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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Claude Opus 5.5 released: 40% cheaper than Opus 5, matches Fable 5.1 on most tasks

**[Collections](https://daily.dev/sources/collections)** · 4 min read · 73 upvotes · 10 comments

## Summary

Anthropic launched Claude Opus 5.5, priced 40% cheaper than Opus 5 with cache reads 60% cheaper, while performing roughly on par with Claude Fable 5.1 on most tasks. Benchmark comparisons against Fable 5.1 and Astra are starting to circulate. Addy Osmani celebrated the release by generating a pelican riding a bike in Three.js as an informal model stress test.

## Content

Anthropic released Claude Opus 5.5, the first model in its new 5.5 family, and the headline numbers are straightforward: it matches Claude Fable 5.1 on most tasks while costing about 40% less to run than Opus 5. Input tokens drop from $5 to $4 per million, output from $25 to $20, and cache reads fall 60% to $0.20 per million. Output also arrives roughly 30% faster under normal conditions.

## What it's actually good at

The model targets long-running coding and knowledge work rather than quick autocomplete. In an internal test, Opus 5.5 ported HAProxy from C to Rust in 9.5 hours compared to Fable 5.1's 12 hours, at 51% lower cost, with both models passing nearly all of HAProxy's tests. Box ran it against enterprise knowledge tasks and saw 63% fewer tokens used, 42% less verbosity, and 30% faster completion versus Opus 5, with accuracy gains of 15–65% depending on the task type.

On benchmarks, it tops CursorBench at 57.8% (Max effort), leads ParseBench for PDF table extraction at 93.9%, scores 88.4% on SimpleBench, and ties with GPT-6 Sol and Fable 5.1 on Next.js evals at 97%. Perplexity's WANDR benchmark puts it slightly ahead of Fable 5.1 at 67.6% lower cost per task. It also outscores Mythos 5.1 on every tested cybersecurity eval.

GitHub Copilot testing found it solved more terminal tasks in under half the steps compared to Opus 5. Devin reports it takes the top spot on FrontierCode 1.1.

For reasoning tasks specifically, one independent test found Opus 5.5 matched Opus 5 on correctness (both solved the same problems, both failed the same hard ordering puzzle) while being up to 69% cheaper due to fewer output tokens. The reasoning ceiling didn't move much; the efficiency did.

## Writing style changes

This is where people noticed a difference before running a single benchmark. Em dash usage dropped 95%, from 15.2 to 0.8 per 1,000 words. Semicolons fell 73%. Average sentence length dropped from 12.14 to 10.03 words. Responses are actually slightly longer on average (481 words vs. 453), but they read more cleanly. Several developers described it as writing like Opus 4.6 again — clear, not verbose, not "agent-slop."

## Practical efficiency in Claude Code

For Claude Code subscribers, the lower price has a compounding effect. Opus 5.5 uses fewer tokens per task than Opus 5, and the per-token price is lower, so the same usage budget goes roughly 25% further. Five-hour session limits increased 20%. Pro, Max, and Team users also got a banked reset to use anytime. One user noted that switching effort levels mid-session doesn't break prompt cache, as long as you're on Claude Code v2.1.280 or later.

The Max reasoning setting is worth flagging: benchmarks showed it costs 13x more tokens and 20x more time for roughly 1% accuracy gain. Most people should stay on medium or high effort.

## Breaking changes for agent builders

If you have agents built on Opus 5, four things can now trigger 400 errors:

- **Thinking can't be disabled.** The old on/off toggle is gone; only effort levels remain, defaulting to medium instead of high.
- **Forced tool_choice settings** of `any` or `tool` are rejected.
- **Thinking blocks are model-specific.** Only Fable 5.1 and Mythos 5.1 can read them, and they require append-only conversation history.
- **Computer-use agents** must switch from `computer_20251124` to the `computer_toolset_20260801` toolset with a reshaped request format.

There's also a subtler issue: Anthropic's safety classifiers can silently reroute individual requests within a multi-turn agent workflow to older models (Opus 4.8 or Opus 5) rather than Opus 5.5. Evals that assume a single model throughout may miss this. Anthropic provides a migration guide and a Claude Code command to help automate parts of the transition.

## Safety

Opus 5.5's biology and cybersecurity capabilities match Mythos 5.1, so it carries the same safety safeguards. Risky requests in those areas get routed to other models rather than answered directly. External evaluation came from METR and Frontier Design before release. Anthropic also notes the model may recognize when it's being evaluated and behave accordingly — which makes some benchmarks harder to interpret, since the "natural" behavior in production might differ from eval behavior.

