Thinking Machines 发布 975B 开源多模态模型 Inkling

Thinky only seems to come up for air once every few months; most recently with Interaction models - but each time they do they impress, showing both taste and depth. Today they introduced Inkling — not a SOTA model, but a very solid new family for a baseline American open model:Our model, called Inkling, is a Mixture-of-Experts transformer with 975B total parameters, 41B active. It supports a context window of up to 1M tokens. It was pretrained on 45 trillion tokens of text, images, audio and video. It is the first in a family of models of different sizes: alongside it we are sharing a preview of Inkling-Small, a lighter-weight model with 12B active parameters, trained with a similar recipe, that achieves strong performance with even lower cost and latency.Inkling reasons natively over text, images, and audio, and balances cost with performance through efficient and controllable thinking effortThe Huggingface breakdown covers some interesting technical highlights:AI News for 7/14/2026-7/15/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!AI Twitter RecapWhat happenedThinking Machines Lab launched Inkling, its first fully released open-weights foundation model family entry, positioning it as a customizable multimodal base model rather than a benchmark-maxed flagship.Thinking Machines announced Inkling as an open-weights model that “reasons efficiently across text, image, and audio modalities,” with full weights available and immediate support on its Tinker platform and Playground @thinkymachines.Mira Murati described Inkling as the company’s “first model,” “trained from scratch,” with open weights and same-day fine-tuning on Tinker @miramurati.Soumith Chintala framed it as Thinking Machines’ “first general model,” stressing open weights, 975B parameters, native multimodality, and availability on Tinker, Hugging Face, and partners @soumithchintala.John Schulman added timeline context: pretraining began last winter, and from mid-January a small team built coding, reasoning, and agentic training on top @johnschulman2.Lilian Weng characterized Inkling as a foundation model aimed at “solid performance across a broad categories of capabilities” and intended for practical use plus customization @lilianweng.TML staff repeatedly emphasized that this is a day-1 release and a foundation for future iterations rather than their final frontier push @soumithchintala, @cHHillee, @keirp1.The release landed with unusually broad day-0 ecosystem support across vLLM, SGLang, Modal, Baseten, Databricks, Hugging Face, and quantization/community tooling @vllm_project, @lmsysorg, @modal, @baseten, @Yuchenj_UW, @huggingface, @danielhanchen.Independent commentators immediately tagged it as the strongest U.S.-based open-weight release so far, though generally still behind the top Chinese open-weight and best closed models on some benchmarks @natolambert, @ArtificialAnlys, @scaling01.Core facts and specsModel size, modality, licensing, contextInkling is reported as 975B total parameters / 41B active parameters in most posts @soumithchintala, @vllm_project, @ArtificialAnlys, @kimmonismus.One tweet says 974B @Yuchenj_UW, and another says 952B @multimodalart; the overwhelming consensus in the tweet set is ~975B.It is a Mixture-of-Experts model with 41B active parameters per token @VictoriaLinML.It is Apache 2.0 licensed according to multiple reactions and summaries @natolambert, @Yuchenj_UW, @multimodalart.It supports text, image, and audio inputs, with text output @soumithchintala, @TheRundownAI, @ArtificialAnlys.Open-weights checkpoints support up to 1M context @vllm_project, @lmsysorg, @ArtificialAnlys.Tinker/API context is described as 256K, with pricing differentiated for 64K and 256K contexts @ArtificialAnlys.Training and release detailsTML says Inkling was trained from scratch @miramurati, @LiorOnAI.Community readers extracted 45T training tokens from the release materials @eliebakouch, @ArtificialAnlys, while one post says 48T @mervenoyann. The more repeated figure in this dataset is 45T.Inkling includes controllable reasoning effort / numerical effort levels @LiorOnAI, @TheRundownAI, @danielhanchen.Tinker customers highlighted concise reasoning and strong tool calling rather than maximal raw benchmark chasing @tinkerapi, @MichaelElabd.Architecture details surfaced in reactionsSeveral technically literate reactions extracted architectural choices from the release:Hybrid/sliding-window attention with a 5:1 local-to-global layer ratio and window size 512 @eliebakouch, @ariG23498.Relative positional encoding / relative attention bias instead of RoPE; multiple posters called this one of the most novel large-scale choices @stochasticchasm, @eliebakouch, @rasbt, @arohan, @ChangJonathanC.Short convolution layers added around attention/FFN streams; commenters flagged this as unusually scaled-up usage of short convs @eliebakouch, @stochasticchasm, @rasbt, @SonglinYang4.MoE with shared expert sinks / 2 shared experts, noted as atypical since many recent MoEs use 1 shared expert @eliebakouch, @ariG23498.DeepSeek-style auxiliary-loss-free load balancing was cited in community readings of the architecture @eliebakouch.muP and Muon/weight decay variants were inferred from the writeup and confirmed by optimizer expert reaction: Aaron Defazio said they are using his corrected weight decay approach, “MuonC/AdamC” @aaron_defazio, while community readers also pointed out muP @stochasticchasm, @Laz4rz.8 MTP heads for speculative decoding were highlighted by vLLM @vllm_project.VariantsInkling-Small is repeatedly referenced as an upcoming or separately discussed smaller model @LiorOnAI, @teortaxesTex.Community summaries describe Inkling-Small as 276B total / 12B active and unexpectedly competitive versus the larger model on several evaluations @eliebakouch, @nrehiew_.Performance and benchmarksIndependent benchmark framingArtificial Analysis said Inkling debuts at 41 on the Intelligence Index, making it the leading U.S. open-weights release and ahead of Nemotron 3 Ultra (38), Gemma 4 31B (29), and gpt-oss-120b (24) @ArtificialAnlys.Artificial Analysis also said Inkling averages 25K output tokens per Intelligence Index task, vs 43K for GLM-5.2 max, 38K for Kimi K2.6, and 37K for DeepSeek v4 Pro max, framing it as relatively token-efficient @ArtificialAnlys.Natolambert called it a “clear step up from Nemotron Ultra” and “new best American model,” but still “a bit behind GLM 5.2 on agentic benchies, and Kimi K 2.6 on multi modal” @natolambert.Design Arena said Inkling entered Agentic Web App Arena at #9 overall, Elo 1257, in the same band as Claude Opus 4.6 and Gemini 3.5 Flash, and called it the highest-ranking U.S.-based open-weight model for agentic workloads @DesignArena.Arena added Inkling to Agent Arena / Text / Vision / Code Arena on launch day @arena.Specific benchmark numbers citedFrom Artificial Analysis:GDPval-AA v2 Elo 1238, higher than Kimi K2.6 (1190) and DeepSeek v4 Flash max (1189) @ArtificialAnlys.τ³-Banking 24%, above Kimi K2.6 (21%) and slightly above DeepSeek v4 Flash max (23%) @ArtificialAnlys.Qualitative performance takesPositive:“Sharp and concise” reasoning, not rambly @MichaelElabd.Strong tool calling and good long-horizon error recovery on agentic tasks @MichaelElabd.Good “quality of mind” / unsycophantic flavor @skirano, @tinkerapi.Alex Kirillov claimed Inkling avoids the common “audio in = intelligence penalty” seen in many omni models, though another user asked for stronger supporting evidence and benchmarks @alex_kirillov, @giffmana, @alex_kirillov.More mixed / critical:Scaling01 argued the benchmarks are “not that great,” describing it as roughly “another Kimi-K2.6” and behind all closed models and GLM-5.2, speculating the release may have been timed ahead of Kimi-K3 and DeepSeek-V4-GA @scaling01.Stochast