Iris-pro

Climbing to the Search Frontier.

🤗 Collection  |  💻 GitHub  |  🔬 Evaluation Harness

Introduction

Iris-pro is an open-weight search agent post-trained from Qwen3.5-397B-A17B. A capable search agent has to decide what to search, how to read what comes back, when to keep going, and when the evidence it has gathered is enough. Iris is trained for exactly that loop, through alternating rounds of supervised fine-tuning and reinforcement learning.

Its smaller sibling is Iris-mini (35B-A3B).

Base	Qwen3.5-397B-A17B
Parameters	397B total / 17B active (512 experts, 10 active)
Layers / hidden	60 / 4096
Context	256K
Precision	bfloat16

Performance

Open-weight systems around 400B:

Model	Size	BrowseComp	BrowseComp-ZH	DeepSearchQA	HLE
MiroThinker-1.7	397B	74.0	75.3	–	42.9
Apodex-1.0	397B	75.5	82.6	84.6	49.0
Nex-N2-Pro	397B	83.7	79.6r	92.3r	50.0r
XYZ-Aquila-pro	397B	84.8	85.1	92.5	53.3
Iris-pro	397B	88.6	85.1	92.9	56.4

DeepSearchQA is scored with F1, the rest with accuracy; HLE uses the 2,158-question text-only subset. Iris numbers use the discard-all context-management setting; baselines come from their public reports, each under its own context management. ^r reproduced by the XYZ-Aquila team.

Context management

Long-horizon search runs out of context before a hard question is resolved, so a published number belongs to the agent and its harness. We report every benchmark in both regimes, under one tool set, one context limit and one judge.

Setting	BrowseComp	BrowseComp-ZH	DeepSearchQA	HLE
w/o	72.6	76.8	86.4	50.8
retry	–	84.1	92.3	56.6
discard-all	88.6	85.1	92.9	56.4
discard-all + retry	90.3	85.1	93.4	56.6

discard-all resets the conversation to the opening question once the prompt crosses a threshold. retry restarts an episode that ended without a parseable answer, carrying forward a short summary of what was already ruled out. We report discard-all as the headline setting even where adding retry scores higher.

Quickstart

Serve with SGLang. At 397B this needs multiple nodes or a large single node; scale --tp-size to the hardware you have:

python -m sglang.launch_server \
  --model-path AllSpark-Research/Iris-pro \
  --served-model-name Iris-pro \
  --port 21234 --tp-size 8 --ep-size 8 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

Then evaluate with Iris-Harness, which reproduces the numbers above:

bash scripts/run_eval.sh \
  --base-url http://127.0.0.1:21234/v1 \
  --llm-config iris-pro \
  --benchmarks "browsecomp:0:1" \
  --context-discard-threshold 131072

The model is trained to call tools through the OpenAI function-calling interface and to wrap its final answer in \boxed{}. Prior reasoning is carried back into the conversation on each turn; the harness's iris-pro config sets this up for you.

License

Apache 2.0.