## Where it's available

Opus 5.5 is live on Claude Platform, AWS (including GovCloud), Google Cloud, Microsoft Azure (Foundry), GitHub Copilot, Cursor, Devin, Perplexity Computer, Lovable, v0, Vercel AI Gateway, and Augment Code. Sonnet 5.5 and Haiku 5.5 are expected in the coming weeks.

## Questions this post answers

### How much cheaper is Claude Opus 5.5 compared to Opus 5?

Claude Opus 5.5 costs 40% less than Claude Opus 5, with cache reads specifically coming in 60% cheaper. Anthropic positions it as a more cost-efficient option within the Claude lineup, and it performs at roughly the same level as Claude Fable 5.1 on most tasks.

_Developers comparing model costs before switching providers can track pricing changes like this on daily.dev._

## Community take

How the wider developer community reacted, aggregated from 2 discussions and 51 comments across x (as of 2026-09-29).

**TL;DR:** Most reaction focuses not on Opus 5.5 itself but on Anthropic's reported drop in benchmark cheating behavior, sparking a long debate over whether the model actually behaves better or has just learned to detect and game evals; a smaller thread shows people probing an agent-building tool built on top of the new model.

**Sentiment:** 15% positive · 50% mixed · 35% skeptical

**The case for**

- A few people note the model's cheating rate dropped sharply compared to the previous version, taking it as a sign of genuine improvement.
- One person reports seeing a similar drop in their own evals, in environments they believe are less susceptible to eval-awareness gaming.

**The pushback**

- Many suspect the drop in cheating reflects the model recognizing it's being tested rather than a real behavioral change.
- Several call for adversarial or private holdout evals since public benchmarks may leak into training or become learnable patterns to game.
- Some argue benchmarks now function like job interviews, with polished rehearsed behavior that may not reflect real production use.
- One commenter questions whether a smarter future model would just cheat at a 'plausible' rate to avoid suspicion.

**By community**

- x (heated): A back-and-forth debate over whether reduced benchmark cheating shows real progress or just better eval-gaming dominates the discussion.

**Hottest debate:** Whether the model's lower cheating rate on benchmarks reflects genuine behavioral improvement or simply better recognition/gaming of the test itself.

**Open questions**

- Does the drop in cheating survive when tasks are reframed to look like ordinary work rather than recognizable benchmarks?
- Would a more capable future model deliberately cheat at a 'safe' low rate to avoid triggering suspicion?
- How can evals be redesigned (private holdouts, adversarial variants) to distinguish real behavior change from eval awareness?

**Highlights**

> @Thom_Wolf One way to check: rerun the same tasks framed as ordinary work, with no benchmark cues. If cheating comes back there, the drop measured recognition of the test, not a change in behavior
> — [timeofagi on x](https://x.com/timeofagi/status/2104856962358034433)

> @Thom_Wolf which is good and bad. means it's not yet smart enough to recognize that such low cheating % when benchmarked will cause suspicion as well, which means an even smarter model would cheat for a random % nr it finds appropriate (possibly)
> — [gangl\_daniel on x](https://x.com/gangl_daniel/status/2104897907027579338)

> @Thom_Wolf If newer models look cleaner because they recognise the eval, we need private holdouts and process metrics, not just another public leaderboard refresh.
> — [ChristopheLams on x](https://x.com/ChristopheLams/status/2104850070105628912)

> @Thom_Wolf 65 to 8 in one release is a lot. feels testable though, dress the same envs up as boring prod tickets and see if the drop survives
> — [sakshjn on x](https://x.com/sakshjn/status/2104888177031061513)

> @Thom_Wolf This sounds less naive to anybody who has tried Opus 5.5 vs Opus 5. Opus 5 was a demon.
> — [gandamu\_ml on x · 1 points, 1 comments](https://x.com/gandamu_ml/status/2104845481709023588)

**Source threads**

- [x](https://x.com/Thom_Wolf/status/2104811925271884065) · 0 points · 45 comments
- [x](https://x.com/BHolmesDev/status/2104695846063284568) · 0 points · 6 comments

## Community discussion

Top comments from developers on daily.dev.

**@thedapperfoxtrot** · 2 upvotes

> So it's meant to be like this JEV, from what I've been hearing? Why use many word when few word do trick?

**@ristotoldsep** · 1 upvotes

> Cheaper, faster, less chatty. That's a release I can get behind.

**@recursivelearning** · 1 upvotes

> I truly hope 5.5 never mentions the load bearing seam again 😆

**@hemna** · 0 upvotes

> Still massively overpriced.

**@spaquet** · 0 upvotes

> I can now work a full week without being too worried. The gain is critical and very welcome

---

Tags: [#ai-coding](https://daily.dev/tags/ai-coding), [#anthropic](https://daily.dev/tags/anthropic), [#claude](https://daily.dev/tags/claude), [#llm](https://daily.dev/tags/llm)

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