Inkling-Small

Inkling-Small

Thinking Machines Lab
Recursive Flint

Recursive Flint

Recursive
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About

Inkling-Small is an efficient model that offers performance comparable to Inkling at a quarter of its size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters, trained on NVIDIA GB300 NVL72 systems. It supports native reasoning across text, images, and audio, variable thinking effort, and context windows of up to one million tokens. Users adjust reasoning effort from minimal to extra high to balance performance and compute. Improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. It performs well in coding and tool-use harnesses, exceeds 80% on SWE-bench Verified, and combines strong reasoning with efficient output. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens.

About

Recursive Flint is a 40-billion-parameter mixture-of-experts language model for agentic coding on local hardware. It activates about 2.5 billion parameters per token and supports reasoning, multi-turn tool use, and context windows up to 512,000 tokens. Developers can use Flint with coding agents to inspect codebases, fix bugs, implement features, and write tests while running inference on their own hardware. Its weights are published under the Apache 2.0 license. Flint works with llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang; Recursive also provides rec, an inference engine optimized for Apple Silicon M4 Pro and M5 Pro/Max. Setup documentation and examples help developers run the model locally. Flint is for developers and technical teams that want to operate and adapt a code-focused model on their own systems.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Supported
Linux Not Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment

Audience

Software developers and technical teams building or using coding agents, especially those seeking to run a coding model locally.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Pros & Cons from Real Users

Pros

  • Inkling-Small is really interesting from a developer’s point of view because it hits a sweet spot between serious model capability and practical deployability. A 276B-parameter model with only 12B active parameters per token is exactly the kind of architecture that makes sense if you care about cost, speed, and scaling real AI workflows. I also like that it is open weights under Apache 2.0. That makes it way more appealing for developers who want to fine-tune, inspect, customize, or build on top of the model without being completely locked into a closed API. The multimodal support is a big plus too. Being able to work with text, images, and audio inputs gives Inkling-Small a lot of room for developer tools, coding agents, support bots, document workflows, and internal automation.

Cons

  • The main downside is that “small” here is still not tiny. Even with only 12B active parameters, this is still a large open model that will require real infrastructure if you want to host it yourself. I would also want to test it deeply before making it the backbone of a production coding agent. The model card and early coverage look promising, but real developer workflows expose problems that benchmarks do not always catch: messy repos, flaky tests, weird dependencies, tool failures, and long multi-step tasks.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Thinking Machines Lab
Founded: 2025
United States
thinkingmachines.ai/news/inkling-small/

Company Information

Recursive
Founded: 2026
United States
recursive.com/updates/introducing-recursive-flint

Alternatives

Alternatives

Laguna XS 2.1

Laguna XS 2.1

Poolside
Inkling

Inkling

Thinking Machines Lab
Beam

Beam

Reflection

Categories

Categories

AI Coding Models Supported
AI Models Supported

Integrations

LM Studio Not Supported
Model Context Protocol (MCP) Supported
Ollama Not Supported
OpenRouter Not Supported
Tinker Supported
Unsloth Not Supported
vLLM Not Supported

Integrations

LM Studio Supported
Model Context Protocol (MCP) Not Supported
Ollama Supported
OpenRouter Supported
Tinker Not Supported
Unsloth Supported
vLLM Supported
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
Claim Recursive Flint and update features and information
Claim Recursive Flint and update features and information