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Description

DeepCoder, an entirely open-source model for code reasoning and generation, has been developed through a partnership between Agentica Project and Together AI. Leveraging the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has undergone fine-tuning via distributed reinforcement learning, achieving a notable accuracy of 60.6% on LiveCodeBench, which marks an 8% enhancement over its predecessor. This level of performance rivals that of proprietary models like o3-mini (2025-01-031 Low) and o1, all while operating with only 14 billion parameters. The training process spanned 2.5 weeks on 32 H100 GPUs, utilizing a carefully curated dataset of approximately 24,000 coding challenges sourced from validated platforms, including TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each problem mandated a legitimate solution along with a minimum of five unit tests to guarantee reliability during reinforcement learning training. Furthermore, to effectively manage long-range context, DeepCoder incorporates strategies such as iterative context lengthening and overlong filtering, ensuring it remains adept at handling complex coding tasks. This innovative approach allows DeepCoder to maintain high standards of accuracy and reliability in its code generation capabilities.

Description

Lumen Outpost represents Cosine’s refined post-trained coding model, evaluated against its foundational model Kimi K2.6, along with GPT-5.5, GPT-5.4, and Gemini 3.1 Pro, specifically focusing on intricate, long-term coding assignments across 13 different programming languages. This model is designed not only for precision in coding but also to enhance key behavioral indicators vital in engineering processes, such as agent initiative, strategic planning, scope management, action coherence, succinct updates, and effective communication. According to Cosine’s benchmark analysis, the specialized post-training significantly elevated the base model's performance, with Lumen Outpost surpassing Kimi K2.6 in tests like Niche-Bench, Slop-Bench, Vibe-Bench, as well as in terms of cost efficiency for successful task completion. In the Niche-Bench assessment, which evaluates niche, legacy, and environmentally constrained programming languages, Lumen Outpost attained a score of 53.9% and excelled or equaled performance in 9 out of the 13 languages evaluated, demonstrating marked improvements particularly in Fortran, ABAP, Java, and Rust. The impressive results symbolize a significant leap in the practical application of coding models in real-world scenarios, underscoring the effectiveness of targeted training methodologies.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

ABAP
Fortran
Hugging Face
Java
Rust
Together AI

Integrations

ABAP
Fortran
Hugging Face
Java
Rust
Together AI

Pricing Details

Free
Free Trial
Free Version

Pricing Details

$20 per month
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Agentica Project

Founded

2025

Country

United States

Website

agentica-project.com

Vendor Details

Company Name

Cosine

Country

United Kingdom

Website

cosine.sh/blog/lumen-outpost-benchmark-report

Product Features

Product Features

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