Qwen3-4B-Instruct-2507

Qwen3-4B-Instruct-2507 (Qwen)

Mid-size instruction-tuned language model balancing reasoning, speed, and cost.

• Modern instruct model. A ~4B-parameter model from the Qwen3 family, trained on high-quality multilingual data and instruction-tuned for reliable task following and chat behavior.

• Stronger than small LLMs. Delivers noticeably better reasoning, coding, and multi-step instruction handling than sub-2B models, without jumping to heavyweight deployments.

• Usable context window. Supports longer prompts and multi-turn conversations, making it suitable for assistants, workflows, and document-based tasks.

• Efficient to deploy. Much lighter than frontier models, practical on a single GPU or optimized CPU setups with good throughput.

• Open and flexible. Released under a permissive open license and integrates cleanly into standard LLM pipelines.

Why pick it for Norman AI?

Qwen3-4B-Instruct-2507 is a strong middle ground when you want modern instruction-following and solid reasoning without the cost or latency of large models. Ideal for assistants, automation, and evaluation where balance matters.

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
    {"role": "assistant",
     "content": "Sure! Here are some ways to eat bananas and dragonfruits together"},
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]

response = await norman.invoke(
    {
        "model_name": "qwen3-4b-instruct-2507",
        "inputs": [
            {
                "display_title": "Prompt",
                "data": messages
            }
        ]
    }
)