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前 OpenAI CTO Mira Murati 的实验室在 7 月 15 日放出首个从零训练的开放权重模型 Inkling(Apache 2.0),7 月 31 日又用 Inkling Small 证明「三分之一参数、几乎同等智能」。本文讲清:它是什么、架构上有什么讲究、思考档位怎么用、和 Kimi/GLM/DeepSeek 怎么选,以及要避开的坑。
On July 15, the lab of former OpenAI CTO Mira Murati released Inkling, its first open-weights model trained from scratch (Apache 2.0); on July 31, Inkling Small showed "a third of the parameters, nearly the same intelligence." This document covers what it is, what's distinctive in the architecture, how the thinking-effort dial works, how it stacks up against Kimi/GLM/DeepSeek — and the pitfalls to avoid.
是什么:Inkling 是一个 MoE(混合专家)Transformer:975B 总参数、41B 激活,上下文最长 1M token,在 45 万亿 token 的文本+图像+音频+视频上从零预训练,原生多模态推理,Apache 2.0 开源权重。官方定位很坦诚:「不是今天最强的模型」,而是一个为微调定制而生的宽基座——多模态、省 token、可控思考档位,且直接接入自家微调平台 Tinker。
What it is: Inkling is a Mixture-of-Experts transformer: 975B total parameters, 41B active, up to a 1M-token context, pretrained from scratch on 45 trillion tokens of text, images, audio and video, reasoning natively across modalities, with Apache 2.0 open weights. The official positioning is refreshingly candid: "not the strongest overall model available today" — but a broad base built for customization: multimodal, token-efficient, with a controllable effort dial, wired into the lab's Tinker fine-tuning platform.
为什么值得关注:它是开放权重阵营里罕见的「从零训练 + 全家桶思路」样本:模型(Inkling / Inkling-Small)+ 微调平台(Tinker)+ 交互系统(interaction models)一体设计;发布长文还公开了大量训练细节(30M rollouts 的大规模 RL、校准训练、反审查训练),透明度在同类中少见。
Why it matters: it's a rare open-weights specimen of "trained from scratch + full-stack thinking": models (Inkling / Inkling-Small) + a fine-tuning platform (Tinker) + an interaction system, designed together; and the launch post discloses unusual amounts of training detail (30M-rollout large-scale RL, calibration training, censorship-resistance training).
读这个模型,先读它的立场。Thinking Machines 的使命表述是「构建扩展人类意志与判断的 AI」,落到产品上是一个明确的赌注:未来不属于一个万能的封闭模型,而属于「拿一个够宽的开放基座,用自己的数据调成自己的模型」。所以发布的重心不是刷榜,而是三件事:
To read this model, read its stance first. Thinking Machines' mission line is "building AI that extends human will and judgment," which lands as a concrete bet: the future belongs not to one universal closed model, but to "take a broad open base and tune it into your own model with your own data." Hence the launch emphasizes three things over leaderboard-chasing:
官方原话:「Inkling 不是今天可用的最强模型,无论开放或封闭」——它押的是「多模态 + 高 token 效率 + 可微调」这个组合拳。读任何厂商发布都该以此为镜:敢写明短板的发布,数字部分的可信度通常也更高。
In the post's own words: "Inkling is not the strongest overall model available today, open or closed" — the bet is the combination of multimodality, token efficiency, and tunability. Read every vendor launch against this mirror: posts that state their weaknesses plainly tend to have more trustworthy numbers too.
MoE 主体大致沿用 DeepSeek-V3 配方,但有几处为效率和长上下文特意做的偏离,官方长文写得相当具体:
The MoE core largely follows the DeepSeek-V3 recipe, with several departures made deliberately for efficiency and long context — the post is unusually specific:
训练侧还有两个值得记住的细节:① 后训练冷启动用了包括 Kimi K2.5 在内的开放模型生成的合成数据做初始 SFT(占算力小头),大头是在合成与人工环境上的大规模 RL;② RL 过程中出现了一个未被奖励直接鼓励的涌现现象——思维链随训练越来越「电报化」,丢掉冠词和连接词但保持可读、答案不变,纯粹由效率压力驱动(Cognition 训练 SWE-1.7 时也报告过同样现象)。
Two training details worth remembering: ① post-training was bootstrapped with an initial SFT on synthetic data generated by open models including Kimi K2.5 (a small fraction of compute), with the bulk spent on large-scale RL over synthetic and human-created environments; ② an emergent, unrewarded phenomenon appeared during RL — the chain of thought grew telegraphic over training, dropping articles and connectives while staying comprehensible with unchanged answers, driven purely by efficiency pressure (Cognition reported the same while training SWE-1.7).
基准面上,Inkling 的姿态是「宽而平衡」:HLE(带工具)46.0%、GPQA Diamond 87.2%、SWE-Bench Verified 77.6%、MCP Atlas 76.0%,多数项落在开放权重第一梯队之内、封闭旗舰之下;Design Arena 的盲测人评里排开放模型前列。安全上,FORTRESS 对抗档 78.0% 为其对比的开放模型中最高,且良性误拒率低(Benign 95.9%)。
On benchmarks, Inkling's posture is "broad and balanced": HLE with tools 46.0%, GPQA Diamond 87.2%, SWE-Bench Verified 77.6%, MCP Atlas 76.0% — mostly within the open-weights first tier, below closed flagships; among the top open models in Design Arena's blinded human evals. On safety, its FORTRESS adversarial score of 78.0% is the highest among the open models it compared, with low benign over-refusal (95.9%).
