MODEL PROFILE

MiniCPM4.1-8B

openbmb · MiniCPM4.1-8B

MiniCPM4.1 launches end-side versions with 8B parameter scale, both achieving best-in-class performance in their respective categories. 1. It is advisable to use temperature=0.9, topp=0.95. And we suggest setting max_output_token to 65,536 tokens. 2. For math problems, we recommend using "Please reason step by step, and put your final answer within \boxed{}." 3. And for English multiple-choice questions, we recommend starting with "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering." And "你回答的最后一行必须是以下格式 '答案:$选项' (不带引号), 其中选项是ABCD之一。请在回答之前一步步思考" for Chinese MCQ. MiniCPM4.1 adopts sparse attention and speculative decoding to improve the inference efficiency. On RTX 4090, MiniCPM4.1 achieves 3x decoding speed improvement in reasoning.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:06
OVERVIEW

Model overview

MiniCPM4.1 launches end-side versions with 8B parameter scale, both achieving best-in-class performance in their respective categories. 1. It is advisable to use temperature=0.9, topp=0.95. And we suggest setting max_output_token to 65,536 tokens. 2. For math problems, we recommend using "Please reason step by step, and put your final answer within \boxed{}." 3. And for English multiple-choice questions, we recommend starting with "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering." And "你回答的最后一行必须是以下格式 '答案:$选项' (不带引号), 其中选项是ABCD之一。请在回答之前一步步思考" for Chinese MCQ. MiniCPM4.1 adopts sparse attention and speculative decoding to improve the inference efficiency. On RTX 4090, MiniCPM4.1 achieves 3x decoding speed improvement in reasoning.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。