MODEL PROFILE

Ovis2.6-30B-A3B

ATH-MaaS · Ovis2.6-30B-A3B

We introduce **Ovis2.6-30B-A3B**, the latest advancement in the Ovis series of Multimodal Large Language Models (MLLMs). Building on the strong foundation of Ovis2.5, Ovis2.6 upgrades the LLM backbone to a **Mixture-of-Experts (MoE)** architecture, delivering superior multimodal performance at a fraction of the serving cost. It also brings major improvements in long-context and high-resolution understanding, visual reasoning with active image analysis, and information-dense document comprehension. The following table presents a detailed performance comparison. Please note that superscripted results are sourced from external technical reports, and Qwen scores represent the highest value between its Think and Instruct versions. For quick reference, the best results are highlighted in red, and the second-best results are underlined. All values are rounded to one decimal place. The thinking and thinking budget logic can be applied in the same way for multi-image, video and pure text scenarios.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:40
OVERVIEW

Model overview

We introduce **Ovis2.6-30B-A3B**, the latest advancement in the Ovis series of Multimodal Large Language Models (MLLMs). Building on the strong foundation of Ovis2.5, Ovis2.6 upgrades the LLM backbone to a **Mixture-of-Experts (MoE)** architecture, delivering superior multimodal performance at a fraction of the serving cost. It also brings major improvements in long-context and high-resolution understanding, visual reasoning with active image analysis, and information-dense document comprehension. The following table presents a detailed performance comparison. Please note that superscripted results are sourced from external technical reports, and Qwen scores represent the highest value between its Think and Instruct versions. For quick reference, the best results are highlighted in red, and the second-best results are underlined. All values are rounded to one decimal place. The thinking and thinking budget logic can be applied in the same way for multi-image, video and pure text scenarios.

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

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

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

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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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。