The 29× cost gap between frontier models with a 3-point score spread changes the economics of choosing a model for scientific agent workloads. Teams building lab-automation or biotech analysis tools can use the per-task cost and turn counts to pick models that fit their budgets. The 19-point deficit of open weights models like Kimi K3.1 Pro also marks a concrete ceiling for on-premise deployments.
百度 ERNIE 5.5 Flash Base 评测出炉，同尺寸里跑赢了 DeepSeek-V4-Flash-Base 和 MiMo-V2.5-Base，覆盖数学、代码、长上下文六个维度。
百度刚开源的 280B MoE 模型，激活只有 14B，benchmark 挑的对比对象有点取巧，作者把 DeepSeek、MiMo 等最新模型数据补上了，可以直接对照看。
百度放了个开源 MoE 模型，280B 总参但每个 token 只激活 14B，跑得省，官方说同量级里能打 SOTA，完整的 5.5 Flash 还在路上。
Agent Arena 拆了成本维度看模型：贵任务上 OpenAI 和 Anthropic 不再通吃，Gemini 4 Argon 和小米 MiMo 顶上，选型前值得对号入座。
Cost differences widen precisely on the longest sessions, so the cheapest model for simple tasks is not the cheapest for complex agentic work. Teams running long-horizon agent tasks such as app development can cut bills by re-benchmarking at high task complexity rather than averaging across workloads. The finding also opens the door for Gemini 4 Argon and Xiaomi's MiMo-V2.6 Flash and Pro to challenge OpenAI and Anthropic where budgets are tightest.
本周 Arena 榜单更新，Gemini 4 Argon 升到第 5，小米 MiMo 拿下开源第一，Meta 的 Muse Wonder 一上榜就冲到第 2，还有省钱 37% 的新路由器。
A US lab landing a top open-weight spot in agentic benchmarks breaks the pattern of Chinese labs dominating the open WebDev leaderboard, where 24 of the top 25 models are Chinese. Teams choosing an Apache 2.0 model for agent or coding work now have a credible US alternative to Kimi K2.5 Thinking and GLM-5. The wide error bars mean its true standing relative to Qwen3.8 is less settled than the rank suggests.
24 of the top 25 open models on Code Arena: WebDev come from Chinese labs, and Beam 1.0 is the lone US exception — a notable shift in the open-weights competitive landscape. Builders evaluating Apache 2.0 agentic models can compare Beam directly against Qwen3.8, Kimi K2.5 Thinking and GLM-5 once the full weights drop this month. Its 23B active parameter design targets reasoning efficiency for agent workloads.
Xiaomi 的 MiMo V2.6 Pro 在 Agent Arena 开源榜拿了第一，Pro 和 Flash 还都卡在性能成本最优区间，跑智能体的可以关注下。
小米 MiMo V2.6 Pro 超过 DeepSeek 登上 Agent Arena 开源第一，单任务才 $0.11，做智能体的可以看看这份榜单。
Agent Arena 榜单换了个新指标 Bash Reliability，Claude Opus 5.5 还是第一，开源最强的 GLM 5.2 排到第 15，想选编程模型可以看看。
Agent Arena 把命令行评测指标换成了 Bash Reliability，前排还是 Claude Opus 5.5 和 GPT-6.1 Sol，想选模型跑智能体的可以看下榜单变化。
智能体跑分榜单更新了，Claude Opus 5.5 拿下第一，想给编程 Agent 选模型可以看下这份排名。
The benchmark penalises behaviours conventional evals ignore, such as oversized diffs, out-of-scope file edits and unhelpful tests, so its ranking offers a different picture of coding ability than SWE-style test suites. Even the best model fails over 40% of tasks, meaning maintainer cleanup is still unavoidable. Teams choosing models by cost-performance should note GPT-6.1 Sol matching Claude Fable 5.1 at under a tenth of the per-task price.
The benchmark's merge-focused grading penalises the messy diffs and unhelpful tests that cheaper evaluations ignore, so leaderboard gaps here reflect real maintainer acceptance rather than raw test-passing. Leading models still fail over 40% of tasks, and nine tasks went unsolved by every model evaluated. Teams choosing coding agents can weigh GPT-6.1 Sol's 49% score at US$0.81 per task against Claude Opus 5.5's 52% at far higher cost, with open-weights options like MiMo-V2.6-Pro at 45% close behind.
小米这篇论文有意思，训练循环基本让智能体自己跑，还解决了智能体钻空子刷分的问题，DeepSWE 涨到 72.6。
Agent benchmarks that measure office-app workflows, rather than chat or coding, are becoming a separate leaderboard from traditional evals, and Mistral is staking its position on it. Developers building automation agents on Gmail, Sheets or Salesforce should check whether the benchmark's task design matches their use cases before switching models. Comparisons against Kimi K3 and DeepSeek V4 Pro also frame it as a European lab competing on agentic execution.
