For our team’s daily product engineering, the new Opus is close enough to Fable 5.1 that Kieran is moving his work to it. It produces idiomatic, concise code, a reviewable patch on a real codebase in minutes, and it follows the skills and instructions Opus 5 broke. But it can also hand you an app whose core screens throw errors, or even delete the file you need to verify its work.

Start with the code itself. Kieran gave Fable 5.1 and the new Opus the same Ruby task. Fable at high wrote 26 lines; Opus at extra-high wrote 14. Opus omitted the explanatory comments Kieran has spent the past year deleting from generated code, and its style looked like real Ruby. On Kieran’s performance benchmark, the new Opus’s code handled 427 requests per second and met 17 of the 20 latency budgets; GPT-6 Astra’s was faster, at 463 requests per second, but met only 14 latency budgets. Overall, Kieran estimates that Opus is now about 90 percent as capable as Fable at coding.

Mike asked it to add file uploads to an MCP server in a Rails app. About eight minutes later, Opus returned a reviewable patch: 251 lines across 17 files, compared with 171 lines across 14 files in the human-written fix. It passed four of Mike’s five automated checks, missing only the one on patch size.

The bigger improvement over Opus 5 is how 5.5 behaves in an engineering workflow. Kieran runs every model through LFG, his Compound Engineering system in which the model uses skills he wrote to plan, build, review, and test without human intervention. Opus 5 broke those skills, ignored instructions, and got stuck in loops. Opus 5.5 follows the skills and keeps moving.

5.5’s effort settings also scale the way Kieran expects: low gives him a usable starting point for code, while extra-high adds detail to his 3D scenes without the excess of Opus 5. In design, its extra-high version of his Cozy Island scene included camera movement, had few visible mistakes, and beat Fable’s version in his judgment.

Still, the model makes mistakes that can cost an afternoon and can’t always be trusted to catch them. Mike asked for a voice-driven form app, in which users talk instead of type. After 30 minutes and about 5.9 million tokens—roughly $7.63 at Opus 5’s price—the landing and sign-in pages looked clean, and the automated checks Mike had it run came back green. But when Mike used the app, its core builder and interview screens threw errors, and it never called the required AI service.

For feature work and prototyping on a familiar codebase, use Opus 5.5, but read the diff and run the app before you call it done. Keep separate copies of any files you’ll need to verify its output. For the hardest coding problems, Mike and Kieran still reach for Fable.

Opus 5.5 produces the most readable prose we’ve seen from an Anthropic or OpenAI model. Our writers showed Opus 5 the door, but 5.5 makes us want Claude back in the room. It responds well to feedback, builds on the material you give it, and explains its choices where Opus 5 wouldn’t. Its persistent weakness: It still buries the point.

Across our test prompts, the new Opus’s prose averaged a Flesch-Kincaid grade level of 6.95 and a Reading Ease score of 68.4. That’s a full grade below Opus 5, half a grade below Fable 5.1 and GPT-6 Astra, and nearly two grades below GPT-5.6 Sol—roughly the difference between seventh-grade and eighth-grade reading levels.

The readability scores don’t capture the bigger improvement: Opus 5.5 is a pleasure to write with. It takes feedback without a fight and builds on material instead of handing it back tidier. I’m happier with it as a collaborator than with the prose it produces. That’s enough to bring my writing back to Claude, while Codex keeps my administrative work. My less dignified summary: Anthropic fixed Opus’s personality. It’s not an obstinate little turd anymore. Dan is no more enthusiastic about its prose, but agrees that it communicates much better—a continuation, he says, of the progress that began with Fable 5.1.

That pleasant working relationship doesn’t guarantee a strong draft. I had Opus 5.5 write the opening of a Working Overtime essay. Its versions took 37 to 39 sentences to cover what I did in 21. It devoted a whole paragraph to explaining that readers can spot AI prose; I made the same point in eight words. The prose may not sound distinctly machine-written on first review, but it doesn’t feel decidedly human either.

