#评测/基准

Kimi K3.1 Pro 在 Artificial Analysis 智能指数上以 55 分位列第四，在智能体性能上大幅提升；一旦其权重发布，它将成为领先的开源权重模型 @Kimi_Moonshot 的新推理模型比 Kimi K3（max，44）提升了 11 分。在智能指数上，它仅落后于 Claude Opus 5.5（max with fallback，58）、Claude Sonnet 5.5（max with fallback，56）和 Muse Wonder Full（max，56）。 一旦 K3.1 Pro 的权重发布，它将成为得分最高的开源权重模型，智能指数上领先开源权重模型与专有模型之间的差距将从 12 分缩小到 3 分。 关键结果： ➤ 智能体性能大幅提升：Kimi K3.1 Pro 在 GDPval-AA 上以 1,846 Elo 位列第三，比 Kimi K3 提升 312 Elo，仅次于 Claude Opus 5.5（1,866 Elo）和 Muse Wonder Full（1,857 Elo）。Kimi K3.1 Pro 还在 Terminal-Bench 4.0 上取得了所有模型中的最高分。K3.1 Pro 得分为 67%，高于 Kimi K3 的 13%，并领先于 Claude Sonnet 5.5（64%）。测试智能体 SaaS 工作流的 AutomationBench-AA 从 58% 升至 73%。 ➤ 其他评估的提升较为温和：Humanity's Last Exam 从 47% 升至 52%，CritPt 从 23% 升至 25%，SciCode 从 59% 升至 61%。AA-Omniscience 指数从 20 升至 30，幻觉率从 53% 降至 44%，准确率从 48% 升至 51%。 ➤ 每任务成本与 Kimi K3 大致相当，为每任务 1.98 美元：K3.1 Pro 在每个智能指数任务上使用 55K 输出 token，比 Kimi K3 的 48K 多 14%，而其第一方 token 价格低 33%。 更多模型细节： ➤ 上下文窗口：1M token。 ➤ 规模：总参数 2.8T，激活参数 104B ➤ 输入模态：文本和图像 ➤ 权重与许可证：权重目前未发布，预计 Moonshot 将在发布后不久公布权重 ➤ 定价：每 1M 输入/输出/缓存命中 token 分别为 2/10/0.20 美元，三项费率均比 Kimi K3 低 33%。 祝贺 @Kimi_Moonshot 团队发布！

Big news: @Kimi_Moonshot is back in the top-4 labs in the Code Arena: WebDev with the release of Kimi K3.1 Pro! This model just landed #8 in the Code Arena: WebDev with 1692 pts, matching Claude Opus-5 (Max) at 70% lower cost. This is a 9 place jump (+37 points) from Kimi K3 (Max) at #17 with 1655 pts. Kimi K3.1 Pro improved from Kimi K3 across all categories. Highlights: - #7 in Brand & Marketing (up from #9) - #9 in Simulations (up from #20) Cost efficiency is another upgrade. Priced at $2/$10 per million input/output tokens, Kimi K3.1 Pro (Max) has a median cost per task of $1.23 (29% less than Kimi K3). See its placement compared to the Pareto frontier in thread. With open weights coming soon, Kimi K3.1 Pro would be the new #1 open model in the Code Arena: WebDev, overtaking DeepSeek-v4.1-Flash. Stay tuned! See @Kimi_Moonshot’s ranking by lab, and 3D generations from Kimi K3.1 Pro below in thread. Huge congrats to the @Kimi_Moonshot team on this release!

隆重推出 Kimi K3.1 Pro：我们能力最强的 3T 级开放权重模型。 🔹 1M token 上下文，2.8T 总参数 / 104B 激活参数（MoE），多模态。 🔹 全项目级编程：可在 Unreal、Unity 和 Godot 中开发游戏。 🔹 擅长在 Blender 和 CAD 中进行参数化 3D 建模。 🔹 在金融工作上更可靠：建模准确，幻觉降低 65%。 🔹 在编程、智能体和知识基准测试中，与最强的闭源模型不相上下。 Kimi K3.1 Pro 和 K3.1 Pro Highspeed（快 2–3 倍）现已在 http://Kimi.ai、Kimi Work、Kimi Code 和 Kimi API 上线。 开放权重将于 2026 年 11 月 4 日发布。 🔗 API：http://platform.kimi.ai 🔗 技术博客：http://kimi.ai/blog/kimi-k3-1-pro

Introducing five new languages on the Voice Arena TTS leaderboard. Models from @ElevenLabs rank #1 on all five of the new Voice Arena TTS leaderboards: French 🇫🇷, German 🇩🇪, Bahasa 🇮🇩, Tagalog 🇵🇭 and Indian English 🇮🇳. Eleven v4 Turbo takes the top position on four of the five boards. On Bahasa, ElevenLabs holds both #1 and #2, with Eleven v4 first and Eleven v4 Turbo second. Across five languages spanning Europe, Southeast Asia and South Asia, each judged only by native listeners, ElevenLabs finishes first on every board, ahead of the next-best model from another provider by 11 to 26 Elo points. Voice Arena ranks models through blind, head-to-head votes by vetted native speakers, scored with Bradley-Terry Elo. Board-by-board results below 🧵

