> ## Content Index
> Fetch the complete content index at: https://carussignal.com/llms.txt
> Use this file to discover other available public pages before exploring further.

# Inkling Isn't the Strongest Open Model — And Thinking Machines Says So on Purpose
- URL: https://carussignal.com/inkling-thinking-machines-open-weights-customizability-strategy/
- Published: 2026-07-28T03:00:54.000Z
- Updated: 2026-07-28T03:00:54.000Z
- Author: Carus Cha
- Tags: Inkling, Thinking Machines Lab, open weights, Mira Murati, open source AI

# Inkling Isn't the Strongest Open Model — And Thinking Machines Says So on Purpose

> Thinking Machines Lab, the startup led by former OpenAI CTO Mira Murati, released Inkling — a 975B-parameter open-weights model under Apache 2.0\. It is not the top of any leaderboard, and the company says so openly. That admission is the strategy, not a weakness.

Most model launches lead with a leaderboard win. Inkling, released in mid-July 2026, did something unusual: its makers said plainly that it is not the strongest model available. In a market where every release claims a new state of the art, choosing *not* to make that claim is itself the positioning. Here is what Inkling actually is, and why "customizable" may matter more than "best."

## Table of Contents

- [What Inkling is](#what-inkling-is)
- [The benchmarks, in context](#the-benchmarks-in-context)
- [Why customizability beats the leaderboard here](#why-customizability-beats-the-leaderboard-here)
- [What to weigh before you build on it](#what-to-weigh-before-you-build-on-it)

## What Inkling is

![Mixture-of-experts routing activates only a subset of model modules for each token](https://carussignal.com/content/images/2026/07/1-branded-7.webp)

Inkling is the first open-weights model from Thinking Machines Lab, the company founded by former OpenAI chief technology officer Mira Murati. It shipped in mid-July 2026 under an Apache 2.0 license — a genuinely permissive license that allows commercial use, modification, and redistribution.

The technical profile:

| Attribute                   | Inkling                        |
| --------------------------- | ------------------------------ |
| Architecture                | Mixture-of-Experts transformer |
| Total parameters            | 975 billion                    |
| Active parameters per token | 41 billion                     |
| License                     | Apache 2.0 (open weights)      |
| Modalities                  | Text, images, audio            |
| Context window              | 1,000,000 tokens               |

Two design choices stand out. First, the Mixture-of-Experts design means that although the model holds 975B parameters, only about 41B activate for any given token — a route to large-model quality at a fraction of the per-query compute of a dense model that size. Second, the Apache 2.0 license and open weights mean anyone can download, fine-tune, and deploy it without asking permission or paying per token.

## The benchmarks, in context

Inkling's public scores place it as a strong open model without topping the closed frontier:

- **SWE-bench Verified: 77.6%** (real software-engineering tasks)
- **MMMU-Pro: 73.5%** (multimodal reasoning)
- **VoiceBench: 91.4%** (speech understanding)

On the composite Artificial Analysis Intelligence Index, Inkling debuted around 41, reported as the top-scoring U.S. open-weights model at launch.

The honest reading: these are competitive, not chart-topping, numbers. The best closed models still lead on the hardest coding and reasoning suites. But the gap between the best open-weight models and the best closed models has compressed to single-digit percentage points on several benchmarks — which reframes the entire choice. When the quality difference is small and shrinking, factors *other* than raw benchmark rank start to dominate the decision.

## Why customizability beats the leaderboard here

![Builder weighing benchmark strength against model control and deployment freedom](https://carussignal.com/content/images/2026/07/2-branded-7.webp)

Thinking Machines is explicit that Inkling is a base to build on, not a finished product meant to win comparisons. That is a coherent bet on three structural advantages of open weights:

1. **Fine-tuning on your own data.** With the weights in hand, a team can specialize the model for a narrow domain — legal, medical coding, a specific codebase — and often beat a larger general model *on that task*. A leaderboard measures general ability; production cares about your task.
2. **Cost asymmetry.** A permissively licensed model you host yourself removes per-token API fees and, with an MoE design activating only \~41B parameters, keeps inference cost manageable. At scale, the economics can invert the "closed model is better" calculus.
3. **Control and portability.** Open weights mean no vendor lock-in, no surprise deprecation of the model you built on, and the ability to run in your own environment for privacy or compliance reasons.

Put together, "not the strongest" is a feature pitch: the value is not in beating a benchmark out of the box, but in being the best *starting point* for a system you shape yourself.

## What to weigh before you build on it

Open weights shift work and responsibility onto you. Before committing:

- **Total cost of ownership.** "No API fee" is not "free." Self-hosting a 975B-parameter MoE model requires serious GPU capacity, MLOps skill, and reliability engineering. For low or bursty volume, a closed API can still be cheaper.
- **Where the gap actually bites.** If your workload is exactly the hardest frontier reasoning or agentic coding, the single-digit benchmark gap may be the difference that matters. Test on *your* tasks, not the leaderboard.
- **Safety and governance are now yours.** A permissive license also means you own the guardrails, evaluation, and misuse mitigation that a hosted provider would otherwise manage.
- **Longevity of the base.** Building on any model — open or closed — is a bet on the ecosystem around it. Open weights protect you from deprecation but not from a stagnant community.

## Frequently Asked Questions

**Who makes Inkling?** Thinking Machines Lab, a startup founded by Mira Murati, former chief technology officer of OpenAI. Inkling is the company's first open-weights model.

**What does "open weights under Apache 2.0" mean in practice?** You can download the model's trained parameters and use, modify, fine-tune, and redistribute it — including commercially — under a permissive license, rather than only calling it through a paid API.

**Is Inkling better than the leading closed models?** No, and the company does not claim it is. Its scores are competitive but not chart-topping. Its pitch is customizability, cost control, and portability as a base model, not raw benchmark leadership.

**What is the point of 975B parameters if only 41B are active?** That is the Mixture-of-Experts design: a large pool of specialized parameters, but only a subset activates per token. It aims for big-model quality at much lower per-query compute than a dense model of the same size.

## Key Takeaways

- Inkling is Thinking Machines Lab's first model: 975B total / \~41B active MoE, Apache 2.0, multimodal, 1M-token context.
- Benchmarks (77.6% SWE-bench Verified, 73.5% MMMU-Pro, 91.4% VoiceBench) are strong but not frontier-leading.
- The open-vs-closed quality gap has narrowed to single digits on several benchmarks, making non-benchmark factors decisive.
- "Not the strongest" is deliberate positioning: Inkling is sold as the best *base to customize*, not the best out-of-the-box model.
- Open weights move cost, control, and safety responsibility to you — evaluate on your own workload before building.

## How this was written

I gathered Inkling's specifications, license, benchmark scores, and the company's stated positioning from multiple independent write-ups of the mid-July 2026 launch and cross-checked the architecture and benchmark figures for consistency before drafting this analysis.

## References

- [VentureBeat — Thinking Machines open-sources first multimodal model, Inkling](https://venturebeat.com/technology/thinking-machines-open-sources-first-multimodal-language-model-inkling-focused-on-low-cost-and-resistance-to-censorship?ref=carussignal.com)
- [Simon Willison — Inkling: Our open-weights model](https://simonwillison.net/2026/Jul/16/inkling/?ref=carussignal.com)
- [TFTC — Murati's 975B open-weights AI model challenges closed labs](https://www.tftc.io/inkling-open-weights-ai-model-thinking-machines-murati?ref=carussignal.com)