Today we are announcing Harvey LAB-AA v1.1 in collaboration with Harvey. This updates our scoring methodology for the Legal Agent Benchmark (LAB) to add a hallucination check and require correct responses to not include material misstatements. LAB-AA v1.1's new headline metric, Hallucination-Gated All-Pass Rate, only credits a task when the deliverables satisfy every rubric criterion and contain no material hallucinations. We define a material hallucination as one that would mislead a reader on a substantive point, such as a wrong contractually required date, while a minor hallucination is a real error that is unlikely to meaningfully affect the legal interpretation of a deliverable. Minor hallucinations are reported separately and do not affect the headline score. Grok 4.7 (xhigh) leads at 9.4% Hallucination-Gated All-Pass Rate, and >60% of otherwise passing results across the models tested at launch contain a material hallucination. This is the first step in enhancing the methodology for Harvey LAB-AA. In future updates, we’re working with Harvey to better account for the full set of factors lawyers value, including usability features like style and tone. Key takeaways: ➤ Top of the leaderboard: Grok 4.7 (xhigh) from @SpaceXAI leads at 9.4% Hallucination-Gated All-Pass Rate, narrowly ahead of Muse Spark 1.3 (max) from @AIatMeta at 8.9% and GPT-6 Astra (max) from @OpenAI at 8.6% ➤ Hallucinations reshape the leaderboard: without the hallucination gate, Muse Spark 1.3 (max) would lead clearly with a 26.7% all-pass rate, but two thirds of those passes contain one or more material hallucinations, resulting in a Hallucination-Gated All-Pass Rate of 8.9%. GPT-6 Astra (max) retains almost all of its passes after the hallucination gate (8.9% to 8.6%) and moves from joint 10th to 3rd ➤ The GPT-6 model family is the most grounded: GPT-6 Astra (max) averages 0.03 material hallucinations per task (4 across all 120 tasks) and GPT-6 Sol (max) averages 0.07. When comparing six checker models on a 20-task subset, GPT-6 Astra had zero material hallucinations under every checker, including Claude Opus 5.5 (high). By comparison, Gemini 3.8 Flash (high) averages 13.96 material hallucinations per task. ➤ Top-scoring models aren’t the most expensive: Grok 4.7 (xhigh) takes the top spot on the leaderboard at a cost of ~$9.50 per task, under half the cost of Claude Fable 5.1 (max with fallback) at ~$21.70, the most expensive model. Muse Spark 1.3 (max) represents strong performance for its cost, landing in second for ~$4.20 per task Thank you to @nikogrupen and @ItsJulioPereyra from @harvey for their work on Harvey LAB and collaboration.
#评测/基准
Elon Musk@elonmuskAI 评分5151引用Artificial Analysis@ArtificialAnlys
Sherwin Wu@sherwinwuAI 评分4444很高兴看到 LAB 的更新版本!原来的 LAB 结果让我们摸不着头脑。 Astra 的幻觉率是最低的——这在法律实践中尤为重要。
引用Artificial Analysis@ArtificialAnlysToday we are announcing Harvey LAB-AA v1.1 in collaboration with Harvey. This updates our scoring methodology for the Legal Agent Benchmark (LAB) to add a hallucination check and require correct responses to not include material misstatements. LAB-AA v1.1's new headline metric, Hallucination-Gated All-Pass Rate, only credits a task when the deliverables satisfy every rubric criterion and contain no material hallucinations. We define a material hallucination as one that would mislead a reader on a substantive point, such as a wrong contractually required date, while a minor hallucination is a real error that is unlikely to meaningfully affect the legal interpretation of a deliverable. Minor hallucinations are reported separately and do not affect the headline score. Grok 4.7 (xhigh) leads at 9.4% Hallucination-Gated All-Pass Rate, and >60% of otherwise passing results across the models tested at launch contain a material hallucination. This is the first step in enhancing the methodology for Harvey LAB-AA. In future updates, we’re working with Harvey to better account for the full set of factors lawyers value, including usability features like style and tone. Key takeaways: ➤ Top of the leaderboard: Grok 4.7 (xhigh) from @SpaceXAI leads at 9.4% Hallucination-Gated All-Pass Rate, narrowly ahead of Muse Spark 1.3 (max) from @AIatMeta at 8.9% and GPT-6 Astra (max) from @OpenAI at 8.6% ➤ Hallucinations reshape the leaderboard: without the hallucination gate, Muse Spark 1.3 (max) would lead clearly with a 26.7% all-pass rate, but two thirds of those passes contain one or more material hallucinations, resulting in a Hallucination-Gated All-Pass Rate of 8.9%. GPT-6 Astra (max) retains almost all of its passes after the hallucination gate (8.9% to 8.6%) and moves from joint 10th to 3rd ➤ The GPT-6 model family is the most grounded: GPT-6 Astra (max) averages 0.03 material hallucinations per task (4 across all 120 tasks) and GPT-6 Sol (max) averages 0.07. When comparing six checker models on a 20-task subset, GPT-6 Astra had zero material hallucinations under every checker, including Claude Opus 5.5 (high). By comparison, Gemini 3.8 Flash (high) averages 13.96 material hallucinations per task. ➤ Top-scoring models aren’t the most expensive: Grok 4.7 (xhigh) takes the top spot on the leaderboard at a cost of ~$9.50 per task, under half the cost of Claude Fable 5.1 (max with fallback) at ~$21.70, the most expensive model. Muse Spark 1.3 (max) represents strong performance for its cost, landing in second for ~$4.20 per task Thank you to @nikogrupen and @ItsJulioPereyra from @harvey for their work on Harvey LAB and collaboration.
