Claude Opus 5 shipped on the 24th of July, 2026, as claude-opus-5. It's priced at $5 per million input tokens and $25 per million output tokens — the same as Opus 4.8, and half of what Fable 5 costs.
The context window is 1M tokens long, the max output is 128K. And there's one breaking change: the model thinks by default now. In other words, on the older Opus you had to ask for thinking explicitly, and it's now set up so it thinks on its own automatically.
What actually changed from the previous Opus?
Now it's allocating thinking depth per turn itself depending on the set effort level; the higher the effort, the more it thinks, but you can still set it explicitly in the request, and the parameter itself hasn't changed — thinking: {"type": "adaptive"} is still valid and still means the default. This means that when migrating a project that didn't do any thinking just by renaming it to this new Opus, you need to make sure you have enough room for both the thinking and the visible part of its response, so raise max_tokens.
Also, if you disable thinking — which you can now do only at high effort or below, because pairing it with xhigh or max returns a 400 — it may output a call to a tool as plain text instead of emitting a proper tool_use block, or leak some of its internal tags into the output. So they recommend keeping it enabled and controlling the cost via the effort level instead.
How much does it cost?
It's priced at $5 per million input tokens and $25 per million output tokens, just like Opus 4.8. The price didn't move, but capability did. Fable 5 is $10 and $50 for the same volumes, so this is exactly half.
There's also fast mode — a separate pricing line, and a research preview that's been running since February (fast-mode-2026-02-01), on Opus 4.8 as well. It's available only through the Claude API; it's not on Amazon Bedrock, Google Cloud or Microsoft Foundry. It's priced at $10 per million input tokens and $50 per million output tokens, so 2x the price for up to 2.5x the output tokens per second (for the record this is throughput, not time to first token, so don't expect end-to-end latency to halve).
The minimum length a prompt needs to be to be cacheable is now 512 tokens instead of 1024, so prompts that were too short to get cached before are being cached automatically now.
Should you use it or Fable 5?
Opus 5 is half the price of Fable 5, and they're very close in terms of the numbers, so for most use cases it's usually about the price. According to their benchmarks:
- on CursorBench 3.2 at max effort it comes within half a percent of Fable 5 for half the price
- on OSWorld 2.0, which is computer-use work, it does even better than Fable 5 for a third of its cost
- it's state of the art on Frontier-Bench and GDPval-AA
- it more than doubles Opus 4.8 on Frontier-Bench v0.1
- it scores 3 times higher than the next best model on ARC-AGI 3
- it reaches around 1.5x the next best pass rate on Zapier's AutomationBench at the same cost
These are Anthropic's figures, so take them with a grain of salt; they're grading themselves. But generally speaking — mid-tier is no longer a compromise pick, and if you were to use Fable 5 by default, it's worth reconsidering in light of these numbers.
One place it doesn't win is exploit development. While on OSS-Fuzz it finds vulnerabilities about as well as Mythos 5, writing the exploits is scored separately — and there it's far behind.
Which effort level should you use?
The effort level starts at high by default; it's the default level both on the Claude API and for Claude Code, and this is what Anthropic document as a starting point. There are five levels in total — low, medium, high, xhigh and max — and in terms of this new Opus, it's more important than ever to choose the right one, as this model translates more effort into better output than any previous Opus, so it makes more of a difference which level you pick.
We'd recommend testing yourself and adjusting up or down from high based on these results — lower when the quality is good enough to save time and money, higher for the hardest tasks. It's also worth noting that especially in this model, the two lowest levels are actually really capable, so the quality there is really good for a fraction of the cost, and the code review is very decent even at the lower levels.
On the other hand, if you're planning to use the top two levels, make sure to set max_tokens high enough, 64K is a good place to start; you need it to have space for both thinking and acting across tools and subagents. Stream the responses rather than awaiting one of them.
Why does it show a smaller context window than a million?
The context window is 1M tokens long, and that's both its size and the max. It's not 200k long, this figure is about the client app; update it if possible. It's a single size, so you can't have reduced variants.
What breaks when you switch?
If you change the model to Opus 5, apart from renaming it, you need to update the surrounding configuration. The biggest potential pitfall is a prompt scaffolding that you might have left behind from older models — the model self-validates out of the box, so any leftover lines like "add a final verification step" or "use a subagent to verify" are prompting it to do additional verification now. Make sure to remove them.
Two things simply aren't available on this Opus: web fetch and Priority Tier. If you're relying on either, that's a blocker rather than something you configure around.
If you're an agent-infra maintainer, there are also 2 new features for you (both behind beta headers at the moment):
- you can now add/remove tools between conversation turns without losing the cached prompt, so you no longer need to resend a prompt with a fixed list of tools every time (beta header
mid-conversation-tool-changes-2026-07-01) - they have introduced a new fallback mode, which uses their recommended fallback models per refusal category instead of a list you maintain yourself (
server-side-fallback-2026-07-01) — Claude API only, and explicitly not supported on Microsoft Foundry
What does it do differently with no code change at all?
If you change the model to this new Opus, you may notice a few differences in terms of behaviour even if you haven't changed anything in your prompts:
- longer answers and written deliverables
- talks more during agent sessions, narrating its steps
- is more eager to use subagents in multi-agent setups
- does self-verification
None of these are bugs, they're all steerable by prompting. If you feel like the new Opus is slower or noisier than the previous one after the name change, make sure to check if it's not one of these differences before concluding that the model must've regressed; the "thoughtful and proactive" part of their description is the same thing from their side of the fence.
In terms of its actual capabilities:
- sustains long chains of reasoning
- can implement features and do big refactors in agent coding across long tool loops, writing multi-file code without leaving stubs or placeholders behind
- does really good code review and finds a lot of real bugs with a low number of false positives
- is good at reading charts, documents, and diagrams
- builds multi-sheet spreadsheets and slide decks
- runs subagent teams without them stepping on each other's toes
What we would do on day one
In terms of the day 1 plan:
- rename the model
- check every
max_tokens, as now it needs to accommodate thinking too; it's the most likely reason your output gets cut off after the switch - find and remove any verification scaffolding in prompts and agent instructions
- make sure that anywhere you've disabled thinking, the adjacent effort level is high or below, as pairing it with xhigh or max will throw a 400; enforce this before shipping
And perhaps most importantly: run your own code against it, not their benchmarks, and not ours either — although they could be a good start. We recently published a runnable benchmark with Kimi K3, Fable 5 and Opus 4.8 already hooked up. It contains a TypeScript ledger seeded with real bugs, with hidden tests and a prompt to benchmark the models against.
- Building with AI is our actual day job. We build software with AI for other companies — this is us reading the release notes on the tools we use every day.
- Curious how our benchmarks went? See how Kimi K3, Fable 5 and Opus 4.8 handled the same production task.
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