S2V-01
MiniMax基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85003
speech-01-hd
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-01-turbo
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-02-hd
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-02-turbo
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-2.6-hd
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-2.6-turbo
MiniMax来自海螺最新语音，OpenAI tts 格式
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查看详情 ↗speech-2.8-hd
MiniMax适合对音质要求较高的场景，侧重高保真和录音棚级清晰度，适用于有声书、影视配音、广告旁白、数字人等。
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查看详情 ↗speech-2.8-turbo
MiniMax适合对音质要求较高的场景，侧重高保真和录音棚级清晰度，适用于有声书、影视配音、广告旁白、数字人等。
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查看详情 ↗T2V-01
MiniMax基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗T2V-01-Director
MiniMax基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗MiniMax-H3
MiniMaxAI通过 App 直接使用 MiniMax\-H3。- WebApp 全球版：hailuoai\.video \| 中国版：hailuoai\.com - 桌面端 全球版：hub\.minimax\.io \| 中国版：hub\.minimaxi\.com MiniMax H3 是一款通用的全模态生成系统。它支持对由文本、图像、视频和音频组成的多模态上下文进行统一理解，并能生成带有原生立体声音频的视频，分辨率最高可达 2K，时长最长可达 15 秒。得益于其面向任务泛化的系统设计，H3 在预训练阶段便已具备广泛的多模态上下文理解和生成能力，从而在执行复杂的多模态指令时表现出色。完整的 H3 系统由以下三个模块组成：- H3-Context-IR：随着输入日益复杂，我们构建了一个专用系统，用于深入理解和精炼输入的多模态指令，随后将其转换为 H3 能够直接理解的形式——上下文中间表示（Context Intermediate Representation），以供生成使用。**H3-Context-IR 对最终输出的质量至关重要，因此我们强烈建议将其纳入您的生成流程，或按照“提示词指南”构建您自己的上下文处理系统。** - H3-Base：基于 H3-Context-IR 的输出生成音频和视频，生成结果分辨率为 768p。 - H3-Regenerate-2K：将 768p 的生成结果与原始上下文一同回流至 H3，以 2K 分辨率重新生成输出。这一过程充分利用了 H3 强大的生成能力以及原始上下文中蕴含的丰富信息，使其能够生成细节更准确、视觉保真度更高的高分辨率输出。
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查看详情 ↗MiniMax-M1-40k
MiniMaxAIWe introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. Consistent with MiniMax-Text-01, the M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute – For example, compared to DeepSeek R1, M1 consumes 25% of the FLOPs at a generation length of 100K tokens. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems ranging from traditional mathematical reasoning to sandbox-based, real-world software engineering environments. We develop an efficient RL scaling framework for M1 highlighting two perspectives: (1) We propose CISPO, a novel algorithm that clips importance sampling weights instead of token updates, which outperforms other competitive RL variants; (2) Our hybrid-attention design naturally enhances the efficiency of RL, where we address unique challenges when scaling RL with the hybrid architecture. We train two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively. Experiments on standard benchmarks show that our models outperform other strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, particularly on complex software engineering, tool using, and long context tasks. With efficient scaling of test-time compute, MiniMax-M1 serves as a strong foundation for next-generation language model agents to reason and tackle real-world challenges. Benchmark performance comparison of leading commercial and open-weight models across competition-level mathematics, coding, software engineering, agentic tool use, and long-context understanding tasks. We use the MiniMax-M1-80k model here for MiniMax-M1. We report results derived from the Agentless scaffold. Departing from the original pipeline, our methodology employs a two-stage localization process (without any embedding-based retrieval mec
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查看详情 ↗MiniMax-M1-40k-hf
MiniMaxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗MiniMax-M1-80k
MiniMaxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗MiniMax-M1-80k-hf
MiniMaxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗MiniMax-M2.5
MiniMaxAIExtensively trained with reinforcement learning in hundreds of thousands of complex real-world environments, M2.5 is **SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks**, boasting scores of **80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp** (with context management). Trained to reason efficiently and decompose tasks optimally, M2.5 exhibits tremendous speed in performing complicated agentic tasks, completing the SWE-Bench Verified evaluation **37% faster** than M2.1, matching the speed of **Claude Opus 4.6**. M2.5 is the first frontier model where users do not need to worry about cost, delivering on the promise of intelligence too cheap to meter. **It costs just $1 to run the model continuously for an hour at a rate of 100 tokens per second.** At 50 tokens per second, the cost drops to $0.30. We hope that the speed and cost effectiveness of M2.5 enable innovative new agentic applications.
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查看详情 ↗MiniMax-M3
MiniMaxAIMiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters. **Highlights:** - **Native Multimodality:** M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video. - **Context Scaling via Sparse Attention:** M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20. - **Coding & Cowork Capability:** M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork. M3 is powered by **MiniMax Sparse Attention (MSA)**, a high-performance sparse attention operator designed for million-token contexts. Compared with GQA, MSA dramatically reduces the attention compute and memory footprint while preserving model quality.
