CodeGemma vs. Inkling-Small Comparison


CodeGemma Google	Inkling-Small Thinking Machines Lab 5.0 out of 5 stars	+	+
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About CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time.	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.
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 Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Individuals wanting a solution to generate code and optimize their coding tasks	Audience AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment
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 Supported
API Offers API Not Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Not Supported Free Trial Not Supported	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
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 5.0 / 5 features 4.0 / 5 Read all reviews
	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 Google United States ai.google.dev/gemma/docs/codegemma	Company Information Thinking Machines Lab Founded: 2025 United States thinkingmachines.ai/news/inkling-small/
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MedGemma Google DeepMind	Inkling Thinking Machines Lab
Code Llama Meta	Inkling 2 Small Thinking Machines Lab
Gemma 2 Google View All	SWE-2 Flash Cognition 5.0 out of 5 stars View All
Categories AI Coding Assistants Supported AI Coding Models Supported AI Models Supported Large Language Models Supported Small Language Models Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Vision Models Supported Open-Weight AI Models Supported Small Language Models Supported

Integrations C Supported C# Supported CSS Supported Clojure Supported Elixir Supported F# Supported GaiaNet Supported Go Supported Google Cloud Platform Supported HTML Supported Java Supported JavaScript Supported Kaggle Supported Model Context Protocol (MCP) Not Supported R Supported Ruby Supported Rust Supported SQL Supported Scala Supported TypeScript Supported Show More Integrations View All 23 Integrations	Integrations C Not Supported C# Not Supported CSS Not Supported Clojure Not Supported Elixir Not Supported F# Not Supported GaiaNet Not Supported Go Not Supported Google Cloud Platform Not Supported HTML Not Supported Java Not Supported JavaScript Not Supported Kaggle Not Supported Model Context Protocol (MCP) Supported R Not Supported Ruby Not Supported Rust Not Supported SQL Not Supported Scala Not Supported TypeScript Not Supported Show More Integrations View All 2 Integrations
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