Jan-v2-VL-high-gguf

Model overview

**Jan-v2-VL** is an 8B-parameter vision–language model for long-horizon, multi-step tasks in real software environments (e.g., browsers and desktop apps). It combines language reasoning with visual perception to follow complex instructions, maintain intermediate state, and recover from minor execution errors. We recognize the importance of **long-horizon execution** for real-world tasks, where small per-step gains compound into much longer successful chains—so **Jan-v2-VL** is built for stable, many-step execution. For evaluation, we use **The Illusion of Diminishing Returns: Measuring Long-Horizon Execution in LLMs**, which measures execution length. This benchmark aligns with public consensus on what makes a strong coding model—steady, low-drift step execution—suggesting that robust long-horizon ability closely tracks better user experience. Tasks where the plan and/or knowledge can be provided up front, and success hinges on stable, many-step execution with minimal drift:

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.

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.

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。

Frequently asked questions

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。