做 AI 智能体开发的团队终于有了真实任务驱动的评测基准——Fable 5 在 30 万任务中碾压对手，值得关注其强执行与弱引导的权衡。
Claude Fable 5 在全新 Agent Arena 排行榜上以最大优势超越 Opus-4.8 和 GPT-5.5，排名第一。该排行榜基于 30 万+真实任务、200 万+工具调用和 4000 万行代码评估，衡量模型在任务成功率、用户表扬/抱怨比等关键信号上的表现。Fable 5 在可完成任务上表现极佳，但可引导性较弱。Agent Arena 提供网页搜索、文件系统和终端工具，让模型完成编写代码、制作幻灯片、研究网页等复杂工作流。
Exciting news: Claude Fable 5 ranks #1 on the new Agent Arena leaderboard! Fable 5 leads by the wid...
Exciting news: Claude Fable 5 ranks #1 on the new Agent Arena leaderboard! Fable 5 leads by the widest margin ever over Opus-4.8 and GPT-5.5 on two key signals: confirmed task success rate and praise vs. complaint, despite weaker steerability. If Fable can do something, it will do it very well. If it can't/doesn't want to do something, it may be hard to steer the model towards the goal. In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks. Models get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide deck, researching the web, building apps, and analyzing documents. We use the causal tracing methodology to measure a model's net improvement which indicates how much it improves outcomes relative to the average model. Huge congrats to @AnthropicAI for the incredible milestone! Below we break down how Claude Fable 5 (based on Mythos) scored across 5 signals, drawn from tasks submitted by a global community of users. Arena.ai @arena Introducing Agent Arena: real-world agentic evals at scale. How do you evaluate agents doing actual work? We measure millions of live sessions where real users accomplish real tasks. On Arena, models now get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide deck, researching the web, building apps, and analyzing documents. Every session produces rich signals. Users iterate with the agent turn-by-turn: approving, editing, correcting, praise or expressing frustration. The environment gives feedback too: shell errors, tool failures, recovery attempts, and more. Our leaderboard measures each model's agentic performance using causal inference across five signals: task success, steerability, error recovery, user praise vs. complaint, and tool hallucination. This leaderboard snapshot is built from 300K+ tasks, 2M+ tool calls, and 40M lines of code by agents. Top labs in Agent Arena: - #1 @OpenAI : GPT-5.5 (High) - #2 @AnthropicAI : Claude-Opus-4.7 (Thinking) - #3 @Zai_org : GLM-5.1 - #4 @GoogleDeepMind : Gemini-3.1-Pro - #5 @Kimi_Moonshot : Kimi-K2.6 More analysis in the thread, with the full technical blog below. 🔗 View Quoted Tweet 💬 3 🔄 6 ❤️ 36 👀 2868 📊 8 ⚡