SWE-2 Reviews & Ratings

SWE-2

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What is SWE-2?

SWE-2 is Cognition’s coding model for software engineering agents, developed to improve the balance between capability, reasoning cost, and execution efficiency. The model is post-trained from Kimi K3, a multi-trillion-parameter model that had already received extensive reinforcement learning for agentic coding. Cognition further trained SWE-2 with a reinforcement learning algorithm that optimizes several reasoning-effort levels during a single training run. These effort levels let users trade off speed and cost against deeper planning, codebase exploration, and verification for more difficult assignments. SWE-2 is designed to reduce the over-exploration seen in earlier models by identifying relevant files and implementation paths more quickly. Its software engineering abilities include repository analysis, code writing and editing, debugging, testing, build and lint workflows, terminal tasks, and verification of completed work. The model places additional emphasis on writing end-to-end tests, catching edge cases and regressions, and gathering evidence instead of simply accepting assumptions in a prompt. Cognition’s training approach also uses cost penalties tied to the model’s performance frontier, length-weighted reward baselines, speculative decoding improvements, low-precision inference techniques, and expanded reinforcement learning data. Training data includes more diverse repositories, additional instruction-following requirements, and iterative verifier improvements designed to reduce reward hacking and false validation. SWE-2 is benchmarked against models such as GPT-6 Astra, GPT-5.6 Sol, Fable 5.1, Grok 4.6, and Kimi K3, with Cognition positioning it around strong coding performance at substantially lower cost. SWE-2 is intended for use across Cognition’s Devin ecosystem, including Desktop and CLI, with rollout to Devin Web and Fusion.

What is Nemotron 3 Ultra?

The Nemotron 3 Nano, a compact yet robust language model from NVIDIA's Nemotron 3 lineup, is specifically designed to excel in agentic reasoning, engaging dialogue, and programming tasks. Its cutting-edge Mixture-of-Experts Mamba-Transformer architecture selectively activates a specific subset of parameters for each token, allowing for quick inference times while maintaining high accuracy and reasoning skills. With an impressive total of around 31.6 billion parameters, including about 3.2 billion active ones (or 3.6 billion when including embeddings), this model outperforms its predecessor, the Nemotron 2 Nano, while demanding less computational power for every forward pass. It boasts the capability to handle long-context processing of up to one million tokens, enabling it to efficiently analyze lengthy documents, navigate complex workflows, and carry out detailed reasoning tasks in one go. Additionally, it is designed for high-throughput, real-time performance, making it particularly skilled in managing multi-turn dialogues, executing tool invocations, and handling agent-driven workflows that require sophisticated planning and reasoning. This adaptability renders the Nemotron 3 Nano a top-tier option for a wide range of applications that necessitate advanced cognitive functions and seamless interaction. Its ability to integrate these features sets a new standard in the landscape of language models.

Media

Media

Integrations Supported

.NET
C
C#
C++
Cerebras
Devin Desktop
Go
Lua
Objective-C
PHP
PowerShell
Python
R
Scala
Solidity
Swift
XML
YAML

Integrations Supported

Nemotron 3
OpenTag

API Availability

API Availability

Pricing Information

$20/month
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training

Company Facts

Organization Name

Cognition

Date Founded

2023

Company Location

United States

Company Website

cognition.com

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

research.nvidia.com/labs/nemotron/Nemotron-3/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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