Inkling
Work with a full million-token context window across text and images, using free open weights or a managed fine-tuning path through Tinker.
The verdict on Inkling: Developers who need an entire codebase or long document set to stay in a single context pass, not chunked Inkling's core use case is developers who need an entire codebase or long document set to stay in a single context pass rather than being chunked. Pricing: Free open-weight download (Hugging Face) / paid managed fine-tuning via Tinker.
Best For
Developers who need an entire codebase or long document set to stay in a single context pass, not chunked
Standout Feature
A 975-billion-parameter open-weight model with a genuine one-million-token context window across text and images
TL;DR
Genuinely capable at scale for free, self-hosting the full model requires serious compute and it's a brand-new lab with limited track record.
Alternatives
Fit Score: use-case reference
How this is scoredInkling scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
A developer who needs an entire codebase or long document set to stay in a single context pass instead of being chunked
4.0/5- Capability
- 5/5
- Reliability
- 3/5
- Value
- 5/5
- Ease
- 4/5
- Compatibility
- 3/5
Inkling's genuine one-million-token context window across text and images is the technical centerpiece, and free downloadable weights mean no ongoing API costs, per the tool's own take.
A team wanting an established lab with a long third-party track record, or needing to self-host without significant compute investment
2.2/5- Capability
- 2/5
- Reliability
- 2/5
- Value
- 3/5
- Ease
- 2/5
- Compatibility
- 2/5
Cons note the full-size model requires significant compute to self-host, and Thinking Machines Lab is a new organization with limited third-party track record so far.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Inkling is Thinking Machines Lab's first open-weight model release, a 975-billion-parameter mixture-of-experts model built to handle text, images, and a one-million-token context window in a single system. The large context window means entire codebases, long document sets, or extended conversation histories can stay in a single pass instead of being chunked and stitched back together. Weights are downloadable for free from Hugging Face for teams that want to self-host or fine-tune locally, while Thinking Machines Lab also offers a paid fine-tuning service through its Tinker platform for teams that want a managed path to a custom version without building their own training infrastructure.
A smaller Inkling-Small variant trades some capability for lower compute requirements, useful for teams that want the same architecture on more modest hardware. Inkling fits research teams and technical builders who want an open, multimodal, long-context model they can inspect, fine-tune, and deploy on their own terms.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Free, downloadable open weights with no usage fees
- • Genuinely long context window for large documents or codebases
- • Managed fine-tuning option for teams without training infrastructure
- • Full-size model requires significant compute to self-host
- • New lab and release, limited third-party track record so far
Key Features
- 975-billion-parameter mixture-of-experts architecture
- 1-million-token context window
- Multimodal text and image input
- Smaller Inkling-Small variant for lighter hardware
- Managed fine-tuning available via Tinker
Trust & Data
Verified 2026-09
- Training on your data
- YesThinking Machines Lab has no privacy page specific to the Inkling model itself; the closest match is the free research API tier's privacy notice, which states prompts and outputs submitted through that API may be used to train, fine-tune, or otherwise improve the lab's own models, with no opt-out mechanism described, a materially different and less protective policy than the company's separate managed fine-tuning API, whose own FAQ says customer data is used solely to fine-tune the customer's models and is never used to train the lab's own models. The applicable policy therefore depends entirely on which access path a user takes to Inkling, and since it is released as open weights, self-hosting bypasses vendor data handling entirely.
- Compliance
- Not published
- Retention
- Personal information is retained for as long as reasonably necessary per the general website privacy notice; no fixed retention period is published.
- Export
- Not published
Sources: thinkingmachines.ai/tml free research api tier privacy notice.pdf, thinkingmachines.ai/tinker
As published by the vendor. Verify independently before purchase decisions.
Our Take
The 975-billion-parameter open-weight model with a genuine one-million-token context window across text and images is the technical centerpiece, and free downloadable weights mean no ongoing API costs. A managed fine-tuning path through Tinker is available if self-hosting isn't the goal. Two constraints apply: full-size self-hosting demands significant compute, and Thinking Machines Lab is a new organization with limited third-party track record. If long-context reasoning on large inputs is the primary requirement, this is one of the few open-weight options that addresses it directly.