CodeT5

CodeT5

Salesforce
Inkling-Small

Inkling-Small

Thinking Machines Lab
5.0 out of 5 stars
CodeT5 Inkling-Small

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CodeT5:

About

Code for CodeT5, a new code-aware pre-trained encoder-decoder model. Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. This is the official PyTorch implementation for the EMNLP 2021 paper from Salesforce Research. CodeT5-large-ntp-py is specially optimized for Python code generation tasks and employed as the foundation model for our CodeRL, yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. This repo provides the code for reproducing the experiments in CodeT5. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on 8.35M functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on 14 sub-tasks in a code intelligence benchmark - CodeXGLUE. Generate code based on the natural language description.
Inkling-Small:

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.
CodeT5:

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
Inkling-Small:

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
CodeT5:

Audience

Developers and users interested in a solution to generate, summarize, and autocomplete code
Inkling-Small:

Audience

AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment
CodeT5:

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
Inkling-Small:

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
CodeT5:

API

Offers API Not Supported
Inkling-Small:

API

Offers API Supported
CodeT5:

Screenshots and Videos

Inkling-Small:

Screenshots and Videos

CodeT5:

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported
Inkling-Small:

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
CodeT5:

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
Inkling-Small:

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5
Inkling-Small:

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.
CodeT5:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
Inkling-Small:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
CodeT5:

Company Information

Salesforce
github.com/salesforce/CodeT5
Inkling-Small:

Company Information

Thinking Machines Lab
Founded: 2025
United States
thinkingmachines.ai/news/inkling-small/
CodeT5:

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Inkling-Small:

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Inkling-Small:
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CodeT5:

Categories

AI Code Generators Supported
Inkling-Small:

Categories

CodeT5:

Integrations

C Supported
C# Supported
Go Supported
Java Supported
JavaScript Supported
Model Context Protocol (MCP) Not Supported
PHP Supported
Python Supported
Ruby Supported
Tinker Not Supported
Inkling-Small:

Integrations

C Not Supported
C# Not Supported
Go Not Supported
Java Not Supported
JavaScript Not Supported
Model Context Protocol (MCP) Supported
PHP Not Supported
Python Not Supported
Ruby Not Supported
Tinker Supported
CodeT5:
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Inkling-Small:
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