Thinking Machines Lab published Inkling's benchmark card the same day it shipped the weights: 77.6% on SWE-bench Verified, 97.1% on AIME 2026, and then a line that almost no AI lab has written at a launch event - "Inkling is not the strongest overall model available today, open or closed." That sentence is the entire product strategy, and it is worth spending a few minutes understanding why.
What Inkling actually is
Thinking Machines Lab shipped Inkling on July 15, 2026 - a 975-billion-parameter Mixture-of-Experts model, 41B active per token, Apache 2.0 licensed, with full weights on Hugging Face and same-day fine-tuning on Tinker.
It was trained from scratch on 45 trillion tokens, arriving 22 months after Mira Murati left OpenAI.
The model supports up to 1M tokens of context via the open weights (256K on the hosted API), and shipped with an NVFP4 quantized checkpoint for NVIDIA Blackwell hardware.
A "controllable thinking effort" mechanism lets callers dial compute up or down per query. Alongside the flagship, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.
The architecture is not a plain transformer. Inkling has 66 decoder layers with a hidden size of 6,144. The first two layers use dense feed-forward blocks. The remaining layers contain 256 routed experts and 2 shared experts. That expert count is notably higher than most public MoE releases - more routing paths means finer-grained specialization, but also more infrastructure complexity at inference time.
Where the benchmarks are honest (and where they are not)
Selected official scores at effort=0.99 include 46.0% HLE with tools, 97.1% AIME 2026, 77.6% SWE-bench Verified, 54.3% SWE-bench Pro (Public), 63.8 Terminal Bench 2.1, 88.7% Global-MMLU-Lite, 73.3% MMMU Pro (Standard 10), 91.4% VoiceBench, and 98.6% StrongREJECT.
Those numbers look strong in isolation. The comparison table published by the lab is where it gets interesting.
GLM-5.2 outperforms Inkling on pure coding, agentic, and complex reasoning tasks. It scores 62.1% on SWE-bench Pro (Public) compared to Inkling's 54.3%, and 82.7 on Terminal Bench 2.1 against Inkling's 63.8. GLM-5.2 also holds the edge in text-only reasoning, scoring 40.1% on HLE (text only) versus Inkling's 30.0%.
DeepSeek V4 Pro beats Inkling on SWE-bench Verified (80.6% vs. 77.6%) and SimpleQA Verified (57.0% vs. 43.9%). Inkling does overtake DeepSeek V4 Pro in mathematical problem-solving, achieving 97.1% on AIME 2026 compared to DeepSeek's 96.7%.
There is a secondary issue worth flagging: comparison scores were drawn from the r/acc leaderboard generated July 14, 2026
- a snapshot one day before launch. That is standard practice, but it means the comparison set is frozen to whatever models were on that leaderboard on that date. New releases since then are not in the table.
An independent aggregator tells a slightly different story. Inkling scores 66.6 out of 100 and ranks #24 of 214 models, with its strongest eligible category being Instruction Following at #12. A rank of 24 on a broad aggregator, while trailing two open-weight rivals on the benchmarks that matter most to engineering teams, is the honest picture of where the model sits today.
| Benchmark | Inkling | DeepSeek V4 Pro | GLM-5.2 |
|---|---|---|---|
| SWE-bench Verified | 77.6% | 80.6% | - |
| SWE-bench Pro (Public) | 54.3% | - | 62.1% |
| Terminal Bench 2.1 | 63.8 | - | 82.7 |
| AIME 2026 | 97.1% | 96.7% | - |
| HLE (text only) | 30.0% | - | 40.1% |
Sources: Thinking Machines Lab model card (July 15, 2026); VentureBeat benchmark comparison.
The Bridgewater number and why it is the only one that matters
The lab points to a project with the hedge fund Bridgewater as proof. The two trained an open model on Bridgewater's financial know-how, and it scored 84.7% on financial reasoning tests, beating top proprietary models at a fraction of the cost. That figure comes from the two companies' own evaluation, not an independent one.
That caveat is important. Self-reported benchmark results on a proprietary test, run by the company that funded the training, cannot be treated as equivalent to a neutral eval. But the structure of the claim is still meaningful: a fine-tuned specialist built on an open base outperforming a closed general-purpose model on a narrow domain task is exactly the scenario Inkling is designed for. The number is plausible even if it is not auditable.
