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Model Library
Alibaba Qwen AgentWorld
Qwen3 235B A22B Thinking 2507Qwen3 235B A22B Instruct 2507Qwen3 30B A3B Thinking 2507Qwen3 30B A3B Instruct 2507Qwen3 4B Thinking 2507Qwen3 4B Instruct 2507Qwen3 32BQwen3 14BQwen3 8BQwen3 4BQwen3 1.7BQwen3 0.6B
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  5. Qwen3 30B A3B Thinking 2507

Qwen3 30B A3B Thinking 2507

The Qwen3 30B A3B Thinking 2507 is a reasoning-focused Mixture-of-Experts (MoE) Causal Language Model designed for high-complexity analytical workloads that require explicit, step-by-step deliberation. This model operates exclusively in thinking mode and is trained to expose its internal reasoning process during generation, making it well-suited for advanced tasks in mathematics, science, coding, and academic problem solving. It supports a native 262,144-token context window, enabling deep reasoning over long documents, large codebases, and multi-source inputs without losing coherence.

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About this model

PaperBlogHF Model Card

The Qwen3 30B A3B Thinking 2507 is a reasoning-focused Mixture-of-Experts (MoE) Causal Language Model designed for high-complexity analytical workloads that require explicit, step-by-step deliberation. This model operates exclusively in thinking mode and is trained to expose its internal reasoning process during generation, making it well-suited for advanced tasks in mathematics, science, coding, and academic problem solving. It supports a native 262,144-token context window, enabling deep reasoning over long documents, large codebases, and multi-source inputs without losing coherence.

Model Type

MoE LLM

Released

July 31, 2025

License: Apache 2.0

Parameters

30B

Model ID
Qwen/Qwen3-30B-A3B-Thinking-2507
Capabilities
Text GenerationInstruction FollowingReasoningMathematical ReasoningCode Generation+4

Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-30B-A3B-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

INFERENCE COMMAND
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Performance under load

57k46k34k23k11k011664128256512Total Tokens / secConcurrent requests
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.

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Shivam Sharma

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