Claude Platform

“We fed Claude Sonnet 5.5 hundreds of real support use cases across replies and escalation requests. It made fewer wrong decisions and resolved tickets faster than the Claude models we use in production today. Tickets were processed 20% faster, getting our customers the help they need without the wait.”

“The decision to choose Claude was entirely data-driven. We tested multiple model providers side by side, and Claude consistently delivered the best results for case resolution rates and customer satisfaction scores.”

“The partnership with Anthropic has been exceptional—their guidance and support have helped our engineering teams maximize the models.”

“You can just throw Claude Fable 5.2 at new, ambiguous systems it has never seen before, and it will figure out how to actually work within them. It’s almost like an incident commander that can send out faster models to get quick signals and coordinate, which removes that burden from me as an operator.”

“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.”

“Claude Fable 5.2 says it doesn’t know things instead of hallucinating. At one point it told me its last estimate was wrong and that it wouldn’t give a number until it had measured the change. Very impressive!”

“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.”

“In an I-9 compliance review, Claude Fable 5.2 gave the client clearer direction than Fable 5.1. It identified the employees needing immediate attention and explained how to correct each issue and who should act. It’s an exciting improvement in the model’s ability to give practical legal guidance.”

“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.”

“The model is in my head. It understands the intent behind every prompt I’ve given it. I haven’t had to say ‘No, I meant…’ a single time. Projects we’ve been pushing along with other models for weeks got finished and approved by our teammates in a few days with Claude Fable 5.2, and the humans reviewing its code had less to say.”

“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.”

“Claude Fable 5.2 is incredible at playing every major role within a research system: defining questions, interpreting, coordinating, and of course finding and implementing the key ideas. Definitely one of the strongest on taste and judgment that we’ve seen. In an internal research automation benchmark, it achieved a full 10% lower error than the next best model.”

“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.”

“In one test combining country debt summaries, repayment history and regional benchmarks, Claude Fable 5.2 generated SQL that returned the expected results. The SQL generated by two other models also ran successfully, but incorrectly summed country-level summary amounts, producing values five to eight times the expected amounts. Getting these business calculations right is why we see value in using Fable 5.2 for enterprise SQL generation.”

“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.”

“In a recent evaluation, we asked Claude Fable 5.2 to investigate how one organization tested a new integration in its core technology systems. It traced the answer to the right sources and didn’t overstate what the evidence showed. It also flagged that the question’s framing could be misleading. In investment research, that restraint makes clear what remains unknown and where the market may be mispricing risk or opportunity.”

“On one task, Claude Fable 5.2 had to pull a company’s financials out of its 10-K into a forecasting model, and the template didn’t say whether a metric should be the reported figure or calculated. It inferred the author’s intent from the cell formatting, caught that the regional numbers didn’t sum to the reported total, and got every cell right. Better judgment calls like that are the main reason it scored 91% on our eval vs 86% for the next best model.”

“In Epic’s early testing, Claude Sonnet 5.5 cleared the same quality bar you’d expect from a higher-tier model, holding up on a system design audit and a data-flow review. The new model managed tens of thousands of lines of code for gameplay system architecture, kept responses snappy, handled multi-hour tasks, and delivered with less prescriptive prompting.”

“Without changing any of our prompts, Claude Sonnet 5.5 did better than Sonnet 5 on almost all of our offline Slackbot evals, in fewer steps and with about 14% fewer output tokens. When someone gives Slackbot a task, quality and speed are what matter most, and Sonnet 5.5 allows Slackbot to deliver better outcomes for users, faster.”

“We fed Claude Sonnet 5.5 hundreds of real support use cases across replies and escalation requests. It made fewer wrong decisions and resolved tickets faster than the Claude models we use in production today. Tickets were processed 20% faster, getting our customers the help they need without the wait.”

“The decision to choose Claude was entirely data-driven. We tested multiple model providers side by side, and Claude consistently delivered the best results for case resolution rates and customer satisfaction scores.”

“The partnership with Anthropic has been exceptional—their guidance and support have helped our engineering teams maximize the models.”

“You can just throw Claude Fable 5.2 at new, ambiguous systems it has never seen before, and it will figure out how to actually work within them. It’s almost like an incident commander that can send out faster models to get quick signals and coordinate, which removes that burden from me as an operator.”

“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.”

“Claude Fable 5.2 says it doesn’t know things instead of hallucinating. At one point it told me its last estimate was wrong and that it wouldn’t give a number until it had measured the change. Very impressive!”