Claude Opus 5.5 登顶 Artificial Analysis 编程智能体指数，但单任务成本更高
Anthropic 的 Opus 5.5 在编程智能体评测拿第一，三项基准都涨了，价格还降了 20%，不过每任务要烧 13 美元 tokens，用前算算账。
Anthropic 的 Claude Opus 5.5 在 Artificial Analysis Coding Agent Index 拿到 66 分，排名第一，比 Opus 5（60 分）高 6 分，比 Claude Fable 5.
1（62 分）高 4 分。三项评测全部提升：Terminal-Bench 4.
0 从 54.5% 升到 63.
1%，DeepSWE v1.1 从 62.
5% 升到 68.4%，SWE-Atlas-QnA 从 62.
1% 升到 66.4%。
价格降到每百万 tokens 输入 $4、输出 $20（Opus 5 为 $5/$25），缓存读取从 $0.50 降到 $0.
20。但单任务成本从 $10.
79 涨到 $13.04，因为每个任务消耗约 1560 万 tokens，输出 tokens 约为 Opus 5 的 2.
4 倍。
Claude Opus 5.5 is the new #1 in the Artificial Analysis Coding Agent Index, with gains across all three evaluations, though at a higher Cost per Task
At max effort in Claude Code, Opus 5.5 scores 66 on the Coding Agent Index, the highest score we have measured. It is up 6 points against Opus 5 (60) and 4 points against Claude Fable 5.1 (62).
Anthropic has cut Opus pricing to $4/$20 per million input/output tokens, from $5/$25 for Opus 5, and cache reads to $0.20 from $0.50. Even with those reductions, Opus 5.5’s Cost per Task is $13.04, above Opus 5’s $10.79, because it uses substantially more tokens.
Key takeaways:
➤ Improves across all three Coding Agent Index evaluations: Terminal-Bench 4.0 rises to 63.1% from 54.5% for Opus 5, DeepSWE v1.1 to 68.4% from 62.5%, and SWE-Atlas-QnA to 66.4% from 62.1%. The largest gain is on Terminal-Bench, at +8.6 percentage points.
➤ The top score comes at the highest Cost per Task: Opus 5.5’s Cost per Task is $13.04, up 21% from Opus 5 at $10.79. It uses about 15.6 million tokens per task against 11.4 million for Opus 5, including about 2.4× as many output tokens.
➤ Extends the Coding Agent Index vs Cost per Task Pareto frontier: No lower-cost model in our comparison matches Opus 5.5's score. It moves the frontier upward at its high-cost end.
Other model details:
➤ Pricing: $4/$20 per million input/output tokens, down 20% from Opus 5. Cache reads cost $0.20 per million, down 60% from $0.50.
➤ Evaluation setup: Claude Code at max effort, measured on DeepSWE v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA. The Coding Agent Index gives each evaluation equal weight.
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison and AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Claude, Anthropic, Claude Code, 编程助手.
Source attribution: curated from Artificial Analysis (@ArtificialAnlys). Cross-source corroboration: 11 independent sources have covered this development (including 宝玉, 向阳乔木, cat), confirming industry-wide signal strength.
