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 3.5 Lightning?

NVIDIA's Nemotron 3.5 Lightning represents an advanced mixture-of-experts model that features an impressive 30 billion parameters, with 3 billion of these actively engaged, and is specifically designed to deliver efficient, high-throughput performance for AI agents that operate continuously over extended periods. This model is crafted for the execution aspects of agentic systems, skillfully handling common tasks such as invoking tools, verifying outputs, carrying out routine commands, and assigning responsibilities to subagents, while larger reasoning models focus on strategic planning and orchestration. By utilizing a mixture-of-experts framework, it selectively engages a limited number of parameters for each input token, effectively combining the vast potential of a larger model with substantially decreased computational requirements. The training process is fine-tuned for popular agent harnesses, significantly improving inference speed through methods like speculative decoding, multi-token prediction, DFlash, and DSpark, which enhance its adaptability to various operational contexts. Moreover, it supports BF16 and NVFP4 checkpoints, ensuring deployment flexibility across platforms ranging from local systems such as DGX Spark and GeForce RTX hardware to large-scale data center environments. This innovative design not only amplifies AI capabilities but also positions Nemotron 3.5 Lightning as a pivotal resource for the evolution of intelligent systems, paving the way for future advancements in the field.

Media

Media

Integrations Supported

C
C#
C++
Cerebras
Devin Desktop
HTML
JSON
Kubernetes
MATLAB
PowerShell
R
Ruby
Rust
SQL
Solidity
Swift
Terraform
XML

Integrations Supported

Hermes Agent
NVIDIA NemoClaw

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

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

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

nvidia.com

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Open-Weight AI Models

Not specified

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