**Jan-Code-4B** is a **code-tuned** model built on top of Jan-v3-4B-base-instruct. It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows. Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small. Jan-code is optimized for direct integration with Jan Desktop, select the model in the app to start using it.
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View details ↗**Jan-Code-4B** is a **code-tuned** model built on top of Jan-v3-4B-base-instruct. It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows. Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small. Jan-code is optimized for direct integration with Jan Desktop, select the model in the app to start using it.
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View details ↗**Jan-v1** is the first release in the **Jan Family**, designed for agentic reasoning and problem-solving within the Jan App. Based on our **Lucy** model, Jan-v1 achieves improved performance through model scaling. Jan-v1 uses the Qwen3-4B-thinking model to provide enhanced reasoning capabilities and tool utilization. This architecture delivers better performance on complex agentic tasks. For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.1% accuracy.
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View details ↗**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:
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View details ↗**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:
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View details ↗**Jan-v3.5-4B** is a fine-tuned variant of Jan-v3-4B-base-instruct, specialized on math reasoning and identity datasets. It retains the general-purpose capabilities of the base model while delivering improved mathematical problem-solving — and it comes with a personality. Unlike generic assistants, Jan-v3.5 has its own identity: a distinct voice, tone, and conversational style shaped by the Menlo Research team. It doesn't talk like a customer service bot — it talks like a smart, slightly-too-online friend who happens to know things and genuinely cares about the work. Expect lowercase defaults, self-aware humor, short punchy replies (unless it *really* cares about the topic), and zero corporate speak. - **Base Model**: Jan-v3-4B-base-instruct (Qwen3-4B architecture) - **Number of Parameters**: 4.0B - **Number of Parameters (Non-Embedding)**: 3.6B - **Number of Layers**: 36 - **Number of Attention Heads (GQA)**: 32 for Q and 8 for KV - **Context Length**: 262,144 natively
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