# Qwen3-Coder-Next Technical Report

arXiv: https://arxiv.org/abs/2603.00729 · HTML: https://mlsanity.com/paper/2603.00729/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 42 of its 57 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 42)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://mlsanity.com/paper/2601.11868/) (2026)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Training Software Engineering Agents and Verifiers with SWE-Gym](https://mlsanity.com/paper/2412.21139/) (2024)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [MathArena: Evaluating LLMs on Uncontaminated Math Competitions](https://mlsanity.com/paper/2505.23281/) (2025)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://mlsanity.com/paper/2505.20411/) (2025)
- [Vulnerability Detection with Code Language Models: How Far Are We?](https://mlsanity.com/paper/2403.18624/) (2024)
- [CWM: An Open-Weights LLM for Research on Code Generation with World Models](https://mlsanity.com/paper/2510.02387/) (2025)
- [Agent Learning via Early Experience](https://mlsanity.com/paper/2510.08558/) (2025)
- [CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence](https://mlsanity.com/paper/2406.07599/) (2024)
- [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025)

## Built on by (most cited first, 30 of 71)

- [OpenClaw-RL: Train Any Agent Simply by Talking](https://mlsanity.com/paper/2603.10165/) (2026)
- [SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents](https://mlsanity.com/paper/2605.21384/) (2026)
- [CodeTracer: Towards Traceable Agent States](https://mlsanity.com/paper/2604.11641/) (2026)
- [Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning](https://mlsanity.com/paper/2606.22995/) (2026)
- [REAM: Merging Improves Pruning of Experts in LLMs](https://mlsanity.com/paper/2604.04356/) (2026)
- [PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory](https://mlsanity.com/paper/2604.08000/) (2026)
- [CLI-Anything: Towards Agent-Native Computer Use](https://mlsanity.com/paper/2606.03854/) (2026)
- [Orchard: An Open-Source Agentic Modeling Framework](https://mlsanity.com/paper/2605.15040/) (2026)
- [The Verification Horizon: No Silver Bullet for Coding Agent Rewards](https://mlsanity.com/paper/2606.26300/) (2026)
- [Revisiting DAgger in the Era of LLM-Agents](https://mlsanity.com/paper/2605.12913/) (2026)
- [PM-Bench: Evaluating Prospective Memory in LLM Agents](https://mlsanity.com/paper/2607.12385/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring](https://mlsanity.com/paper/2608.09802/) (2026)
- [An Empirical Study of Harness Design for Coding Agents](https://mlsanity.com/paper/2609.20804/) (2026)
- [DeonticBench: A Benchmark for Reasoning over Rules](https://mlsanity.com/paper/2604.04443/) (2026)
- [Priming: Hybrid State Space Models From Pre-trained Transformers](https://mlsanity.com/paper/2605.08301/) (2026)
- [OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond](https://mlsanity.com/paper/2605.19660/) (2026)
- [Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism](https://mlsanity.com/paper/2606.00408/) (2026)
- [Rethinking the Role of Efficient Attention in Hybrid Architectures](https://mlsanity.com/paper/2606.15378/) (2026)
- [WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing](https://mlsanity.com/paper/2603.25226/) (2026)
- [RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation](https://mlsanity.com/paper/2603.25804/) (2026)
- [boldsymbolf-OPD: Stabilizing Long-Horizon On-Policy Distillation with Freshness-Aware Control](https://mlsanity.com/paper/2605.17862/) (2026)
- [PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents](https://mlsanity.com/paper/2605.19932/) (2026)
- [DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning](https://mlsanity.com/paper/2607.00341/) (2026)
- [Mach-Mind-4-Flash Technical Report](https://mlsanity.com/paper/2607.09375/) (2026)
- [NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness](https://mlsanity.com/paper/2609.08183/) (2026)
- [TDAD: Test-Driven Agentic Development - Reducing Code Regressions in AI Coding Agents via Graph-Based Impact Analysis](https://mlsanity.com/paper/2603.17973/) (2026)
- [Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents](https://mlsanity.com/paper/2604.14004/) (2026)
- [AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation](https://mlsanity.com/paper/2604.16625/) (2026)
- [AssertLLM2: A Comprehensive LLM Benchmark for Assertion Generation from Design Specifications](https://mlsanity.com/paper/2605.27472/) (2026)

## Abstract (arXiv)

We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference. In this work, we explore how far strong training recipes can push the capability limits of models with small parameter footprints. To achieve this, we perform agentic training through large-scale synthesis of verifiable coding tasks paired with executable environments, allowing learning directly from environment feedback via mid-training and reinforcement learning. Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Coder-Next achieves competitive performance relative to its active parameter count. We release both base and instruction-tuned open-weight versions to support research and real-world coding agent development.