MODEL PROFILE

Ovis2.5-2B

ATH-MaaS · Ovis2.5-2B

We are pleased to announce the release of **Ovis2.5**, the successor to Ovis2, designed for native-resolution visual perception and enhanced multimodal reasoning. It integrates a native-resolution vision transformer (NaViT) that processes images at their original, variable resolutions, eliminating the need for fixed-resolution tiling and preserving both fine details and global layout—crucial for visually dense content such as charts and diagrams. To strengthen reasoning, Ovis2.5 is trained not only on linear chain-of-thought (CoT) but also on reflective reasoning, including self-checking and revision. This advanced capability is available at inference as an optional *thinking mode*, enabling users to trade latency for higher accuracy on complex inputs. Building on these advances, **Ovis2.5-9B** achieves an average score of 78.3 on the OpenCompass multimodal evaluation suite (SOTA among open-source MLLMs under 40B parameters), while the lightweight **Ovis2.5-2B** scores 73.9, continuing the “small model, big performance” philosophy for resource-constrained scenarios. **Key Features** * **Native-Resolution Perception** — NaViT vision encoder preserves fine details and global structure without lossy tiling. * **Deep-Reasoning Capability** — Optional *thinking mode* for self-checking and revision beyond linear CoT. *Thinking budget* supported. * **Chart & Document OCR** — State-of-the-art at its scale for complex chart analysis, document understanding (including tables and forms), and OCR. * **Broad Task Coverage** — Demonstrates leading performance on image reasoning, video understanding, and grounding benchmarks, showcasing strong general multimodal capability.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:14
OVERVIEW

Model overview

We are pleased to announce the release of **Ovis2.5**, the successor to Ovis2, designed for native-resolution visual perception and enhanced multimodal reasoning. It integrates a native-resolution vision transformer (NaViT) that processes images at their original, variable resolutions, eliminating the need for fixed-resolution tiling and preserving both fine details and global layout—crucial for visually dense content such as charts and diagrams. To strengthen reasoning, Ovis2.5 is trained not only on linear chain-of-thought (CoT) but also on reflective reasoning, including self-checking and revision. This advanced capability is available at inference as an optional *thinking mode*, enabling users to trade latency for higher accuracy on complex inputs. Building on these advances, **Ovis2.5-9B** achieves an average score of 78.3 on the OpenCompass multimodal evaluation suite (SOTA among open-source MLLMs under 40B parameters), while the lightweight **Ovis2.5-2B** scores 73.9, continuing the “small model, big performance” philosophy for resource-constrained scenarios. **Key Features** * **Native-Resolution Perception** — NaViT vision encoder preserves fine details and global structure without lossy tiling. * **Deep-Reasoning Capability** — Optional *thinking mode* for self-checking and revision beyond linear CoT. *Thinking budget* supported. * **Chart & Document OCR** — State-of-the-art at its scale for complex chart analysis, document understanding (including tables and forms), and OCR. * **Broad Task Coverage** — Demonstrates leading performance on image reasoning, video understanding, and grounding benchmarks, showcasing strong general multimodal capability.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。