Sage-A1.5

[!Note] This repository contains model weights and configuration files for Sage-A1.5 in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.

Sage‑A1.5 is a 35B Mixture‑of‑Experts agentic model based on Qwen3.6 and Agents-A1 from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon A1, Sage‑A1.5 learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: Agentic Orchestration, Verification, and Open Scientific Discovery.

First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing Sage‑A1.5 to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope Sage‑A1.5 provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.

Highlights

• Agentic Orchestration: Orchestrates parallel investigations across subagents with independent contexts, enabling them to challenge assumptions, refine conclusions through evidence, and synthesize their findings into comprehensive results.
• Verification Mode: An opt-in self-review capability that lives inside the model's own chain of thought — the model pauses mid-trajectory to review what it has established and what has failed, then acts on a revised direction in the same turn, with no external critic.
• Agentic Reasoning: Sage-A1.5 excels at decomposing complex tasks into executable sub-steps, planning ahead, and adapting its strategy based on intermediate results.
• Tool Use: Natively supports function calling and tool integration, enabling seamless interaction with APIs, code interpreters, search engines, and other external tools.
• Scientific and Professional Reasoning: Handles tool-integrated scientific reasoning and professional knowledge question answering.
• Instruction Following: Precisely follows detailed, multi-constraint instructions across diverse domains.

We welcome developers and enterprises to integrate and try Sage-A1.5 and share their feedback.

Performance

We evaluate Sage-A1.5 in real-world agentic and research-oriented workflows across six directions — long-horizon search, engineering tasks, instruction following, scientific research, general agentic tasks, and scientific agentic tasks. Despite operating in the ~35B model class, Sage-A1.5 delivers highly competitive performance against frontier-scale systems such as Kimi-K3, GLM-5.3, and GPT-5.6-sol. It achieves overall SOTA on XBench-DS-2510 (92.0), IFBench (81.6), PinchBench (90.0), FrontierScience-Olympiad (81.0), FrontierScience-Research (48.3), CMT (80.0), and OmniDocBench (91.5), and reaches 95.1 on GAIA — higher than every larger-scale model we compare against. Among comparable ~35B models it ranks first on a broad range of tasks, including BrowseComp (81.7), SWE-bench Pro (61.2), MLE-Lite (65.2), SciCode (44.3, tied with Agents-A1), AA-LCR (81.0), HLE w/ tools (55.1), VitaBench (45.4), and ChemCOTBench (66.5). Compared with A1, it improves substantially on engineering and agentic workloads (SWE-bench Pro +17.6, SWE-bench Multilingual +14.0, PinchBench +13.1, MLE-Lite +21.3). These results show that Sage-A1.5 combines strong long-horizon search ability, robust scientific reasoning, and reliable instruction following, establishing it as a highly capable and efficient agentic model that narrows the gap with much larger frontier models.

🥇 Overall SOTA    🟢 Best Among Comparable Models (~35B)

📏 Comparable Models (~35B)

🚀 Larger-scale Models

[!Note] SWE-Bench Series: We use the mini-SWE-agent harness with a temperature of 1.0, top-p of 0.95, a 200K-token context window, and automatic context compaction enabled. All evaluations are conducted in a fully offline environment without internet access.

GLM-5.3 on OmniDocBench: GLM-5.3 is a text-only (unimodal) model without multimodal input support, so its results on OmniDocBench, a multimodal benchmark, are not reported.

Usage

SGLang

SGLang is a fast serving framework for large language models and vision language models.

Install SGLang with uv:

uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate

uv pip install sglang

See its documentation for more details.

The following commands create API endpoints at http://localhost:8000/v1:

• Standard Version:

  python -m sglang.launch_server \
    --model-path InternScience/Sage-A1.5 \
    --port 8000 \
    --mem-fraction-static 0.8 \
    --context-length 262144 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
  

• Multi-Token Prediction (MTP):

  python -m sglang.launch_server \
    --model-path InternScience/Sage-A1.5 \
    --port 8000 \
    --mem-fraction-static 0.8 \
    --context-length 262144 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --speculative-algorithm EAGLE \
    --speculati