Models / Essential AI / / Rnj-1 Instruct API
Rnj-1 Instruct API
Elite agentic coding model with advanced tool use and STEM reasoning capabilities

This model is not currently supported on Together AI.
Visit our Models page to view all the latest models.
Rnj-1 Instruct API Usage
Endpoint
How to use Rnj-1 Instruct
Model details
Architecture Overview:
• 8.3B parameter instruction-tuned model built on Rnj-1 base with 32K context window supporting extended reasoning chains.
• Post-trained with 150B-token supervised fine-tuning stage optimized for instruction following, agentic behavior, and tool use.
• Specialized for multi-turn interactions with strong fill-in-the-middle (FIM) capabilities scoring 86.21% on HE-FIM-Python.
Training Methodology:
• Trained using Muon optimizer throughout all phases with targeted data distributions for reasoning and agentic abilities.
• Post-training inspired by long context mid-training with YaRN, Nemotron approaches, and agentic environment simulations.
• Deliberately limited post-training to preserve flexibility for community specialization and domain adaptation.
Performance Characteristics:
• Elite agentic coding: 20.8% SWE-bench Verified, 49.0% Performance-Enamel, outperforming comparable models by order of magnitude.
• Advanced code generation: 83.5% HumanEval+, 75.7% MBPP+, 57.1% BigCodeBench, 35.2% LiveCodeBench v6.
• Superior tool use: 62.2% BFCL v3, exceptional function calling and API integration capabilities.
• Strong mathematics: 92.6% GSM8K, 90.8% Minerva-Math, 43.3% AIME'25, competitive on olympiad-level problems.
• Robust scientific reasoning: 38.9% GPQA-Diamond, 30.2% SuperGPQA, 76.7% MMLU-STEM across physics, chemistry, biology.
Prompting Rnj-1 Instruct
Applications & Use Cases
Agentic Development:
• Creating AI assistants that iteratively solve software engineering tasks, resolve PRs, and fix security vulnerabilities.
• Developing agents for performance optimization using profilers and iterative code improvement workflows.
Code Generation & Assistance:
• Powering intelligent code completion and generation across multiple programming languages.
• Creating interactive coding assistants for data analysis, visualization, and end-to-end application development.
• Building developer tools with strong tool-calling capabilities for API integrations and system interactions.
Technical Problem Solving:
• Mathematical problem solving systems for education, research, and computational tasks.
• Scientific reasoning applications requiring long-context understanding across STEM domains.
• RAG systems and knowledge bases for technical documentation, troubleshooting, and support workflows.
Enterprise Applications:
• Internal coding assistants integrated with company codebases and workflows.
• Automated code review and quality improvement systems leveraging agentic capabilities.
• Technical support automation combining tool use, code execution, and reasoning for complex queries.