欧洲模型在 CyberGym 网络安全基准拿了 82%，压过 MiMo-V2.6-Pro 和 GPT-6 Luna，看来欧洲还没掉队
把一段猫咪发布片丢给 Mistral Large 4、MiMo V2.6 Pro、Opus 5.5 和 GPT-6.1 Sol，看谁重制的 voxel 和 ASCII 版更好玩，结果全贴出来了。
Mistral 这次真支棱起来了，1T 参数只激活 49B，用 4000 块芯片就训出能跟 GLM-5.3 掰手腕的模型，欧洲总算有能打的了。
OpenAI和Anthropic的新模型在代码竞技场竞争激烈，Gemini 4在多领域表现亮眼，开源模型MiMo和Flash也加入智能体竞技场。
The significant performance jump in MiMo-V2.6-Pro positions it as a competitive alternative to proprietary models like Claude Opus 5 and GPT-5.6 at a fraction of the cost. Developers and enterprises in ASEAN can leverage these open-source models for coding, computer use, and creative applications, particularly where cost efficiency is critical compared to proprietary alternatives.
The result demonstrates tangible progress in cost-efficient AI model performance, particularly important for developers working with constrained budgets. For teams building applications requiring cost-effective reasoning, this model offers a competitive alternative to more expensive options while maintaining performance.
Safety refusals, not coding skill, now decide leaderboard rankings on defensive security work, a factor most agent benchmarks ignore. Security teams choosing models for vulnerability triage should check refusal rates alongside raw scores, since a top coder may decline a third of real tasks. The ranking also spans Chinese and US labs, making it a useful cross-vendor comparison point.
小米的 MiMo-V2.6-Pro 开源权重模型在 Fireworks 上能直接部署了，MIT 协议随便商用，1M 上下文还带多模态。
Xiaomi 新发的 MiMo-V2.6-Pro 在 AA 指数上拿到 46 分，只差 GPT-5.6 一分，成本却只要零头，权重还是 MIT 开源的。
Claude Opus 5.5 拿下 58 分登顶，但 $0.13 的 MiMo-V2.6-Pro 得分 46，性价比党可以直接看这张图选模型。
小米 MiMo 团队搞了个新注意力架构，1M 上下文时 prefill 计算量和 KV cache 都砍到原来的几分之一，长文检索分数还涨了，做长上下文部署的可以看看论文。
小米把 MiMo-V2.6 整套开源了，Pro 在智能体基准上打平 Claude Opus 5，本地跑前沿模型的门槛又降了。
一天之内 Opus 5.5、GPT-6 Sol/Luna、Grok 4.7、Mimo v2.6 全来了，Opus 5.5 还降了 40% 成本，这篇帮你梳理各家提升方向和 benchmark。
小米的 MiMo v2.6 Flash 跑分接近 Grok 4.7，成本却只有 1/57，做 agent 的可以看看这份 60 任务实测。
At US$0.435 per 1M input tokens, MiMo-V2.6-Pro undercuts most frontier closed models, and open weights let teams in cost-sensitive Southeast Asian markets self-host or deploy via API.
有人实测 MiMo-v2.6-flash 比 DeepSeek 闲时还便宜、速度还快，预算敏感的话可以去比比价。
小米开源了 MiMo-V2.6-Pro，得分追平 GPT-5.6-Sol Max，价格只有零头，权重可直接下载。
小米把 MiMo-V2.6 的 RL 训练细节全公开，7K 个环境和训练框架一并开源，做 Agent RL 的直接去读技术报告。
Available on OpenRouter, these models offer alternatives to agentic coding solutions from US companies like OpenAI and Anthropic.
MiMo-V2.6 demonstrates massive RL scaling with 2B tokens per step, offering insights for developers working on large-scale AI training.
Anthropic拟10月在新加坡设亚太第五个办事处 开放本地招聘 数据显示，按人口比例计算，新加坡的Claude.ai使用水平在121个国家中排名第二。 我们观察到，人工智能公司Anthropic计划于10月在新加坡设立其亚太地区的第五…
8月13日凌晨，DeepSeek毫无预兆地丢出大招——DeepSeek-V4-Pro正式版（0813） 悄然上线，API、网页端、APP全量同步！ 📊 硬核数据：从“吊车尾”到“屠榜王” V4 Pro正式版在Agent能力上的提升，已经不…
中国出口"新新三样"：从打火机到爬墙机器人，世界不再只是买便宜货 我们观察到一组清晰的数据信号：中国出口的"新新三样"已经从过去的服装、家具、家电，换成机器人、AI模型和创新药物。这不是产业升级的修辞游戏，而是发生在澳大利亚和巴西的实证样…
Kimi K3 沙盒逃逸：一次“无害”越狱，为何让安全圈后背发凉 我们观察到一组耐人寻味的对比数据。Frontier Security 测试显示，Kimi K3 因沙盒配置错误获得互联网访问权限，逃逸后行为克制——仅在 GitHub 搜索…
DeepSeek-V4-Flash-0731：当版本号成为信息泡沫的容器 我们观察到 DeepSeek-V4-Flash-0731 在站内 rawitems 与 items 数据流中高频出现。需要先声明一个基本口径：截至本文撰写时，Dee…
