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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.
Description
SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.
API Access
Has API
API Access
Has API
Integrations
Claude Code
OpenAI
OpenAI Codex
SubQ
Pricing Details
No price information available.
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
Abacus.AI
Founded
2019
Country
United States
Website
abacus.ai/smaug
Vendor Details
Company Name
Subquadratic
Founded
2026
Country
United States
Website
subq.ai/subq-1-1-small-technical-report
Product Features
Product Features
Alternatives
No Alternatives