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268 KiB
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268 KiB
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{"alibaba/qwen3-max":{"maxTokens":65536,"contextWindow":262144,"supportsPromptCache":true,"supportsImages":true,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":0.861,"outputPrice":3.4410000000000003,"description":"This is the best-performing model in the Qwen series. It is ideal for complex, multi-step tasks."},"alibaba/qwen-turbo":{"maxTokens":0,"contextWindow":1000000,"supportsPromptCache":false,"supportsImages":false,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":0.049999999999999996,"outputPrice":0.19999999999999998,"description":"Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance.\n\nSignificantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.","cacheWritesPrice":0.049999999999999996,"cacheReadsPrice":0.049999999999999996},"alibaba/qwen3-30b-a3b-instruct-2507":{"maxTokens":65536,"contextWindow":131072,"supportsPromptCache":true,"supportsImages":true,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":0.19999999999999998,"outputPrice":0.7999999999999999,"description":"Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and agentic tool use. Post-trained on instruction data, it demonstrates competitive performance across reasoning (AIME, ZebraLogic), coding (MultiPL-E, LiveCodeBench), and alignment (IFEval, WritingBench) benchmarks. It outperforms its non-instruct variant on subjective and open-ended tasks while retaining strong factual and coding performance.","cacheWritesPrice":0.7999999999999999,"cacheReadsPrice":0.19999999999999998},"alibaba/qwen3-coder-plus":{"maxTokens":65536,"contextWindow":1048576,"supportsPromptCache":true,"supportsImages":true,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":1,"outputPrice":5,"description":""},"alibaba/qwen3.5":{"maxTokens":65536,"contextWindow":256000,"supportsPromptCache":false,"supportsImages":true,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":0.6,"outputPrice":3.5999999999999996,"description":"The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers state-of-the-art performance comparable to leading-edge models across a wide range of tasks, including language understanding, logical reasoning, code generation, agent-based tasks, image understanding, video understanding, and graphical user interface (GUI) interactions. With its robust code-generation and agent capabilities, the model exhibits strong generalization across diverse agent.","cacheWritesPrice":3.5999999999999996,"cacheReadsPrice":0.6},"alibaba/qwen-max":{"maxTokens":0,"contextWindow":32768,"supportsPromptCache":false,"supportsImages":false,"supportsReasoningBudget":false,"supportsReasoningEffort":false,"inputPrice":1.5999999999999999,"outputPrice":6.3999999999999995,"description":"Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a
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