Qwen3.7 Max

Name: Qwen3.7 Max
Author: Alibaba

by AlibabaQwen 3

APIAvailable through a hosted API — pay per token, no self-hosting required

Released May 20261000K context≈ 750,000 words

Qwen3.7 Max is Alibaba's closed-weight frontier reasoning model — the first Qwen model that is proprietary rather than open-source. It targets the hardest coding, debugging, and agent orchestration tasks with chain-of-thought reasoning and a 1M context window. At $2.50/$7.50 per million tokens (often discounted 50%) it competes on price with Western frontier models. Available internationally through Alibaba Cloud Model Studio and OpenRouter. Text-only — no vision or multimodal input.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Exceptional

Factual Knowledge

Qwen3.7 Max

by AlibabaQwen 3

APIAvailable through a hosted API — pay per token, no self-hosting required

Released May 20261000K context≈ 750,000 words

Qwen3.7 Max is Alibaba's closed-weight frontier reasoning model — the first Qwen model that is proprietary rather than open-source. It targets the hardest coding, debugging, and agent orchestration tasks with chain-of-thought reasoning and a 1M context window. At $2.50/$7.50 per million tokens (often discounted 50%) it competes on price with Western frontier models. Available internationally through Alibaba Cloud Model Studio and OpenRouter. Text-only — no vision or multimodal input.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Exceptional

Factual Knowledge

Benchmark Scores

Benchmark	Score	Type	Recorded
IFBench	79.1	accuracy	23d ago
SciCode	53.5	accuracy	23d ago
LiveCodeBench	87.1	accuracy	23d ago
GPQA Diamond	92.4	accuracy	23d ago
TAU2	94.7	accuracy	23d ago
Humanity's Last Exam	41.4	accuracy	23d ago
IFEval	94.3	accuracy	23d ago
MMLU-Pro	89.3	accuracy	23d ago
LCR	79.0	accuracy	23d ago
SWE-Bench	80.4	accuracy	23d ago

Glossary

Agent OrchestrationCoordinating multiple AI agents to work together on complex tasks.Chain-of-ThoughtA reasoning technique where an AI model shows its step-by-step thinking process before arriving at a final answer, making its logic transparent and verifiable.Chain-of-Thought ReasoningA technique where a model works through a problem step by step, showing its reasoning process before arriving at a final answer.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Frontier ModelsState-of-the-art, cutting-edge AI models that represent the current best performance in the field.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal InputThe ability to accept and process multiple types of input data simultaneously, such as both images and text in the same request.OrchestrationCoordinating multiple components (data ingestion, routing, inference) to work together in a structured workflow.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning ModelA model trained to show explicit step-by-step reasoning and problem-solving logic before producing final answers, rather than jumping directly to conclusions.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Benchmark Scores

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Glossary