AREX-2
huggingface.co- Category
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- Rank
- No. 4153Tools index
Previous survey · No. 3965 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Models: Train & Run
- Date
About
AREX-2 is an image-text-to-text model from the Beijing Academy of Artificial Intelligence, fine-tuned from a Qwen base model. Its tags point to agent, deep-research, reasoning, tool-use and long-context use. It can be run with Transformers, vLLM or SGLang and is released under Apache-2.0.
At a glance
AREX-2 is a 27-billion-parameter model trained to revise its answers after reading feedback from tests or tools. It is worth benchmarking for multi-step coding and research jobs where the agent can check a result and use that check to improve the next attempt.
Good fit
Teams with an agent harness that can give the model test results, logs or scores and control how many rounds it spends on a task.
Consider something else when
If your workload mostly needs a quick single response, evaluate that separately before paying the latency and compute cost of repeated rounds.
Start here
Pick a small coding task with a deterministic test. Compare a single attempt with a run that can see the test failure and revise its answer. Track the final result and the time and tokens used in both runs.
Cost and requirements
BAAI releases the weights under Apache 2.0 and documents inference with a compatible Transformers version. Allow for model memory and runtime overhead. Longer contexts and additional attempts add compute costs.
Limits to know
The model card reports benchmark results for specific evaluation setups. You still need to supply the tools, feedback and execution limits for your own tasks.
Sources checked 2026-11-05. Source-based assessment.
Why it made the leaderboard
An openly licensed (Apache-2.0) multimodal model built for agent and tool-use workflows, from a credible open research lab, ready to self-host for research or production agent pipelines.
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