Introducing Claude Sonnet 5.5 \ Anthropic Skip to main content Skip to footer Research Policy Commitments Learn News Try Claude Introduction Performance Cost Safety Getting started Claude Sonnet 5.5 September 28, 2026 (1) Introduction (2) Performance and cost (3) Safety (4) Getting started Skip intro Scroll down Introducing Claude Sonnet 5.5, the second model in the Claude 5.5 family. It’s a clear upgrade over Claude Sonnet 5, runs 30%+ faster, and costs up to 30% less for most work. Sonnet 5.5 is a faster, lower-cost complement to Claude Opus 5.5. Where Opus 5.5 is built for complex work requiring careful judgment, Sonnet 5.5 is strongest at well-scoped everyday tasks, fixing bugs, and creating polished documents, slides, and spreadsheets. It’s also got a sharp eye for design. Claude Haiku 5.5, built for high-volume and cost-sensitive applications, will join the Claude 5.5 family in the coming weeks. Sonnet 5.5 improves over Sonnet 5 on: Performance. Sonnet 5.5 scores 70.6% on Terminal-Bench 4.0, an agentic coding evaluation, compared to Sonnet 5’s 10.3%. It scores two points below Opus 5.5 on GDPval-AA, a test of real-world work across a variety of occupations. And it’s strong on long-horizon work and image understanding—it’s the first Sonnet model to beat Pokémon Red working only from screenshots. Collaboration. Like Opus 5.5, Sonnet 5.5 writes more clearly than our previous generation of models; early testers described it as a better partner for collaboration than Sonnet 5. Its speed also makes it well suited to fast iteration on less complex tasks. Cost. Sonnet 5.5 is priced the same as Sonnet 5 at $2 per million input tokens, $10 per million output tokens, and $0.20 per million tokens for cache reads, but it typically needs far fewer tokens to do the same work. In our testing, it costs up to 30% less per task than its predecessor. Speed. Sonnet 5.5 generates outputs 30%+ faster than Sonnet 5, making it our fastest Sonnet model to date. Alignment and safety. On our automated behavioral audit, Sonnet 5.5 improves on or matches Sonnet 5 on most measures of alignment. Because its cybersecurity capabilities are comparable to Opus 5’s, it’s the first Sonnet model to launch with cyber safeguards and fallbacks like those we’ve developed for our most capable models. Its biology safeguards are the same as Sonnet 5’s. Both safeguards target a narrow set of high-risk requests; routine software development and most life sciences work are unaffected. Performance Sonnet 5.5 improves on Sonnet 5 across domains—in some cases dramatically. On several evaluations, Sonnet 5.5 at Max effort even performs comparably to Opus 5.5. However, benchmark scores capture only one facet of a model’s capabilities; in our own testing, and in that of external testers, Opus 5.5 remains clearly stronger at complex, open-ended work requiring sustained judgment. Sonnet 5.5 Sonnet 5 Opus 5.5 GPT-6 Sol Agentic coding Terminal-Bench 4.0 Agentic coding Terminal-Bench 4.0 70.6% 10.3% 66.4%¹ — Agentic coding FrontierCode 1.1 (Main) Agentic coding FrontierCode 1.1 (Main) 46.2% Max² 42.4% 54.4% 49.3% 52.1% Xhigh Agentic coding CursorBench 4.0 Agentic coding CursorBench 4.0 55.5% 34.1% 57.8% — Knowledge work GDPval-AA v2.1³ Knowledge work GDPval-AA v2.1³ 1844 1449 1846 1487⁴ Knowledge work AA-Briefcase v1.1³ Knowledge work AA-Briefcase v1.1³ 1811 1359 1822 1483⁴ Multidisciplinary reasoning Humanity’s Last Exam Multidisciplinary reasoning Humanity’s Last Exam 64.5% with tools 54.9% with tools 67.7% with tools — Computer use OSWorld 2.1 Computer use OSWorld 2.1 80.1% partial 57.0% partial 81.8% partial — Visual chart recognition Chartography Visual chart recognition Chartography 61.6% no tools 15.6% no tools 64.4% no tools 53.6%⁴ no tools For details on how we run our evaluations, see the Sonnet 5.5 System Card . The charts below plot each model’s score against its cost per task at every effort level. As effort goes up, models typically work for longer, leading to a higher cost per task but generally also a higher score. The closer a point is to the top left of the chart, the more capability it delivers per dollar. On several benchmarks, Sonnet 5.5 at Low or Medium effort beats Sonnet 5’s best score for about a tenth of the cost per task. It complements Opus 5.5 best when running at lower effort settings, where it costs less per task. At higher settings, it can perform comparably at a similar cost. Agentic terminal coding Agentic coding: FrontierCode Agentic coding: CursorBench Knowledge work: AA-Briefcase Agentic terminal coding Agentic coding: FrontierCode Agentic coding: CursorBench Knowledge work: AA-Briefcase Terminal-Bench 4.0 Accuracy vs. cost Sonnet 5.5 Opus 5.5 Sonnet 5 GPT-5.6 Sol 0 10 20 30 40 50 60 70 Score (%) 1 2 5 10 Cost per attempt (USD, log scale) Low Med High Xhigh Max Terminal-Bench 4.0 measures how well a model can complete complex, multi-step professional tasks within a command-line interface. At Medium effort, the default in the Claude apps, Sonnet 5.5 far exceeds Sonnet 5’s best score for less than a tenth of the cost per task. Terminal-Bench and OpenAI did not report GPT-6 Sol performance publicly, so we report GPT-5.6 Sol here. FrontierCode v1.1, main set Accuracy vs. cost Sonnet 5.5 Opus 5.5 Sonnet 5 GPT-6 Sol 30 35 40 45 50 55 0 Score (%) 0.25 0.50 1 2 5 10 20 Cost per task (USD, log scale) Low Med High Xhigh Max FrontierCode measures whether an agent’s code changes would be merged. At High effort, the default on the Claude Platform, Sonnet 5.5 matches GPT-6 Sol’s best score for about a fifth of the cost per task.² CursorBench 4.0 Accuracy vs. cost Sonnet 5.5 Opus 5.5 Sonnet 5 GPT-5.6 Sol 20 30 40 50 60 0 Score (%) 0.50 1 2 5 10 Cost per task (USD, log scale) Low Med High Xhigh Max CursorBench evaluates coding agents on ambiguous, multi-file tasks taken from real Cursor sessions. Sonnet 5.5 at Low effort exceeds Sonnet 5’s best score for less than a tenth of the cost per task. CursorBench 4.0 does not r