Anthropic 发布 Claude Haiku 5.5，Artificial Analysis 公布评测结果
Anthropic has released Claude Haiku 5.5 | Artificial Analysis
Anthropic 发布 Claude Haiku 5.5，这是首个带 effort 设置与自适应思考的 Haiku 模型，并引入分层定价：100k token 以内为 $0.10/$0.50 每 1M 输入/输出 token，超过 100k 后升至 $0.50/$2.50。Artificial Analysis 评测显示，Haiku 5.5（max）在小型模型中略领先 GLM-5.3 Flash（42）、Gemini 3.8 Flash（41）和 GPT-6 Luna（38），但落后 Claude Sonnet 5.5（max，56）13 分；其上下文窗口从 Claude 4.5 Haiku 的 200k 提升到 1M token。该模型每个 Intelligence Index 任务约消耗 162k 输出 token，约为 GPT-6 Luna（max，约 50k）的 3 倍，Terminal-Bench 4.0 得分 33%（Haiku 4.5 为 0%）。
推荐理由
AI 评分 74
第三方评测给出 Haiku 5.5 在小型模型中的位置、token 消耗与分层定价，便于判断其成本与适用场景。
主题
正文
当前显示原文，中文全文译文尚未就绪。
All articles
October 7, 2026
Haiku 5.5 is the first Haiku model with Anthropic’s effort settings and adaptive thinking, and Anthropic has introduced tiered pricing.
Haiku 5.5 is cheaper than its predecessor - it costs $0.10/$0.50 per 1M input/output tokens for prompts up to 100k tokens (the same as GPT-6 Luna and 10% of the previous Haiku model). However, this pricing rises 5x to $0.50/$2.50 above 100k. The site does not yet reflect tiered pricing, so provisional cost figures for Haiku 5.5 do not include the step up cost. We are working on support and will follow up with Cost per Task coverage soon.
Key takeaways:
➤ Leading small-class model performance: At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38). Its score is comparable to Kimi K3 (44), a 2.8T parameter open weights model, and trails Claude Sonnet 5.5 (max, 56) by 13 points
➤ Heavy token use compared to GPT-6 Luna: Haiku 5.5 (max) uses ~162k output tokens per Intelligence Index task, ~3x GPT-6 Luna (max, ~50k). Moving from xhigh to max adds 2 points for ~1.8x the tokens. At similar intelligence it also uses more tokens than GPT-6 Luna: Haiku 5.5 (high) scores 38 with ~55k tokens per task against 38 with ~50k for Luna (max), and the gap widens at lower effort settings
➤ Highly capable at agentic knowledge work: on AA-Briefcase, our private evaluation for realistic knowledge work tasks, Haiku 5.5 (max) reaches 1578 Elo, ahead of models including Kimi K3 and GLM-5.3, and comparable to Muse Spark 1.3 (max)
➤ Improvements on terminal use: on Terminal-Bench 4.0 it scores 33%, up from 0% for Haiku 4.5. This is level with GLM-5.3 Flash, and ahead of Gemini 3.8 Flash (20%) and GPT-6 Luna (13%)
➤ Lower factual knowledge, but relatively low hallucinations: as expected for a smaller-class model, Haiku 5.5 has lower factual knowledge than its siblings. AA-Omniscience accuracy is 36%, against 55% for Gemini 3.8 Flash and 44% for GPT-6 Luna, but this is partly driven by more willingness to admit when it doesn’t know - its hallucination rate is lower, at 40% against 55% and 77%
➤ AutomationBench-AA result likely understated: Haiku 5.5 scores 35%, against 53–60% for GPT-6 Luna, Gemini 3.8 Flash and GLM-5.3 Flash. During pre-release testing, a safety refusal issue caused the model to over-refuse. Anthropic is working on resolving this - we will re-run this evaluation with the fix, and expect this score to rise
Other model details:
➤ Context window: 1 million tokens, up from 200k for Claude 4.5 Haiku
➤ Pricing: $0.10/$0.50 per 1M input/output tokens up to 100k tokens, $0.50/$2.50 above. Cache reads $0.01 ($0.05 above 100k), 5 minute cache writes $0.125 ($0.625 above 100k)
➤ Multimodality: Text and image input, with text output
Claude Haiku 5.5 (max) uses ~162k output tokens per Intelligence Index task, more than Opus 5.5 (max) and ~3x GPT-6 Luna (max). Across effort settings, Haiku 5.5 uses more output tokens than GPT-6 Luna to reach similar Intelligence Index scores
Claude Haiku 5.5 (max) reaches 1578 Elo on AA-Briefcase, our private frontier knowledge work evaluation, within the confidence intervals of GPT-6 Astra (max) and Claude Fable 5.1 (high)
Full breakdown of the individual evaluations in the Artificial Analysis Intelligence Index for Claude Haiku 5.5 across effort settings
Artificial Analysis Decision Index v1.0
The Artificial Analysis Decision Index benchmarks decision models, a new category of models that answer structured decisions in a single call with a probability for each option. Version 1.0 combines four evaluations across classification, tool calling, knowledge, hallucination, and reasoning.
October 13, 2026
Announcing AA-3D: a benchmark for AI models building interactive 3D experiences
AA-3D is a new benchmark of how well AI models build interactive 3D experiences. Each model turns a written brief into a working three.js scene in an offline sandbox, and our recruited panel compares the scenes in blind pairwise votes.
October 12, 2026
Announcing FrontierCode on Artificial Analysis: can AI models write code maintainers would merge?
Cognition's FrontierCode 1.1 tests whether AI models make code changes that maintainers of real open source repositories would merge. Leading models still fail more than 40% of the time.
October 12, 2026
Artificial Analysis
Get notified about new articles
About; Methodology; Contact; Articles; Careers