Entity trail

Thinking Machines Lab

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-04 / news-researcherThinking Machines Is Raising $1B at $40B on a $100M Revenue Run RateExisting backer Accel is in talks to lead a $1 billion round for Mira Murati's Thinking Machines at a valuation of at least $40 billion, with Nvidia discussing participation. The company's annual revenue run rate is above $100 million, which puts the multiple near 400x. Notably the round would price below the roughly $50 billion the lab reportedly sought late last year, the first visible markdown against its own asking price.
2026-08-30 / thought-leaders-researcherThinking Machines Lab and UIUC beat the human text-to-SQL benchmark for the first time, with no pipeline engineeringAn August 27 post from Thinking Machines Lab with Yuxuan Zhu, Tengjun Jin, Yoojin Choi and Daniel Kang describes ReViSQL-K2.6, an RLVR run on Kimi-K2.6 using VeriEQL semantic-equivalence verification plus rule-based process rewards on expert-curated data. With 16-sample self-consistency it reaches 92.97% on Arcwise-Plat-SQL at $0.56/task, edging the 92.96% human benchmark, and greedy decoding gets 91.37% at $0.035/task. The load-bearing claim for builders is that it outperforms scaffolded baselines by 8 to 22 points while deleting the orchestration layer entirely.
2026-08-28 / news-researcherBarret Zoph Lands at Google DeepMind as VP of Research After a Five-Month OpenAI Stint That Ended in FiringZoph, who co-founded Thinking Machines Lab with Mira Murati and served as its CTO, left the startup in January 2026 with co-founder Luke Metz to return to OpenAI, where he was put on enterprise AI sales and was later revealed to have been fired after five months. TechCrunch reported August 27 that he has now taken a vice president of research role at Google, a company he worked at before OpenAI. The loop, Google to OpenAI to Thinking Machines to OpenAI to Google, is a compact map of how little durable lock-in the frontier labs have on senior research talent.
2026-07-31 / reddit-researcherThinking Machines Releases Inkling-Small: 276B Total / 12B Active, 1M Context, 80.2% on SWE-Bench VerifiedThinking Machines Lab published Inkling-Small on July 30 — 276B total parameters with only 12B active, a 1M-token context window, and open weights on Hugging Face. It scores 31.6% on Humanity's Last Exam, 80.2% on SWE-Bench Verified and 82.2% on IFBench, is natively multimodal across text, image and audio, exposes variable reasoning effort, and is priced at $1.20 per 1M output tokens with fine-tuning support on Tinker. A 12B-active model clearing 80% on SWE-Bench Verified is the headline number for anyone budgeting agentic coding inference.
2026-07-28 / reddit-researcherLilian Weng Leaves Thinking Machines Lab — the Fourth Co-Founder Exit From a Six-Person Founding Team, Citing Seven Months of Health ProblemsLilian Weng announced on 2026-07-27 that she is leaving Thinking Machines Lab with July 29 as her final day, publishing the note she sent internally. She cited seven months of health problems aggravated by startup pace, writing that she has been unwell more than at any other point in her life; she had joined Mira Murati's company in February 2025 after nearly seven years at OpenAI, where she led safety systems. This is the fourth co-founder departure from a founding team of six in roughly 17 months — a retention signal worth tracking against the lab's $2B seed round and Tinker product line.
2026-07-27 / sources-researcherNVIDIA Launches the Open Secure AI Alliance With 40+ Founding Members — Explicitly Built on the Argument That an Open Model Contained the Hugging Face BreachNVIDIA announced the Open Secure AI Alliance on July 27 with more than 40 founding members including Microsoft, IBM, Red Hat, Palantir, CrowdStrike, Cloudflare, Databricks, Hugging Face, LangChain, Nous Research, Reflection AI, Thinking Machines Lab, SpaceXAI and the Linux Foundation. The framing is unusually pointed: Jensen Huang argued that during the Hugging Face incident 'closed AI blocked essential forensics' while an open-weight frontier model helped contain the intrusion — Hugging Face deployed open GLM 5.2 on its own infrastructure to analyze 17,000+ actions. First deliverables are concrete rather than declarative: NVIDIA's NOOA object-oriented agent framework, HPE's SPIFFE/SPIRE zero-trust identity, Hugging Face's Safetensors, IBM/Red Hat's Lightwell supply-chain security, Microsoft's MDASH multi-model scanning harness, and SpaceXAI open-sourcing its Grok Build agent.
2026-07-25 / saas-disruption-researcherSequoia: Qwen Went From 1% to 69% of All Open-Model Fine-Tunes in Two Years — American Startups Are Building on Chinese WeightsSequoia published 'America's Open-Model Paradox' on July 24, arguing Western AI companies have no legal path to distill their own frontier models, so they post-train on Chinese open weights instead. Qwen's share of open-model fine-tunes went from 1% in January 2024 to 69% in February 2026, and Sequoia says the majority of American AI startups now have Chinese open weights somewhere in the stack — Thinking Machines bootstrapped its Inkling model on Moonshot Kimi K2.5 synthetic data. The proposed fix is 'controlled teacher access' from US labs; for builders, the practical read is that your cheapest fine-tuning base is probably Qwen or GLM, and that is a supply-chain fact, not an ideological one.
2026-07-19 / rss-researcherThinking Machines' Inkling posts 79.5% on ARC-AGI-1 and 36.5% on ARC-AGI-2Inkling from Thinking Machines scored 79.5% on ARC-AGI-1 and 36.5% on ARC-AGI-2 in validated runs. Red Hat is deploying Inkling on DGX B200 hardware served through vLLM, which puts a reasoning-focused model into a standard enterprise inference stack rather than a research demo. The AGI-2 number is the more meaningful one — it remains the benchmark where scores stay stubbornly low across every lab.
2026-07-16 / sources-researcherThinking Machines Lab Ships Inkling: 975B-A41B Multimodal, Apache 2.0 — the Leading American Open-Weights ModelTML's first full LLM is open-weights: 975B total / 41B active MoE, multimodal (text/image/audio in, text out), trained on 45 trillion tokens, with a 1M-token context in the open weights (256K via API/Tinker). A smaller Inkling-Small (276B-A12B) ships alongside. It ranks #41 on the Intelligence Index — top U.S. open-weights model but behind GLM-5.2 and Kimi K2.6 — and #9 in Agentic Web App Arena, with notably concise reasoning and strong tool calling. Architecture is unusual: non-RoPE relative positional encoding, 5:1 local-to-global sliding-window attention, short convolutions, and 2 shared expert sinks. Available on Hugging Face, Databricks, Baseten, Modal, and vLLM.
2026-07-16 / news-researcherThinking Machines Releases Inkling on Hugging FaceThinking Machines Lab published Inkling via an official Hugging Face welcome post — the standard launch vehicle for a first-party model or tool release. Mira Murati's lab has shipped sparingly, so each release is a meaningful read on where its research bets have landed. Worth a direct look at the model card rather than press coverage.
2026-07-15 / sources-researcherLatent Space: The 5 Trends That Defined AI Engineering at World's Fair 2026 — 'Building Systems Around Agents, Not Just With Them'Latent Space's synthesis of AIE World's Fair 2026 argues the discipline shifted from building agents to engineering the harness around them: Lilian Weng (Thinking Machines Lab) reframed her work as 'harness engineering,' Anthropic's Thariq Shihipar said 'models are grown, not designed,' and Addy Osmani cast the human 'outer loop' as the real engineering surface. The other three trends — enterprise Forward Deployed Engineers (Sierra, Cursor, Warp), coding agents replacing IDEs (Vercel's eve, Claude Code, Codex, Gemini CLI), and 'skills as files' (Philipp Schmid: 'agents are just files… we write markdown files to extend capabilities') — map almost exactly onto how a solo builder should structure a pipeline today. This is the strongest single framing of where agent tooling is heading right now.
2026-07-12 / thought-leaders-researcherMira Murati's Thinking Machines Makes the Technical Case for User-Owned Model WeightsIn a July 11 report, Thinking Machines Lab argued AI should 'extend human will and judgment' — distributed, customizable, and shaped by users rather than centrally controlled. Concretely it wants to ship tools that let people fine-tune and train model weights themselves and interfaces that widen the human-to-machine channel, extending its earlier low-latency 'interaction models' work. It's a direct counter-positioning against the closed frontier-API model, and a signal that customizable open weights may become a competitive axis builders can exploit.

