Overview

Agents-A1 is a 35B Mixture-of-Experts agentic model from InternScience, activating roughly 3B parameters per token (35B-A3B). It is built to scale heterogeneous agentic abilities across multiple domains, including long-horizon search, engineering, scientific research, instruction following, and tool-calling. The central idea behind the model is agent-horizon scaling: reaching the performance of much larger, trillion-parameter systems by scaling the horizon and diversity of an agent's behavior rather than scaling raw parameter count.

Key Features

• Agentic reasoning. Agents-A1 decomposes complex tasks into executable sub-steps, plans ahead, and adapts its strategy based on intermediate results.
• Native tool use. The model natively supports function calling and tool integration, enabling interaction with APIs, code interpreters, search engines, and other external tools.
• Scientific and professional reasoning. It handles tool-integrated scientific reasoning and professional knowledge question answering.
• Instruction following. It precisely follows detailed, multi-constraint instructions across diverse domains.
• Long context. The model supports a context window of up to 262K tokens for long-horizon trajectories and extended agentic workflows.

Architecture

Agents-A1 is a Mixture-of-Experts model with 35B total parameters and about 3B active per token. Its training investigates agent-horizon scaling from two perspectives. First, scaling long-horizon trajectories: the model is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. Second, scaling heterogeneous agent abilities through a three-stage training paradigm: full-domain supervised fine-tuning to align the base model with broad agentic behaviors, domain-level teacher models to capture specialized expertise in each domain, and multi-teacher multi-domain on-policy distillation with heterogeneity-aware optimization to improve knowledge transfer across domains.

Use Cases

Agents-A1 targets real-world agentic and research-oriented workflows: long-horizon web search and browsing agents, engineering and coding tasks, tool-integrated scientific research, general agentic assistants that orchestrate external tools, and settings that demand precise, multi-constraint instruction following.

Performance

InternScience evaluates Agents-A1 across six directions: long-horizon search, engineering tasks, scientific research, instruction following, general agentic tasks, and scientific agentic tasks. Despite operating in the ~35B model class, it delivers competitive results against frontier-scale systems. The model reports state-of-the-art results on several challenging benchmarks, including Seal-0 (56.4), HiPhO (46.4), FrontierScience-Olympiad (79.0), FrontierScience-Research (40.0), IFBench (80.6), and IFEval (94.8). Among comparable ~35B models it ranks best on a broad range of tasks, including BrowseComp (75.5), XBench-DS-2510 (86.0), GAIA (96.0), SciCode (44.3), HLE with tools (47.6), and MolBench-bind (56.8). Together these results position Agents-A1 as a capable and efficient agentic model that narrows the gap with much larger frontier systems while combining strong long-horizon search, robust scientific reasoning, and reliable instruction following.

For full model details, weights, and quantized variants, see the Agents-A1 model card on Hugging Face.