#评测/基准

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: http://reflection.ai/beam

Exciting update: There’s a new leader in the Agent Arena among open models. MiMo V2.6 Pro by @XiaomiMiMo has landed the #1 spot with +3.8% net improvement! Earlier this week we evolved the Bash Recovery signal in Agent Arena (more info in thread). With this update, the new top 5 open models are: - #1 MiMo V2.6 Pro: +3.8% - #2 Deepseek V4.1 Flash (Max): +3.0% - #3 Hy4 preview: +2.9% - #4 MiMo V2.6 Flash: +2.5% - #5 GLM 5.2 (Max): +2.4% MiMo V2.6 Pro ($0.11 cost per task) and Flash ($0.05 cost per task) are the only two open models on the Agent Arena Pareto frontier! - Pro outperforms both #2 Deepseek V4.1 Flash while costing 15% less, and #3 Hy4 preview while costing 71% less. - Flash is 1.3 percentage points below Pro but is 55% cheaper. Congrats to the @XiaomiMiMo team on this contribution to the open ecosystem!

Gemini 4 Argon from @Google on ARC-AGI (Verified): - ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness) - ARC-AGI-2: 91.3%, $0.32/task - ARC-AGI-1: 98.5%, $0.16/task Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3.

今天我们发布 Muse Spark Open，这是迄今为止能力最强的美国开放权重模型。 专为编程和智能体工作打造，它擅长长周期任务、复杂软件工程和多步推理。 关键模型规格： → 816B 总参数 / 42B 激活参数 → 1M token 上下文窗口 → 原生支持文本、图像、视频、音频多模态

Gemini 4 Argon from @Google on ARC-AGI (Verified): - ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness) - ARC-AGI-2: 91.3%, $0.32/task - ARC-AGI-1: 98.5%, $0.16/task Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3.

Ling 3.1 Flash running locally beat GPT-6 Luna at LEGO Eiffel Tower 🧱 @AntLingAGI just dropped Ling 3.1 Flash open weights and it crushed GPT-6 Luna on the same @threejs prompt for 2.8x cheaper Run AI models locally -> http://atomic.chat

So @OpenBMB MiniCPM v4.7 35B A3B Comes With a DSPARK Drafter. This Thing SPITS ! 370 tok/s • Total: 3,589 tokens in 10s • Aggregate: 370.0 tok/s sustained · 359.0 wall-avg · 351.8 peak burst • Per-stream decode: 370.0 high / 370.0 low / 370.0 avg tok/s • TTFT 0.30s avg · E2E 10s avg

⚠️ Frontier AI models still fall significantly short of human designers on design creativity. ⚠️ Introducing Design Creativity Bench, a new benchmark evaluating AI models’ creativity on UI design tasks. Despite rapid advances in their ability to generate functional websites and product UIs, leading AI models still struggle to make creative design decisions. We evaluated 16 frontier models across 80 open-ended UI tasks, each adapted to two distinct product domains alongside human designer references. Existing UI benchmarks often measure a single output quality metric. Instead, we measure three dimensions of design creativity: - Originality: How distinct a model's designs are from other models' outputs for the same brief. - Creative Range: How much a model adapts its design decisions when the product domain changes. - Appropriateness: How well the generated UI satisfies the functional requirements of the brief. The results reveal a substantial gap in overall creativity: The best model, @OpenAI GPT-6 Astra, scores 65.5 against a human reference of 92.3. We're publishing the full leaderboard, methodology, and reproducible evaluation framework to make design creativity a measurable dimension of frontier model capability. 📊 Benchmark Leaderboard: https://kombai.com/benchmark/ 📄 Paper: https://arxiv.org/abs/2610.11539 🤗 HuggingFace Datasets: https://huggingface.co/datasets/Kombai/design-creativity-bench 🔗 Github: https://github.com/kombai-io/design-creativity-bench/