Announcing Artificial Analysis Coding Agent Index v2.0: FrontierCode v1.1 from Cognition replaces DeepSWE, SWE-Atlas-QnA grading is revised, and Claude Opus 5.5 in Claude Code leads at 61.7 Today we are launching v2.0 of the Coding Agent Index. The Index evaluates models together with their agent harnesses, now across 290 tasks covering software engineering, terminal use, and repository Q&A, with the three benchmarks weighted equally. Changelog (Index v1.5 → Index v2.0): → FrontierCode v1.1 replaces DeepSWE v1.1. FrontierCode is a benchmark from @cognition with a private test set: 100 software engineering tasks on real open-source repositories, graded on whether a maintainer would merge the change. An attempt that fails a blocking check scores zero, and one that passes them all scores between 70% and 100% depending on the non-blocking checks it meets. We also scan each trajectory with a modified version of Cognition's rule-based scanner, and any attempt flagged for accessing solution material scores zero. → Revised SWE-Atlas-QnA grading, now SWE-Atlas-QnA-AA. Scale AI's 124 repository Q&A tasks are unchanged. We reworded 127 of the 1,306 rubric criteria for more consistent verdicts and moved to GPT-6 Luna (xhigh) as the judge, with five votes per criterion. The dataset and judge configuration are published on Hugging Face. Key results: ➤ Claude Opus 5.5 leads the Index: Claude Code with Claude Opus 5.5 (max) scores 61.7, followed by Claude Code with Claude Sonnet 5.5 (max) at 59.3, Muse Code with Muse Wonder Full (max) at 58.9 and Claude Code with Claude Fable 5.1 (max with fallback) at 57.6. ➤ FrontierCode v1.1 is where Opus 5.5 pulls ahead: Opus 5.5 scores 56.0% on FrontierCode, ahead of Codex with DeepSeek V4.1 Flash (max) at 52.6%, Codex with GPT-6.1 Sol (max) at 49.4% and Codex with GPT-6 Astra (max) at 49.3%. Below the top, FrontierCode scores are tightly packed: 15 of the 24 configurations on the Index score between 43% and 49%. ➤ SWE-Atlas-QnA-AA grading is stricter and more consistent: across the 21 configurations evaluated on both Index versions, the mean score falls 1.7 points, from 58.8% to 57.1%, with 14 of 21 moving down. Individual configurations move by up to 7 points: Muse Code with Muse Spark Open (xhigh) falls 7.0, Kimi K3 in Kimi Code CLI and Codex with GPT-6 Luna (max) 6.2, and Muse Code with Muse Wonder (max) 5.6, while Codex with GPT-6.1 Sol (max) gains 2.1. ➤ Four configurations sit on the cost frontier: Codex with DeepSeek V4.1 Flash (max) scores 47.3 at $0.39 per task, Codex with GPT-6.1 Sol (max) 54.1 at $1.50, Codex with GPT-6 Astra (max) 54.7 at $7.04 and Claude Code with Claude Opus 5.5 (max) 61.7 at $11.42. Costs are pay-per-token API costs, not subscription prices. We will keep updating the Coding Agent Index incrementally, with more evaluations and agents to come.

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5. This is a 17-place jump from Kimi-k2.6 (#18 -> #1). In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5. The full model weights will be released by July 27. Congrats to the @Kimi_Moonshot team on this major milestone!

today, Krea 3 makes it to the top of image arenas. - "Fast" pushes the pareto curve of speed and quality. - "Ultra" ranks top-4, only surpassed by OpenAI models. the only company valued under $1T in the top-10! more below 👇

MiniMax M3.1 Flash is now #1 on our search capabilities leaderboard for efficiency. It delivers web research performance with Parallel Search similar to DeepSeek V4.1 Flash (1017) at ~3x lower cost.

Exciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs! This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts). Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google). Across categories, Muse Wonder Full (Max) landed: - #4 in Data & Analytics and in Consumer Product - #5 in Brand & Marketing - #6 in Content Creation Tools - #7 in Gaming and in Simulations - #8 in Reference-Based Design Congrats to the @AIatMeta team and @alexandr_wang on this release!

Today we’re releasing ClaimBench, our new benchmark targeted towards decision models. It tests whether a model can tell if a financial document supports, contradicts, or doesn’t address a claim. We evaluated 15 models on 1,960 claims paired with 10-K excerpts, with Decider V1.2 leading the pack of decision models.

Update to the Agent Arena Pareto frontier: GPT-6 Luna (Max) has landed on the Agent Arena Pareto frontier with +1.59% net improvement at a median cost of just $0.05 per task! Luna costs 29% less than DeepSeek-V4.1-Flash and 76% less than Hy4 preview, while trailing their net-improvement scores by just 2.27 and 2.51 percentage points respectively. Compared to other GPT models on the Pareto frontier, GPT-6 Luna (Max) costs 94% less than GPT-6 Sol (Max), and 98% less than GPT-6 Astra (Max). Congrats to the @OpenAI team on this cost-efficient release!

隆重推出 FLUX 3 Max，这是 fal Research 与 @bfl_ai 合作推出的最新后训练视频模型。 在独立第三方基准测试中，FLUX 3 Max 在图生视频质量上并列第一，可在约 12 秒内生成一段 10 秒、720/768p 的视频。 在接下来的三周内，FLUX 3 Max 享受 75% 折扣，使其成为同等质量水平下最实惠的前沿视频模型。