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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
Smaug Flash encompasses a trio of open-weight models meticulously fine-tuned by Abacus.AI to address production agentic workloads, with each model strategically placed along the capability–efficiency spectrum. This model line is developed through a combination of human-curated real-world agentic data and synthetic examples grounded in challenging scenarios, which results in enhancements in agentic programming, real-world tool utilization, automation, long-context reasoning, and adherence to instructions. The flagship model, Smaug Flash, derived from DeepSeek V4 Flash 0731, serves as the primary solution for enterprise agents that require a harmonious blend of speed, efficiency, and dependable performance. Its specific tuning minimizes the potential for spins and confusion during extensive tool use while preserving the speed advantages of the base model. Additionally, Smaug Mini, built on Qwen3.8 27B, is designed for multimodal applications and smaller reasoning tasks, offering a more compact solution with improved real-world agentic capabilities for singular workflows. Together, these models cater to diverse operational needs across various applications, showcasing the versatility of the Smaug Flash family.
API Access
Has API
API Access
Has API
Integrations
Hugging Face
Together AI
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
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
Abacus.AI
Founded
2019
Country
United States
Website
abacus.ai/smaug