Artificial Analysis@ArtificialAnlysAI 评分4949TechCrunch:AIAI 评分6969 Arena 完成 2 亿美元 B 轮融资,估值 10 个月近翻倍至 31 亿美元
AI 模型评测平台 Arena 完成 2 亿美元 B 轮融资,估值 31 亿美元,由 Lightspeed Venture Partners 和 Khosla Ventures 领投,6 月年化收入达 1 亿美元。
Artificial Analysis@ArtificialAnlys精选AI 评分7575
推荐理由:原文给出 Nano Banana 2.1 在两个图像评测榜上的具体排名、价格和提升幅度,读者可据此比较它与其他模型的性价比。
Aravind Srinivas@AravSrinivasAI 评分4040Perplexity Decider 在 DecisionBench 上排名第一(成本也是最低的)
引用Rohan Goel@rohanatlan@perplexity_ai’s Decider V1.1 ranked #1 on http://decisionbench.ai with lowest cost! 949 shared text cases: 93.9% accuracy · 534 ms median · $0.016 / 1k decisions Thanks @AravSrinivas & perplexity team for building - looks really good, we're going to try it for our product use cases Our latest results live on https://decisionbench.ai https://x.com/rohanatlan/status/2103188107143307541
Artificial Analysis 完整文章精选AI 评分7878 Anthropic 发布 Claude Haiku 5.5,Artificial Analysis 评测得分 43
Anthropic 发布 Claude Haiku 5.5,在 Artificial Analysis Intelligence Index 得 43 分,较上一代 Haiku 一年内提升 26 分,是首个支持 effort 设置与 adaptive thinking 的 Haiku 模型。
推荐理由:原文给出 Haiku 5.5 的跑分、token 消耗与分层定价细节,并指出 token 用量偏高和拒绝率问题,可作为选型参考。
Artificial Analysis@ArtificialAnlysAI 评分5858Vidu Q4 Preview 首次进入 Artificial Analysis AA-Video-I2V v1.0 排行榜第 3 名,比 Vidu Q3 Pro 上升 16 位。

Artificial Analysis@ArtificialAnlysAI 评分4949
Arena.ai@arenaAI 评分5555
TypeSafe AI@typesafeaiAI 评分4343嘿 Jev,根据 @OpenRouter 的决策模型排名,我该用哪个模型?
引用OpenRouter@OpenRouterNew: Decision Model Rankings! See the spend share and token share of different decision models and task types, including Relevance, Correctness, Instruction Following and more. @typesafeai is leading all categories today.
Artificial Analysis@ArtificialAnlys同事件AI 评分7474Anthropic 发布 Claude Haiku 5.5,在 Artificial Analysis 智能指数得 43 分,较上一代 Haiku 一年内提升 26 分。
同一件事,精选收录的是《Anthropic 发布 Claude Haiku 5.5,Artificial Analysis 评测得分 43》
Arena.ai@arenaAI 评分6363引用Claude@claudeai隆重推出 Claude Haiku 5.5:我们迄今为止发布的最便宜、最快、最强大的小型模型。 平均而言,它的运行成本比 Claude Haiku 4.5 低约 75%。
Arena.ai@arenaAI 评分5959引用Arena.ai@arenaMistral Large 4 by @MistralAI just landed in the Code Arena: WebDev at #45. With 1534 pts, this is a +304 pt improvement from Mistral Large 3 at #130! This release also marks a 271-point improvement over its next-best-performing variant, Mistral Medium 3.5, at #122. Mistral Large 4 delivers performance within just 2 points of Claude Opus 4.8 (High) at a nearly 6× lower blended price: $3.47/M vs. $20/M tokens. Nearby but higher-performing models are available for less, keeping it just off the Code Arena: WebDev Pareto frontier. @MistralAI announced its open weights will be released at the end of October. Stay tuned for its score on Arena within open models, and congrats to the team on this release!
