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LLMS • CURATED • UPDATED AUG 4, 2026

Inkling

Thinking Machines' 975B Apache-2.0 model that takes text, images and audio natively

Inkling is the first open-weights model from Thinking Machines Lab, released 15 July 2026 under Apache 2.0. It is a 975B-parameter mixture of experts with 41B active per token, trained from scratch on 45 trillion tokens of text, images, audio and video, with a context window up to 1M tokens. The architecture uses 256 routed experts plus 2 shared experts per layer with 6 routed experts active per token, a sigmoid router, and interleaved sliding-window and global attention at a 5:1 ratio. It accepts text, image and audio input natively and returns text. Weights are on Hugging Face in both the original format and an NVFP4 checkpoint for Blackwell hardware. A distilled Inkling-Small followed on 31 July.

Pricing Free
Platforms api, local
API Yes
Open Source Yes
Modalities LLMs, Multimodal Reasoning
Best For Best Open-Weight Multimodal Base
Date Added 2026-08-04

It is the first roughly trillion-parameter open-weights model that takes audio and images natively rather than through a bolted-on encoder, and Apache 2.0 means the weights can be used commercially without asking anyone. Thinking Machines is candid that this is not the strongest model available but a base worth customising, which is a more honest pitch than most open releases make.

Pull the weights from huggingface.co/thinkingmachines/Inkling, or skip the hardware and call it through Together AI, Fireworks, Modal, Databricks or Baseten. For local work, the NVFP4 checkpoint is the one to use on Blackwell cards; everyone else should start from the original checkpoint on vLLM or SGLang. If the plan is to fine-tune rather than use it as shipped, that is what Thinking Machines built the Tinker platform for.

1 Feed audio as 16kHz WAV under about 20 minutes, which is the range it was tuned for
2 Keep images between roughly 40px and 4096px on the long edge
3 Use the controllable thinking effort setting rather than paying for maximum reasoning on every call
4 Treat the vendor benchmark numbers as an upper bound, they were run at effort 0.99 and temperature 1.0
5 Try a hosted inference provider before committing to 975B of self-hosted infrastructure
Claude Fable 5 NotebookLM Claude Opus 5 GPT-5.6 Sol Kimi K3

Domain Fine-Tune On An Open Base

Adapt a frontier-scale multimodal model to a specific domain without licence negotiation.

STEPS:
  1. Confirm Apache 2.0 covers the intended commercial use
  2. Assemble a domain dataset spanning the modalities you actually need
  3. Fine-tune on Tinker or your own infrastructure
  4. Evaluate against the base checkpoint on your own tasks
  5. Deploy through vLLM or SGLang
  6. Keep the base model available as a control for regressions

Audio And Document Understanding In One Model

Process recordings and scanned pages without a separate speech pipeline.

STEPS:
  1. Convert recordings to 16kHz WAV segments under 20 minutes
  2. Send audio and page images in the same request
  3. Ask for a structured summary rather than free text
  4. Spot-check transcription-dependent claims against the source
  5. Batch through a hosted provider if volume is bursty
  6. Compare cost against a separate ASR plus LLM pipeline
Free Completely free
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View Inkling Alternatives (2026) →

Compare Inkling with 5+ similar llms AI tools.

Q

Is Inkling free?

A

Yes, Inkling is completely free to use.

Q

Does Inkling have an API?

A

Yes, Inkling offers an API for programmatic integration.

Q

What is Inkling best for?

A

Inkling is best for Best Open-Weight Multimodal Base. Inkling is the first open-weights model from Thinking Machines Lab, released 15 July 2026 under Apache 2. It is the first roughly trillion-parameter open-weights model that takes audio and images natively rather than through a bolted-on encoder, and Apache 2.0 means the weights can be used commercially without asking anyone. Thinking Machines is candid that this is not the strongest model available but a base worth customising, which is a more honest pitch than most open releases make.

Q

What platforms does Inkling support?

A

Inkling supports api, local.

Q

Is Inkling open source?

A

Yes, Inkling is open source. You can access the source code, contribute, and deploy it on your own infrastructure.

Q

How do I get started with Inkling?

A

Pull the weights from huggingface.co/thinkingmachines/Inkling, or skip the hardware and call it through Together AI, Fireworks, Modal, Databricks or Baseten. For local work, the NVFP4 checkpoint is the one to use on Blackwell cards; everyone else should start from the original checkpoint on vLLM or ...

Q

How do I use Inkling?

A

Inkling is a large language model for text generation, analysis, and conversation. Use the API for programmatic access. Enter prompts or questions to get responses. It excels at apache 2.0, the least restrictive licence of any model at this scale.

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