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.
Response-ending behaviour shapes how conversational and proactive an assistant feels, and this benchmark turns that personality trait into a measurable signal. Product teams choosing a model for customer-facing chatbots can use these figures to match tone, since a 34% question rate reads very differently from a 0.1% one. The spread between MiMo-V2.6-Pro and DeepSeek V4.1 Flash also shows two Chinese labs optimising for opposite interaction styles.
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。
Stanford 这门 Agent 课把数据筛选讲透了：SWE-bench 过滤 68% 样本后 GPT-4o 反而涨 17 分，做训练数据的人直接照着检查清单抄。
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。
Inference latency, not model quality, is often the deciding factor for real-time consumer apps, and Inworld is competing on exactly that metric rather than training its own models. Developers building tutors, companions or game NPCs can route requests to Gemma 4, DeepSeek or GLM through a single API with fallback switching. Serving open models instead of proprietary ones also means lower per-token costs and no vendor lock-in.
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。
纠结日常任务要不要省钱用 Flash 模型的看过来，这篇把 7 款包括 DeepSeek Flash、蚂蚁百灵都测了一遍，写代码表现直接对比。
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.
归藏把他的产品宣传视频 skill 更新了，测试 Mimo V2.6 Flash 也能出效果，想省钱批量做视频的可以拿来直接用。
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.
用 opencode 的 10 美元套餐算了笔账：MiMo 能跑 3 万次请求，比 DeepSeek 的 2.6 万还多，就是不太稳。想省钱写代码的可以看看怎么选。
在 VS Code 里装个 Cline 插件，Gemini 3.8 Flash、DeepSeek V4.1 Flash 这些模型都能白嫖，写代码零成本，试试看。
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。
The same model can score differently depending on the harness around it, so benchmark numbers reflect the full stack rather than the model alone. Teams evaluating coding agents should rerun comparisons under their own harness before drawing conclusions. ReFigBench adds a new evaluation axis, and details are on arXiv (2609.18844) alongside a chat interface from dair.ai.
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