Hugging Face 社区博客精选AI 评分7575 Nemotron 系列微调后达到 IOI 2026 与 IMO 2026 金牌水平
NVIDIA Nemotron 团队基于 Nemotron 3,用 SFT、RL 和反馈驱动推理分别构建了在 IOI 2026 与 IMO 2026 达到金牌水平的系统。
推荐理由:原文给出 IOI 与 IMO 金牌级成绩的具体分数、训练配方和开放资源,读者可以复用其模型与推理系统组合思路。
Arena.ai@arena精选AI 评分6868引用Mistral AI@MistralAI认识一下 Mistral Large 4,又名 Le Chonk。 • 1T 参数,原生多模态。49B 激活。 在综合基准测试中,它是来自美国或欧洲的最佳开放权重模型。 • 在关键工作负载上达到最先进水平,包括网络防御、制造业和金融,并且在视觉定位方面超越了闭源前沿模型。 • 端到端在欧洲打造,并可通过我们自己的 Mistral Cloud 基础设施从欧洲部署。 • 今天即可通过 API 向所有人开放。正与网络安全合作伙伴私下合作。 开放权重版本将于十月底发布。
推荐理由:Arena 实测数据显示 Mistral Large 4 的编码得分、排名和价格对比,可帮读者评估其性价比位置。
Rohan Paul@rohanpaul_aiAI 评分4747
引用Voice Arena@voicearena_aiPulse by @smallest_AI currently ranks #1 in the Diarization + ASR track by DER, as the newest entry on the Voice Arena Diarization Bench. The Diarization + ASR track ranks Speaker-Attributed ASR systems that return speaker labels together with the transcript. Seven systems are in it today. Pulse scores 24.4% DER at a strict 0 ms collar. The next system, ElevenLabs Scribe v2, scores 40.7%. That is 1.7x lower error than the next best in the track. Most of the gap is missed speech. For every other system in the track, missed speech is the largest part of the error, above 30% for each of them. Pulse misses 4.4% of reference speech, the lowest in the track. On the full board of 13 systems, dedicated diarization models and Speaker-Attributed ASR systems together, Pulse places 5th. It is the highest-ranked Speaker-Attributed ASR system on the board and sits ahead of two dedicated diarization models. The result holds across settings: In-person recordings : 24.7% DER In online calls : 22.4% DER Congratulations to @smallest_AI 👏 View full results, per track and per condition: http://voicearena.com/diarization-bench
Rohan Paul@rohanpaul_aiAI 评分6262引用shaped@shapedAutomationBench Verified is out. We went through all 600 public tasks in @Zapier's AutomationBench and checked every verifier. • Agents flagged 323 verifiers as suspicious, human review confirmed 206 real bugs, all 206 are fixed. • We replayed 1,235 Kimi K3 runs on the old and fixed verifiers and 344 of them (27.9%) got a different grade • Where verifiers were too strict, pass rate went from 18.8% to 43.8% • Where they were too lenient, it dropped from 60.2% to 49.7% Audit and dataset links below.
Arena.ai@arenaAI 评分6363引用Google@GoogleMeet Nano Banana 2.1, our latest image generation and editing model 🍌 This upgraded version outperforms our previous models across the board, with notable leaps in visual design, mask-based editing, and subject consistency to help you create more natural-looking images.
Arena.ai@arenaAI 评分6363引用Google@GoogleMeet Nano Banana 2.1, our latest image generation and editing model 🍌 This upgraded version outperforms our previous models across the board, with notable leaps in visual design, mask-based editing, and subject consistency to help you create more natural-looking images.
