Claude Opus 4.8

Developers want agents that can take on real software work and finish it. In our testing across GitHub Copilot CLI and VS Code, Claude Opus 5.5 used among the fewest tokens and steps we measured. In VS Code, it solved more terminal tasks than Opus 5 in less than half the steps. More than making individual tasks more efficient, it’s making developers’ bigger projects more achievable.

I handed Claude Opus 5.5 a large engineering task across six of our repositories and let it run overnight, unattended. It stayed on task for over 18 hours defining how our services talk to each other and working out how each one should apply that. Compared with Opus 5, it hit milestones faster and required minimal reworking. Its code comments were short and useful instead of long and prose-heavy. I’m struggling to find anything negative to say.

For Lovable builders, Opus 5.5 means faster builds with the same quality, whether you’re starting from scratch or working on a live app. It gathers context once, makes fewer and more complete edits, and doesn’t get stuck retrying, finishing in a third to half fewer steps and using significantly fewer tokens along the way.

On our genomics analysis work, Claude Opus 5 behaves more like a careful scientist than any model we’ve run. It reaches for the right statistical tests to rule out confounders, cross-checks its own results by independent methods, and stays on track through long multi-step analyses.

With Claude Opus 5.5, we’ve seen a clear improvement in token efficiency across our internal evaluations, as we’ve been able to complete the same tasks both cheaper and faster.

We test models on real engineering and trading-desk work. On our agentic coding tasks, Claude Opus 5.5 matched Opus 5’s quality in about half the turns, time and output tokens, cutting the cost of that workload by 40 to 50%. It posted the highest score we’ve recorded on one desk’s trading-support suite, passing tasks earlier Claude models had failed, and topped all eight models on our analysis task.

Claude Opus 5.5 delegates to subagents far more effectively and checks its own work in creative ways. Self-verification loops feel easier to set up. It found savings opportunities in our cloud bill that previous models had missed, and in code review it caught a bug by checking external docs for a third-party integration we’d modeled wrong several commits earlier.

Every call an agent makes is time and cost a developer feels. On a public benchmark of real command-line tasks, Claude Opus 5.5 solved more than Opus 5 while making about 40% fewer calls and using half the tokens. For developers building with Kiro, that means faster, more affordable agent sessions for routine tasks and complex challenges alike. Opus 5.5 will soon be available in Kiro.

Even at its lowest effort setting, Claude Opus 5.5 caught 72% of known bugs in our code reviews to Opus 5’s 56% at high effort, with fewer false alarms and a fraction of the output. On US consulting analysis, low thinking effort matched its higher thinking settings on half the output and passed our quality checks. When more lower thinking efforts are deployed in production, that’s client-ready work delivered efficiently.

Financial firms need outputs that are consistently correct. At its lowest effort setting, Claude Opus 5.5 beat Opus 5 at high effort on our BigFinance Bench with about 60% fewer output tokens. Its answers are shorter and better structured, and its slides come out denser, more in line with industry standards.

Evaluating new models is central to the multi-model approach behind the LexisNexis Legal Intelligence Engine. In our initial evaluations, Claude Opus 5.5 identified highly relevant citations consistently, demonstrated strength with statutes, and structured its answers around the central legal frameworks and key issues. These are the kinds of capabilities we look for to help our customers accomplish more with Lexis+ with Protégé.

In quant research, one wrong assumption can undermine a result. At its lowest effort setting, Claude Opus 5.5 largely solved our evaluation task. At higher settings, it went even further: it detected that the minute indexing in our own instructions was off by one and corrected for it, noting that this would cost it points with the grader. It was right, and no model we’ve tested had caught and acted on that before.

As models get better at data work, we’re seeing more convincing-sounding conclusions the data doesn’t support. Claude Opus 5.5 keeps digging past the first plausible answer. One task in our DataBench benchmark asks whether packages were late or tracking was just slow. Opus 5 checked delivery confirmations and called tracking healthy. Opus 5.5 found the packages were late and tracking was broken too. We’re bringing it into the Hex agent for this work.

CoCounsel combines multiple models with our content and expertise for complex legal work. With Claude Opus 5.5, we’re seeing better results in our expert evaluations and on our internal benchmarks, alongside gains in speed and token efficiency. We’re excited for customers to experience that difference in the back-and-forth with CoCounsel as a sounding board, weighing evidence and refining their thinking in ways benchmarks don’t fully capture.

On end-to-end finance workflows graded against expert rubrics, Claude Opus 5.5 covered 86.6% of what we look for versus 60.3% for Opus 5. On retrieval evals, it achieved our best-ever citation recall with better token efficiency than Opus 5, which keeps our cost per research task in check.

Viktor is an AI employee that lives in Slack and Microsoft Teams, so every step he takes shows up in our costs. At the same effort, Claude Opus 5.5 needs fewer steps and tool calls per task than Opus 5 and costs nearly half as much, while getting twice as many of our hardest tasks right.

Verbose, hard-to-follow output has been my biggest frustration with frontier models, and Claude Opus 5.5 fixes it. It writes like a good colleague, and follows our writing rules. A design spec came out usable with very minimal edits, and when it rewrote one of our prompts I preferred its version to my own. When it optimized our test suite, I could follow its reasoning easily and shipped the change with confidence.

I run long Claude Code sessions every day. On a multi-day rebase of 40 stacked pull requests, one Claude Opus 5.5 session directed a dozen more sessions and laid out every conflict plainly. On the calls that it held, it framed them clearly that after hours away I could answer in minutes. All 40 passed CI the next afternoon. It’s a substantial upgrade over Opus 5.

Our customers use Box AI on enormous amounts of content, so speed and cost are a top priority. In our evaluations, Claude Opus 5.5 used a third of the tokens Opus 5 did, and its answers were 40% less verbose without losing accuracy. We expect that to matter a lot for teams running agents across their content in areas like financial services and the public sector.

Overnight, Claude Opus 5.5 autonomously handled a bug in our Lakehouse services layer that I hadn’t had time to diagnose. It investigated, designed the fix, and implemented it on its own. By morning the change was done and passed our test suite. Its writing is easy to follow and more coherent than Opus 5’s. Our pull requests and user-facing docs have needed almost no editing.

Claude Opus 5.5 is the first model we’d default to at medium effort. In our testing it matched Opus 5 on high effort, while using 20 to 25% fewer output tokens. On long, messy investigations it always came back with a clear, actionable answer. This means our customers get more done for less.