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查看详情 ↗MiniMax-M3-MXFP8
MiniMaxAIMiniMax-M3 is a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters. **Highlights:** - **Native Multimodality:** M3 undergoes mixed-modality training from the very first step, enabling deeper semantic fusion across text, image, and video. - **Context Scaling via Sparse Attention:** M3 introduces MiniMax Sparse Attention (MSA) to improve long context efficiency. M3 delivers 9× prefill and 15× decode speedups compared to M2 at 1M context, reducing per-token compute to 1/20. - **Coding & Cowork Capability:** M3 achieves frontier-level performance across long-horizon agentic benchmarks, excelling in both coding and cowork. MiniMax-M3-MXFP8 is the MXFP8 quantized variant of MiniMax-M3, a native multimodal model with 1M context. It has ~428B parameters and ~23B activated parameters.
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查看详情 ↗MiniMax-Music3
MiniMaxAI**MiniMax Music 3** is a high-performance music generation model for creating complete songs up to **five minutes** long. Conditioned on lyrics and a detailed music description, it generates structurally coherent songs with expressive vocals, evolving arrangements, and stable long-form audio quality. MiniMax Music 3 combines an **8B Global LLM** for long-range musical structure, a **0.6B Local LLM** for frame-level acoustic detail, and a continuous hidden-state synthesis system based on **Flow Matching** and **Flow-VAE**. The model produces 32 kHz, 16-bit stereo WAV audio. MiniMax Music 3 natively supports full-song generation up to five minutes. The model maintains musical themes, rhythm, vocal identity, and arrangement progression across long sequences, enabling complete structures such as intro, verse, pre-chorus, chorus, bridge, instrumental break, and outro.
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查看详情 ↗MiniMax-Text-01-hf
MiniMaxAIThis repository is primarily for the Transformers framework. If you're using other open-source frameworks, please use the alternative repository: MiniMax-Text-01 MiniMax-Text-01 is a powerful language model with 456 billion total parameters, of which 45.9 billion are activated per token. To better unlock the long context capabilities of the model, MiniMax-Text-01 adopts a hybrid architecture that combines Lightning Attention, Softmax Attention and Mixture-of-Experts (MoE). Leveraging advanced parallel strategies and innovative compute-communication overlap methods—such as Linear Attention Sequence Parallelism Plus (LASP+), varlen ring attention, Expert Tensor Parallel (ETP), etc., MiniMax-Text-01's training context length is extended to 1 million tokens, and it can handle a context of up to 4 million tokens during the inference. On various academic benchmarks, MiniMax-Text-01 also demonstrates the performance of a top-tier model. The architecture of MiniMax-Text-01 is briefly described as follows: - Total Parameters: 456B - Activated Parameters per Token: 45.9B - Number Layers: 80 - Hybrid Attention: a softmax attention is positioned after every 7 lightning attention. - Number of attention heads: 64 - Attention head dimension: 128 - Mixture of Experts: - Number of experts: 32 - Expert hidden dimension: 9216 - Top-2 routing strategy - Positional Encoding: Rotary Position Embedding (RoPE) applied to half of the attention head dimension with a base frequency of 10,000,000 - Hidden Size: 6144 - Vocab Size: 200,064
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查看详情 ↗MiniMax-VL-01
MiniMaxAIWe are delighted to introduce our **MiniMax-VL-01** model. It adopts the "ViT-MLP-LLM" framework, which is a commonly used technique in the field of multimodal large language models. The model is initialized and trained with three key parts: a 303-million-parameter Vision Transformer (ViT) for visual encoding, a randomly initialized two-layer MLP projector for image adaptation, and the MiniMax-Text-01 as the base LLM. MiniMax-VL-01 has a notable dynamic resolution feature. Input images are resized per a pre-set grid, with resolutions from 336×336 to 2016×2016, keeping a 336×336 thumbnail. The resized images are split into non-overlapping patches of the same size. These patches and the thumbnail are encoded separately and then combined for a full image representation. The training data for MiniMax-VL-01 consists of caption, description, and instruction data. The Vision Transformer (ViT) is trained on 694 million image-caption pairs from scratch. Across four distinct stages of the training pipeline, a total of 512 billion tokens are processed, leveraging this vast amount of data to endow the model with strong capabilities. Finally, MiniMax-VL-01 has reached top-level performance on multimodal leaderboards, demonstrating its edge and dependability in complex multimodal tasks. For production deployment, we recommend using vLLM to serve MiniMax-VL-01. vLLM provides excellent performance for serving large language models with the following features: 🔥 Outstanding service throughput performance ⚡ Efficient and intelligent memory management 📦 Powerful batch request processing capability ⚙️ Deeply optimized underlying performance For detailed deployment instructions, please refer to our vLLM Deployment Guide. MiniMax-VL-01 supports Function Calling capability, enabling the model to intelligently identify when external functions need to be called and output parameters in structured JSON format. With Function Calling, you can:
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查看详情 ↗SynLogic-32B
MiniMaxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SynLogic-7B
MiniMaxAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。