Artificial Analysis 完整文章精选AI 评分6666 Mistral 发布 Mistral Large 4,Artificial Analysis 评测称其为美中之外最智能模型
Mistral 发布 Mistral Large 4(Research Public Preview),在 Artificial Analysis Intelligence Index 得分 38,与 GPT-6 Luna(max, 38)相当,为美中之外最智能模型,计划 10 月底开源 1T 参数(49B 激活)权重。
推荐理由:原文给出 Intelligence Index、Cyber Index、定价与上下文窗口等具体数据,读者可据此评估 Mistral Large 4 的真实水平与成本。
Microsoft Research 博客AI 评分4545 Microsoft 研究员 Jennifer Neville 谈 AI 评测的意外失败与真实世界表现
Microsoft 合伙人研究经理 Jennifer Neville 在 Microsoft Research Podcast 中讨论了评测如何推动 AI 系统突破传统 benchmark 的性能边界,以及模型在超出常规测试时出现的"意外失败"。她结合自身从数学、物理、认知科学到计算机科学的经历,分享了使用当前 AI 系统的实用建议,并强调当结果偏离预期时深入检查数据的重要性。
ARC Prize@arcprizeAI 评分4040Grok 4.7 在 ARC-AGI-2 semi-private 任务的中、高、xhigh 推理档位上平均消耗的 reasoning token 均多于 Grok 4.6,导致每任务成本更高。
ARC Prize@arcprize精选AI 评分6767推荐理由:ARC Prize 官方实测数据同时给出分数和每任务成本,读者可以据此比较 DeepSeek V4.1 Flash 与上一代的性价比取舍。
Arena.ai@arena精选AI 评分6767引用Mistral AI@MistralAI认识一下 Mistral Large 4,又名 Le Chonk。 • 1T 参数,原生多模态。49B 激活。 在综合基准测试中,它是来自美国或欧洲的最佳开放权重模型。 • 在关键工作负载上达到最先进水平,包括网络防御、制造业和金融,并且在视觉定位方面超越了闭源前沿模型。 • 端到端在欧洲打造,并可通过我们自己的 Mistral Cloud 基础设施从欧洲部署。 • 今天即可通过 API 向所有人开放。正与网络安全合作伙伴私下合作。 开放权重版本将于十月底发布。
推荐理由:原文说明 Agent Arena 用真实长程智能体任务和因果追踪方法评分,读者可据此理解该评测如何衡量模型表现。
ElevenLabs@ElevenLabsAI 评分5555引用Artificial Analysis@ArtificialAnlysElevenLabs' Eleven v4 Turbo takes the #1 spot on the Artificial Analysis Provider Voice TTS Arena Leaderboard, ahead of Eleven v4 while costing half the price Eleven v4 Turbo is the faster, lower-cost sibling of Eleven v4 from @ElevenLabs, designed to deliver comparable speech quality with lower latency and cost. Key takeaways: ➤ Provider Voice: Eleven v4 Turbo has an Elo of 1,334 (+19/-19) across 1,645 appearances, placing it ahead of Eleven v4 at 1,321, Alibaba's Qwen-Audio-3.1-TTS-Plus at 1,292 and Cartesia's Sonic 3.6 at 1,278. It ranks #1 in Customer Service and Entertainment and #2 in Assistants and Knowledge Sharing. ➤ Controlled Voice: Eleven v4 Turbo ranks #2 with an Elo of 1,173 (+15/-15) across 1,745 appearances, behind Qwen-Audio-3.1-TTS-Plus at 1,181 and ahead of Eleven v4 at 1,160. It ranks #1 in Arabic, German, Hindi and English (UK). ➤ Pronunciation Robustness: Eleven v4 Turbo scores 90.1%, ranking #2 behind Eleven v4 at 91.7% and ahead of Gemini 3.8 Flash TTS at 89.5%. ➤ Pricing: Eleven v4 Turbo is priced at $40/1M characters, half the price of Eleven v4 at $80/1M characters, compared to Sonic 3.6 at $49/1M characters and Gemini 3.8 Flash TTS at $16.49/1M characters. ➤ Speed: Eleven v4 Turbo processes 96 characters per second of generation time, compared to 84 characters per second for Eleven v4 and 59 for Eleven v3. See more details and listen to samples below 🧵
Artificial Analysis@ArtificialAnlys同事件AI 评分7171同一件事,精选收录的是《Mistral 发布 Mistral Large 4,Artificial Analysis 评测称其为美中之外最智能模型》
Artificial Analysis@ArtificialAnlysAI 评分6464
Artificial Analysis@ArtificialAnlysAI 评分4848
Artificial Analysis@ArtificialAnlysAI 评分5454Artificial Analysis 语音 TTS Arena 榜单中,ElevenLabs 的 Eleven v4 Turbo 以 Elo 1334 位列 Provider Voice 第一。

Gemini Notebook@Gemini_NotebookAI 评分4747引用a16z@a16zThe seventh edition of our Top 100 Consumer AI Apps is here. New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings. Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else. In today's edition: - ChatGPT still holds the throne, now with 1B+ monthly actives on mobile - Claude has climbed to #3 on web with nearly 1B monthly visits - The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud) Full breakdown from @omooretweets: https://www.a16z.news/p/top-100-consumer-ai-apps-seventh
MeshyAI@MeshyAIAI 评分4545引用a16z@a16zThe seventh edition of our Top 100 Consumer AI Apps is here. New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings. Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else. In today's edition: - ChatGPT still holds the throne, now with 1B+ monthly actives on mobile - Claude has climbed to #3 on web with nearly 1B monthly visits - The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud) Full breakdown from @omooretweets: https://www.a16z.news/p/top-100-consumer-ai-apps-seventh