lightonai/OriOn-Qwen-SR1

VISIONConcurrent Unit Cost:2Model Size:33.4BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Apr 7, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

lightonai/OriOn-Qwen-SR1 is a 32 billion parameter Qwen3-VL-based vision-language model developed by LightOn. It achieves state-of-the-art performance on the MMLongBenchDoc benchmark for long-document visual question answering by internalizing synthetic reasoning traces through low-strength model merging. This model is specifically optimized for multi-page document reasoning and long-context visual document understanding, supporting up to 250+ pages in a single pass.

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OriOn-Qwen Synthetic Reasoning 1 Overview

OriOn-Qwen-SR1 is a 32 billion parameter vision-language model from LightOn, built upon Qwen/Qwen3-VL-32B-Instruct. It introduces a novel approach to long-document visual question answering (VQA) by internalizing synthetic reasoning traces, enabling it to surpass much larger models in performance without increasing inference cost.

Key Capabilities

  • State-of-the-Art Long-Document VQA: Achieves 58.3 accuracy on MMLongBenchDoc, outperforming Qwen3-VL-235B-A22B-Instruct (57.0) with 7x fewer parameters.
  • Internalized Reasoning: Utilizes a synthetic reasoning pipeline where evidence extraction and scoring are internalized via low-strength model merging (α=0.25). This means the model does not explicitly generate <think> tokens but retains the full performance benefits.
  • Controllable Reasoning: Reasoning can be activated at inference time by including a <cot> control token in the system prompt, leading to a +3.8 MMLBD improvement.
  • Drop-in Replacement: Compatible with the Qwen3VLForConditionalGeneration and AutoProcessor API, making it easy to integrate.
  • High Context Length: Supports a context length of 262,144 tokens, suitable for extensive multi-page documents.

Good For

  • Long PDF and slide-deck question answering: Capable of processing up to 250+ pages in a single pass.
  • Multi-page document reasoning: Excels at tasks requiring cross-page synthesis.
  • Long-context visual document understanding: Ideal for applications in enterprise, legal, scientific, and financial domains.