tencent/ContextPilot-14B

TEXT GENERATIONPricing:Input $0.48 / Output $0.96Concurrent Unit Cost:1Model Size:14BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 27, 2026License:otherArchitecture:Transformer0.0K Featherless Exclusive Cold

ContextPilot-14B by Tencent is a 14 billion parameter Qwen3-based model designed for proactive context management in long-horizon language-model agents. It enables agents to plan, maintain long-term memory, and offload less useful context while reasoning and using tools. This model is optimized for research in long-context question answering and deep-search tasks, offering enhanced performance with a more compact working context.

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ContextPilot-14B: Proactive Context Management for LLM Agents

ContextPilot-14B, developed by Tencent, is a 14 billion parameter model built on the Qwen3 architecture. It introduces a novel framework, ContextPilot, designed to enhance long-horizon language model agents through proactive context management. This approach allows agents to effectively plan, manage long-term memory, and dynamically offload less relevant context, thereby maintaining efficiency during complex reasoning and tool-use tasks.

Key Capabilities

  • Extended Context-Management Toolset: Incorporates advanced tools for planning, structured memory, retrieval, and soft context offloading, moving beyond basic search, deletion, and summarization.
  • Context-Aware Partial Rollout: Employs an efficient exploration strategy that focuses on critical context-editing decisions.
  • Fine-grained Credit Assignment: Utilizes an RL method that trains intermediate snapshots by assigning credit based on the outcomes of downstream branches, improving learning for context editing actions.
  • Enhanced Performance: Achieves stronger performance on long-context QA and deep-search tasks with a more compact working context compared to existing baselines.

Good For

  • Research into proactive context management for large language models.
  • Developing and evaluating long-horizon AI agents.
  • Applications requiring efficient long-context question answering.
  • Deep-search tasks where context maintenance is crucial.