SpaceXAI 的 Grok Voice 3.0 是新的领先语音到语音模型，在 Artificial Analysis 语音到语音指数上以 86.9 首次亮相即排名第 1，同时在延迟方面也很有竞争力 Grok Voice 3.0 是 @SpaceXSI 的 Grok Voice 2.0 的继任者，这是一款可通过 SpaceXAI Realtime API 使用的语音到语音模型，价格为每分钟 $0.08。我们通过 SpaceXAI Realtime API 使用 Rex 语音对 Grok Voice 3.0 进行了评估。 关键要点： ➤ 语音到语音指数：Grok Voice 3.0 以 86.9 首次亮相即排名第 1，领先于 GPT-Live-1（Astra，medium）的 83.2、Gemini 3.8 Live Extended Thinking（High）的 82.6 和 GPT-Live-1（Sol，low）的 80.1。它高于 Grok Voice 2.0 的 81.3（+5.6 分）和 Grok Voice 1.0 的 73.7（+13.2 分），后者发布至今还不到六个月。该指数平均了语音推理（Big Bench Audio）、智能体性能（Tau Voice）、Arena 偏好和任务成功率 ➤ 语音智能体 Arena：Grok Voice 3.0 在偏好方面以 Elo 1070 排名第 3，落后于 Gemini 3.1 Flash Live Minimal（1100）和 Gemini 3.8 Live（1088），领先于 GPT-Live-1（Sol，low）的 1057，并以 94.8% 的任务成功率排名第 1，为榜单最高，略高于 Grok Voice 2.0 的 94.6%。Grok Voice 2.0 以 Elo 1011 落后 ➤ Tau Voice：Grok Voice 3.0 在我们实现的 Tau Voice 基准上以 71.6% 排名第 2，落后于 GPT-Live-1（Astra，medium）的 74.5%，领先于 Gemini 3.8 Live Extended Thinking（High）的 68.6% 和 GPT-Live-1（Sol，low）的 59.3%——高于 Grok Voice 2.0 的 56.5% ➤ Big Bench Audio：Grok Voice 3.0 在音频推理上得分 98.5%，领先于 Gemini 3.8 Live Extended Thinking（High）的 97.7% 和 Grok Voice 2.0 的 97.2% ➤ 速度：在 Big Bench Audio 上，Grok Voice 3.0 的平均首次音频时间为 0.81 秒，明显领先于 GPT-Live-1（Sol，low）的 1.24 秒、GPT-Live-1（Astra，medium）的 1.34 秒和 Gemini 3.8 Live Extended Thinking（High）的 1.35 秒，但慢于 Grok Voice 2.0 的 0.70 秒 ➤ 成本：Grok Voice 3.0 的输入音频成本为每小时 $4.80，与 Grok Voice 2.0 相同。这高于 GPT-Live-1（Sol，low）的 $4.47 和 Gemini 3.8 Live Extended Thinking（High）的 $3.50，低于 GPT-Live-1（Astra，medium）的 $5.83，并且比 GPT-Realtime-2.1 High 的 $10.75 便宜约 2.2 倍 祝贺 @elonmusk 和 @SpaceXSI 团队 🚀 更多详情见下方 ⬇️

由 @AIatMeta 推出的 Muse Wonder (Max) 已进入 Code Arena：WebDev 以 1651 分位列第 15，Text Arena 以 1513 分位列第 2！ 在 Code Arena：WebDev 中，Muse Wonder (Max) 以 1651 分位列第 15。这一结果略低于以 1659 分位列第 12 的 Muse Spark 1.3 (Max)，与以 1655 分位列第 14 的 Kimi K3 (Max) 相差 4 分以内，并比以 1640 分位列第 16 的 Grok 4.7 (xHigh) 高 11 分。 此次发布在 Text Arena 中相较过去的 Muse 模型有显著提升。与以 1494 分位列第 11 的 Muse Spark 1.3 (Max) 相比，Muse Wonder (Max) 提升了 +19 分并上升 9 位至第 2。它落后于以 1525 分排名第 1 的 Gemini 4 Argon (High) 12 分。 祝贺 @AIatMeta 团队发布这一版本！