max. For everyday coding I'd keep it at medium or high, and give the long, ambiguous jobs to Fable 5.2 at high.

What breaks when your agent moves to Claude Fable 5.2?

If Claude Code, claude.ai or the Claude Agent SDK builds your requests, switching to Claude Fable 5.2 is a model picker change, with two catches.

Routing between the two models has a cost. Moving a conversation onto Fable 5.2 keeps its earlier reasoning; moving it from Fable back to Opus 5.5 drops Fable's thinking (opens in a new tab) for the turns Opus runs. A router that bounces a session between them loses context each time it steps down, so route per task, not per turn.

Fable 5.2 also requires 30-day data retention. If your company needs zero data retention, it's off the table unless Anthropic signs off.

If your own code calls the API, four things in Anthropic's migration notes (opens in a new tab) can return a 400:

• Forced tool use is gone. tool_choice set to any or to a named tool fails. Keep it on auto and use strict tool use or JSON outputs when you need a schema.
• Thinking is always adaptive. A budget_tokens setting, or turning thinking off, fails. The effort parameter is the only control.
• History edits fail on every account. If your code rewrites the system prompt, the tools or an earlier message, the API answers "The block is bound to a different conversation". Agents that trim history need to stop editing it: only append new messages, and use the API's compaction feature to summarise old turns.
• Advisor pairs change. The advisor tool lets one model (the executor) consult a stronger one. A Fable 5.2 executor only accepts a Fable 5.2 or Mythos 5.2 advisor.

How do you use Claude Fable 5.2 without paying for it on every turn?

Keep Opus 5.5 doing the work and let Claude Fable 5.2 advise it. The advisor tool (opens in a new tab), in beta on the API, lets the model running the task consult a stronger one when it gets stuck, and every executor model accepts claude-fable-5-2 as its advisor. Set Opus 5.5 at medium as the executor and Fable 5.2 as the advisor, and you pay Fable's price only on the turns that ask. One catch if your code logs or reads the advice: a Fable 5.2 advisor returns it encrypted.

The reverse split works for long jobs. Anthropic positions Fable 5.2 as an orchestrator that plans and hands simpler tasks to smaller models, and the table supports it: Fable at high plans the migration, and Opus at medium does the file-by-file edits at $2.45 a task. Cursor's numbers show how far the planning has moved since the last version: on its own benchmark, Fable 5.2 scores 56.9% at default effort (opens in a new tab), up from 49.2% for Fable 5.1, and 60% at max, the top of its board.

None of these numbers is your codebase. Before you change the default, run a handful of real tasks at Fable high and at your current Opus setting, and compare cost and pass rate per task, the way Artificial Analysis does. Start Fable at high, the API's default, since Anthropic says most of its gain is already there.

What to do this week

Check the effort setting your agent actually runs. If it's Opus 5.5 at xhigh or max, try Claude Fable 5.2 two levels lower on your next long task and compare the bill and the result. If it's Opus 5.5 at medium for quick work, keep it, and save Fable for the jobs where Opus kept needing another pass. If your own code calls the API, check tool_choice and history edits before you switch. I didn't run these benchmarks myself; every number links to where it came from.

I write about coding agents and the models behind them on the gabs.app blog. I'm a full-stack engineer and my work is on gabs.app; if you're hiring, the contact form comes straight to me.