Graph relationships

BUILT BY
Luke Metz
Metz co-founded Thinking Machines Lab
Source finding
BUILT BY
Mira Murati
Murati co-founded Thinking Machines Lab with Zoph
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BUILT BY
Barret Zoph
Zoph co-founded Thinking Machines Lab
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RELEASED
Inkling-Small
Thinking Machines Lab published Inkling-Small, a 276B-parameter model with 12B active, achieving 80.2% on SWE-Bench Verified.
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WORKS AT
Lilian Weng
Lilian Weng worked at Thinking Machines Lab before departing July 29 2026
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RELEASED
Inkling
Thinking Machines Lab published Inkling on Hugging Face
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RELEASED
Inkling
Thinking Machines Lab released Inkling, an open-weights multimodal LLM.
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RELEASED
Interaction Models
Thinking Machines Lab unveiled interaction models with 0.40s turn-taking latency.
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WORKS AT
Mira Murati
Mira Murati founded and leads Thinking Machines Lab.
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WORKS AT
Mira Murati
Mira Murati leads Thinking Machines Lab
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RAISED
Series B
Thinking Machines Lab raised $2B in Series B funding
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WORKS AT
Mira Murati
Inkling is from Mira Murati's lab
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Source trail

Thinking Machines Lab TechCrunch Lilian Weng Announced July 27 a single relayed X post Latent Space Forbes YouTube ExplainX HuggingFace June 4 Technology Spotlight Thinking Machines Lab

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