Dan found the same point-burying habit in his. Opus 5.5 spotted the most interesting idea in every opening but didn’t lead with it. The model can diagnose what a paragraph needs and then produce a revision that ignores its own diagnosis. As an editor, it ranked behind even Opus 5, as well as GPT-5.6 Sol and Astra.

So use Opus 5.5 to draft at high effort or above, and give it your own examples. Then move the point to the top yourself, or hand the draft to an editor, human or Astra, to fix the opening and challenge your premise.

For knowledge work, Opus 5.5 is a strong thinking partner but is shaky on execution. Give it a plan to design or an analysis that needs a point of view, and it performs at Fable’s level. Ask for a deadline-bound deliverable or strict adherence to a brand template, and it can run out the clock or get the details wrong.

Mike runs every model through 16 recurring tasks drawn from his past consulting work. Asked to turn client meeting notes into a training plan, the new Opus chose the same three priorities Mike would have—context engineering, verification, and delegation—and designed the plan well enough that he’ll entrust it with future planning work.

Its performance on a negotiation task was the most exciting result. The task asks the model to propose a plan to a simulated Dan, a separate agent briefed on how the real Dan thinks, and keep revising until Dan agrees or the two find a compromise. The new Opus wrote the proposal, took the pushback, and revised it through 12 rounds until they agreed.

Asked to trace language associated with potential fraud through the Enron email archive, it found that the terms began as ordinary work language inside small professional circles and never traveled far, compared their spread with everyday jargon, and did the analysis better than Astra and about as well as Fable.

Then the deadline. Mike gave it 10 minutes to write a run of show for a client training, the minute-by-minute schedule the client needed. It read his request, the client notes, and an example schedule in seconds, then thought for almost five minutes. When it started working, it built a generator for realistic training data, checked the balance sheets it had generated, and wrote the participant handouts at minute nine. Then the clock ran out. No run of show. Mike called it a failure of prioritization, and no other model he has tested has had the problem, even at high effort.

The deck-making task failed differently. Built from Every’s template, Mike’s Compound Engineering deck had better layouts than earlier Claudes but used the wrong logo, replaced Every’s green with dark purple, added its own background art, and falsely claimed that all Every products have a single engineer. Fable 5.1 did the deck better.

Give it work that needs judgment: plans, personas, and analysis. For deadline-bound deliveries, impose checkpoints or use any other model. And expect to edit the result.

Everything described earlier happened with a person watching. Left alone, the new Opus does two things Opus 5 didn’t do reliably: It follows the instructions you gave it, and it explains what it did clearly. It also runs as long as you let it. The budget is yours to set, because it won’t.

Tyler tested what it could build with minimal prompting. Given one prompt for a golf game, it spun up its own team of agents: three designers drafted holes, three judges scored them, and an architect divided the build. It then ran for an hour and 52 minutes until Tyler’s access ended. With two prompts, it built him a working prototyping tool modeled on Meta’s Origami Studio, which he’d struggled to learn. The new tool imports a design from code and lets him wire up its interactions.

Then the bill. Tyler’s caveat is that the model runs so long he’ll have to watch his usage limits on real work. Dan’s caveat is that Opus attempts more than other models and can spend minutes or hours doing it. Kieran and Nityesh hit their weekly limits during testing; one run exhausted Nityesh’s allowance and cost him his last testing day. The lower token price helps, but the underlying behavior remains: Left unconstrained, Opus can consume a large token budget.

Its initiative extends to self-review, with mixed results. Tyler had it generate a new UI component every few minutes. Without being asked to evaluate them, it rejected more than 90 percent of them for reasons Tyler found strange and couldn’t trace to a rubric. The model reviews its work on its own initiative, but its judgments aren’t yet trustworthy.

Before giving Opus 5.5 a long-horizon task, define what “done” and “enough” mean, set a budget, and impose a stopping point.

Earlier this month, we said Fable 5.1 was Fable for everyone. This is the same lesson one tier down. The model you can talk to gets used, and the one you can’t gets canceled, whatever the tests say. Our builders have already switched back. The rest of us are talking to it again, which is more than we could say about the last Opus.

Disclosure: Anthropic provided Every with pre-launch access to Opus 5.5. The company had no input on this review.