Model ID
Qwen/Qwen3-4B-Thinking-2507
Capabilities
Qwen3 4B Thinking 2507
The Qwen3 4B Thinking 2507 is a reasoning-specialized Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters. This updated model operates only in thinking mode and is optimized to generate explicit reasoning traces as part of its output, making it suitable for complex analytical tasks that require deeper logical decomposition. It supports a native 262,144-token context window, enabling extended deliberation over long documents, large problem states, and multi-stage reasoning workflows.
About this model
The Qwen3 4B Thinking 2507 is a reasoning-specialized Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters. This updated model operates only in thinking mode and is optimized to generate explicit reasoning traces as part of its output, making it suitable for complex analytical tasks that require deeper logical decomposition. It supports a native 262,144-token context window, enabling extended deliberation over long documents, large problem states, and multi-stage reasoning workflows.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-4B-Thinking-2507). Released under Apache 2.0. Further reading: Paper · Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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