Inkling's explicit non-claim on benchmark leadership signals a structural choice: Thinking Machines Lab is competing on the fine-tuning and customization layer, not the raw performance layer.
Customers fine-tune Inkling through Tinker, Thinking Machines' customisation platform, and they own the result. They also carry the safety risk of whatever they build.
That second sentence is quietly significant for enterprise buyers. Apache 2.0 plus "you own the result" is an attractive licensing story. But it also means there is no vendor standing between your fine-tuned model and whatever it does downstream.
Beagle in action#ai-platform, 11:02amThe real question: what kind of team should run this
Thinking Machines' positioning is unusually honest for a launch week: "Inkling is not the strongest overall model available today, open or closed. Instead, a combination of qualities makes it a good open-weights base for customization: multimodal capabilities, efficient thinking, and availability on Tinker for fine-tuning."
That framing carves out a specific use-case profile:
You have proprietary data that changes the task. A general-purpose coding model has no knowledge of your internal APIs, your incident taxonomy, or your legal entity names. Fine-tuning a capable open base on that data is the move. Inkling is a credible starting point.
You want to stay in a Western supply chain. The launch suggests Thinking Machines wants to provide companies embracing lower-cost Chinese AI models with an alternative. The model fills a gap in the Western open-source AI ecosystem, which has lagged far behind that of China's.
You need multimodal breadth, not just coding depth. Inkling is designed to be broad. It was trained across agentic, reasoning, coding, instruction-following, factuality, vision, and audio tasks, rather than narrowly optimizing for one domain. That breadth matters for customization: different users need models that can adapt to very different workflows, not just excel on benchmarks.
You do not need the cheapest possible inference today. API pricing on the hosted endpoint is $1.87 input and $4.68 output per million tokens. That is mid-tier pricing, not budget. If you are routing high-volume agentic steps where cost dominates, DeepSeek V4 Flash still wins on the economics.
One architecture note worth tracking: Inkling's "controllable thinking effort" mechanism represents a departure from the black-box scaling strategies of frontier competitors. The ability to set reasoning depth per call, rather than routing between a "mini" and a "pro" model, is a different kind of cost control. It means a single model can serve both quick lookups and deep reasoning in the same workflow, without a routing layer. Whether that is simpler or more complex to operate depends on how tightly you want to tune cost per call type.
The more interesting question is what Inkling-Small turns out to be. The 276-billion-parameter variant optimized for low latency and cost is in preview. A 276B MoE with the same fine-tuning pipeline but materially lower inference cost would be a more direct challenge to DeepSeek Flash and the Qwen 3.5 small-model family. That is the release to watch.
Inkling open-weight model: common questions
What is Inkling from Thinking Machines Lab?
Inkling is the first in-house model from Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati. It launched on July 15, 2026 as a Mixture-of-Experts transformer with 975 billion total parameters and 41 billion active per token, released under Apache 2.0 with full weights on Hugging Face. It is designed primarily as a base for fine-tuning, not as a leaderboard challenger.
How does Inkling compare to DeepSeek V4 Pro and GLM-5.2?
Inkling trails both on coding benchmarks. GLM-5.2 scores 62.1% on SWE-bench Pro versus Inkling's 54.3%, and leads on Terminal Bench 2.1 (82.7 vs. 63.8) and HLE text-only reasoning (40.1% vs. 30.0%). Inkling's edge is breadth: it handles vision, audio, and multimodal tasks that neither rival covers in a single open-weight model.
What is Tinker, and how does it relate to Inkling?
Tinker is Thinking Machines Lab's cloud-based API for fine-tuning large language models, launched in October 2025. It automates and simplifies the process of customizing a frontier-scale AI model for specific tasks. Inkling is the first first-party model available on Tinker from day one of release.
What is the "controllable thinking effort" feature in Inkling?
The effort dial runs from 0.2 to 0.99 and controls how much compute the model applies to a given query. At 0.99 - the setting used in all official benchmarks - Inkling reasons deeply. At lower settings, it runs faster and cheaper. This means published benchmark comparisons show the ceiling of the model's capability, not the cost of running it at a realistic production effort level.
Can Inkling run locally?
An NVFP4 quantized checkpoint for NVIDIA Blackwell hardware shipped alongside the full weights. At 975B total parameters and 41B active, the full model requires significant infrastructure. Inkling-Small, the 276B preview variant, will be the more realistic local option for teams without a dedicated GPU cluster.