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。
百度把 280B 的 MoE 基座权重直接放出来了，激活才 14B，FP8 和 BF16 都有，做微调或强化学习研究可以直接拿这个当起点。
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.
AA 换了 v5.0 测评算法，国产模型排名集体下滑，连 Claude 家最便宜的 Haiku 都跑赢了一票国产旗舰，这篇把能力、价格、速度三张榜单都对比了一遍，选模型前可以看看。
小米的 MiMo-V2.6-Pro 进了腾讯 WorkBuddy 和 TokenHub，1M token 上下文，API 兼容 OpenAI 和 Anthropic 格式，接项目成本低。
小米把开源第一的 MiMo-V2.6-Pro 接进了 WorkBuddy，1M 上下文加全模态，价格还只有海外模型的零头。
MiniMax 又发了个省钱版 M3.1 Flash，价格砍半还能打平 DeepSeek V4.1 Flash，不过性价比仍输 MiMo-V2.6 Pro，适合预算敏感的场景。
这周盲测榜出来了，kimi-k3-max 代码榜冲进前十，小米 mimo-v2.6-pro 也往前赶，想选模型可以对着榜单比比价格再下手。
微软亮出自家基础模型 MAI-Base-2，2.8T 参数、1.6 万张 GB200 训练，连中国开源模型都被比下去了。
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.
这期榜单看点是 Claude Opus 5.5 Max 直接拿下智能体榜第一，代码榜里 claude-opus-5.5-high 一口气升了 7 位。国产这边 kimi-k3-max 综合榜 16 名，想看各家模型盲测排名的可以翻翻完整表格。
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.
An MIT licence removes the commercial-use restrictions that still accompany many open-weight releases, so teams can fine-tune or ship it in products without negotiating terms. The hybrid SWA–DSA design without any full-attention layers is a notable architectural signal for anyone tracking how long-context efficiency is evolving beyond dense attention. Engineering teams evaluating open-weight alternatives for code generation and research pipelines should benchmark it against comparably sized MoE releases.
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。
The reveal signals Xiaomi is positioning MiMo-V3 specifically for agent workloads, where attention efficiency over growing histories matters more than raw benchmark scores. Engineers building multi-turn agents can compare HySparse2's KV sharing against Alibaba Qwen and DeepSeek approaches for long-context cost control. The architecture details also hint at what RL training changes shipped in MiMo-V2.6.
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。
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