7 月 31 日正式发布的 Inkling Small 把「效率而非规模」推到台前:276B 总参数、12B 激活(旗舰为 975B/41B),用改进后的预训练数据与配方 + 同一套后训练栈,在多个基准上追平甚至反超旗舰。Artificial Analysis 智能指数 40(旗舰 41),并称同尺寸及以下的开放模型无一更高。
Officially released July 31, Inkling Small pushes "efficiency over size" to the front: 276B total, 12B active (vs. the flagship's 975B/41B), using improved pretraining data and recipe plus the same post-training stack to match or beat its big sibling on several benchmarks. Artificial Analysis scores it 40 on the Intelligence Index (flagship: 41) and says no open model of equal or smaller size scores higher.
短板同样明确:Agent 类任务与事实性知识明显落后——Tau 3 Banking 13.6%(旗舰 23.7%)、SimpleQA Verified 仅 20.9%(旗舰 43.9%)。官方给它的定位是成本/延迟敏感的工作负载:编码、当评分器(LLM-as-grader)、生成合成数据。256K 上下文、文本/图像/语音输入、Apache 2.0,可在 Tinker Playground 浏览器内直接微调。
The weaknesses are equally clear: it lags on agentic tasks and factual knowledge — Tau 3 Banking 13.6% (flagship 23.7%), SimpleQA Verified just 20.9% (flagship 43.9%). The official framing: cost/latency-sensitive workloads — coding, LLM-as-grader, synthetic data generation. 256K context, text/image/speech input, Apache 2.0, fine-tunable in the browser via Tinker Playground.
| 你在选什么 | 选 Inkling / Small 的理由 | 选别家的理由 | What you're choosing | Reasons to pick Inkling / Small | Reasons to pick others |
|---|---|---|---|---|---|
| 开放多模态基座做微调 | 从零训练的原生多模态 + Apache 2.0 + Tinker 一条龙;音频能力在开放阵营名列前茅(VoiceBench 91.4%) | 纯文本任务上 Kimi K2.6 / GLM 5.2 多项基准更高 | An open multimodal base for fine-tuning | Natively multimodal from scratch + Apache 2.0 + the Tinker pipeline; top-tier open audio (VoiceBench 91.4%) | On text-only tasks, Kimi K2.6 / GLM 5.2 score higher on several benchmarks |
| 高吞吐生产负载 | effort 档位 + Small 的 24K 平均输出,成本曲线优势明显 | 若需要强事实性(SimpleQA)或复杂 Agent 流程,旗舰封闭模型仍有明显差距优势 | High-throughput production workloads | The effort dial + Small's 24K average output give a clear cost-curve edge | For strong factuality (SimpleQA) or complex agentic flows, closed flagships keep a sizable lead |
| 合规与可控性 | Apache 2.0 商用友好;FORTRESS 对抗档开放阵营最高;反审查训练明确 | 需要厂商托管的企业合规栈(审计、SLA)时,封闭 API 更省事 | Compliance & control | Apache 2.0 is enterprise-friendly; highest open-model FORTRESS adversarial score; explicit censorship-resistance training | If you need vendor-managed compliance (audits, SLAs), closed APIs are less work |
① 别拿 Small 当知识库:SimpleQA 20.9% 意味着裸问事实极易出错——接检索再用。② Agent 流程先试后上:Tau 3 Banking 13.6% 提示复杂多步工具流程是短板。③ 基准脚注要读:官方 Terminal Bench 成绩剔除了少量「从网络搜索污染」的解;各家 harness 不同,横向对比有水分。④ 时效:Small 于 7 月 31 日刚发布、评分主要来自 Artificial Analysis 单一评测方,更多独立复测还在路上;价格与折扣(Tinker 五折)均为限时信息。
① Don't use Small as a knowledge base: 20.9% SimpleQA means bare factual queries fail often — pair it with retrieval. ② Pilot agentic flows before production: 13.6% on Tau 3 Banking flags complex multi-step tool use as the weak spot. ③ Read benchmark footnotes: official Terminal Bench scores zero out solutions contaminated by web search, and harnesses differ across labs. ④ Freshness: Small shipped July 31; scores lean on a single evaluator (Artificial Analysis) with independent replications pending; pricing and the Tinker discount are limited-time.
Inkling 不是要赢今天的榜,而是要押中明天的分发方式:开放权重 × 原生多模态 × 可购买的思考量 × 微调即产品。Small 用「三分之一参数打平旗舰」证明了配方在进步——但事实性与 Agent 短板提醒你:它是等你雕刻的基座,不是开箱即用的全才。
Inkling isn't built to win today's leaderboard but to bet on tomorrow's distribution: open weights × native multimodality × purchasable thinking × fine-tuning as the product. Small's "third the size, same intelligence" shows the recipe improving — while its factuality and agentic gaps remind you it's a base awaiting your carving, not an out-of-the-box generalist.