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Jan-v2-VL

Jan-v2-VL

Jan-v2-VL is an 8B vision-language model built on Qwen3-VL-8B-Thinking, designed for long-horizon, multi-step agentic tasks in real software environments — browsers, desktop apps, and beyond. It combines language reasoning with visual perception to execute stable, many-step automation chains with minimal drift.

Open in Jan

Variants

Jan-v2-VL comes in three variants tuned for different latency/quality tradeoffs:

VariantFocus
Jan-v2-VL-lowEfficiency-oriented, lower latency
Jan-v2-VL-medBalanced latency and quality
Jan-v2-VL-highDeeper reasoning, higher think time

Capabilities

  • Agentic automation & UI control: Follow complex multi-step instructions in browsers and desktop apps
  • Screenshot grounding: Perceive and act on visual state of the screen
  • Tool calling: Native support for BrowserMCP and similar integrations
  • Error recovery: Maintain intermediate state and recover from minor execution errors
  • Long-horizon execution: Stable performance across many-step automation chains

Performance

Long-Horizon Execution

Jan-v2-VL dramatically outperforms competing VLMs on the long-horizon benchmark, completing tasks up to 49 steps in a single turn — nearly 10x more than the next best model.

Long Horizon Benchmark — Task Length Executed in Single Turn

All three Jan-v2-VL variants significantly exceed the Qwen3-VL-8B-Thinking baseline (score: 5):

Long Horizon Execution — variant comparison

Text-Only Benchmarks

Jan-v2-VL maintains strong performance on standard language benchmarks with no degradation from the base model:

Text-Only Benchmarks — MMLU, SimpleQA, IF Bench, AIME25, EQ Bench, Creative Writing, SWE Bench

Multimodal Benchmarks

Core vision capabilities are fully preserved across all variants:

Multimodal Performance — RealVQA, CountBench, OCR Bench

Requirements

  • Memory:
    • Minimum: 16GB RAM (with Q4 quantization)
    • Recommended: 24GB RAM (with Q8 quantization)
  • Hardware: GPU recommended for real-time agentic use
  • API Support: OpenAI-compatible at localhost:1337

Using Jan-v2-VL

Quick Start

  1. Download Jan Desktop
  2. Select Jan-v2-VL-med (or your preferred variant) from the model list
  3. Start using it for browser automation or visual tasks

Deployment Options

Using vLLM:


vllm serve janhq/Jan-v2-VL-high \
--host 0.0.0.0 \
--port 1234 \
--enable-auto-tool-choice \
--tool-call-parser hermes \
--reasoning-parser qwen3

Using llama.cpp:


llama-server --model Jan-v2-VL-high-Q8_0.gguf \
--vision-model-path mmproj-Jan-v2-VL-high.gguf \
--host 0.0.0.0 \
--port 1234 \
--jinja \
--no-context-shift

Recommended Parameters


temperature: 1.0
top_p: 0.95
top_k: 20
repetition_penalty: 1.0
presence_penalty: 1.5

What Jan-v2-VL Does Well

  • UI automation: Navigate and interact with browsers and desktop applications via screenshots
  • Long-horizon tasks: Execute multi-step plans without losing context or drifting
  • Visual grounding: Identify and interact with specific UI elements from screenshots
  • Tool calling: Integrate with MCP servers like BrowserMCP for end-to-end automation

Limitations

  • GPU recommended: Real-time agentic use benefits significantly from GPU acceleration
  • 8B size: More capable than 4B models for visual tasks, but larger resource footprint
  • Vision required: Text-only tasks do not benefit from the vision-language architecture

Available Formats

GGUF Quantizations

  • Q4_K_M: Good balance of size and quality
  • Q5_K_M: Better quality, slightly larger
  • Q8_0: Highest quality quantization

Models Available

Community

Jan-v3-4BJan-v1