Ming-3.0-audio-tiny
inclusionAI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。
Input Audio→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 84999
Ming-3.0-tts-0.5B
inclusionAIMing-3.0-tts-0.5B Dense 0.5B multilingual speech synthesis with paralinguistic and free-form control. This repository contains **Ming-3.0-tts-0.5B**, the dense Ming-3.0-tts checkpoint built on Qwen2.5-0.5B. The checkpoint includes the backbone, DiT head, audio tokenizer, speaker encoder and text tokenizer. > **Inference requires the Ming-3.0-tts GitHub repository.** This Hugging Face repository is for checkpoint distribution and does not provide a standalone inference setup. Clone the GitHub repository and follow its README for the current dependencies, inference code and usage instructions. The CLI scripts and Python APIs referenced below are provided by the GitHub repository.
Input Text→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-3.0-tts-tiny
inclusionAI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。
Input Text→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-flash-omni-2.0
inclusionAIThe newly released Ming-flash-omni 2.0 leverages the Ling-2.0 architecture—a Mixture-of-Experts (MoE) framework comprising 100B total and 6B active parameters. Representing a generational advancement over its predecessor, it establishes new State-of-the-Art (SOTA) benchmarks among open-source omni-MLLMs. Ming-flash-omni 2.0 effectively synergizes foundational abilities with specialized domain expertise. In particular, it exhibits superior performance in visual encyclopedic knowledge, immersive speech synthesis, and high-dynamic image generation and manipulation. Compared to Ming-flash-omni Preview, Ming-flash-omni 2.0 focuses on optimizing capabilities across the following key domains: - **Expert-level Multimodal Cognition**: It accurately identifies plants and animals, recognizing cultural references (from regional cuisines to global landmarks), and delivering expert-level analysis of artifacts, including era, form, and craftsmanship. By synergizing high-resolution visual capture with a vast knowledge graph, the model achieves "vision-to-knowledge" synthesis, enabling superior knowledge understanding. You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-flash-omni-Preview, Please refer to this link.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-flash-omni-Preview
inclusionAIMing-flash-omni Preview, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100B total parameters, of which only 6B are active per token. Compared to its predecessor, the upgraded version exhibits substantial improvements across multimodal understanding and generation. We significantly advance speech recognition capabilities, achieving state-of-the-art performance in both contextual ASR and dialect-aware ASR. In image generation, Ming-flash-omni Preview introduces high-fidelity text rendering and demonstrates marked gains in scene consistency and identity preservation during image editing. Furthermore, Ming-flash-omni Preview introduces generative segmentation, a capability that not only achieves strong standalone segmentation performance but also enhances spatial control in image generation and improves editing consistency. It demonstrates highly competitive results in various modal benchmarks compared to industry-leading models. Compared to Ming-lite-omni v1.5, Ming-flash-omni Preview features key optimizations in the following 3 areas: - **Sparse MoE Architecture for Omni-Modality**: The Sparse MoE Architecture for Omni-Modality features a 100B-A6B MoE backbone (an extension of Ling-Flash-2.0). To ensure uniform expert activation and stable training across all modalities, Ming-flash-omni Preview employs a Dual-Balanced Routing Mechanism that combines an Auxiliary Load Balancing Loss with a Modality-Level Router Bias Update. - **Generative Segmentation-as-Editing Paradigm**: It unifies segmentation and editing into a semantics-preserving generation task, and achieves $0.90$ on GenEval, surpassing non-RL methods in fine-grained spatial control. - **Context-Aware and Dialectal Speech Recognition**: Ming-flash-omni Preview sets a new State-of-the-Art performance across all 12 ContextASR benchmarks, and it significantly improves recognition performance for 15 Chinese dialects. You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-Lite-Omni v1.5, Please refer to this link.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-Image-0.1-Design
inclusionAI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。
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-Image-0.1-Design-Layer
inclusionAI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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-Lite-Omni-1.5
inclusionAI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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-lite-omni-1.5-FP8
inclusionAI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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-omni-tts-0.5B
inclusionAI🌐 Project Page ｜🤗 Hugging Face ｜ 🤖 ModelScope | 🎮 Gradio Demo-zh | 🎮 Gradio Demo-en | 💬 DingTalk(钉钉) Ming-omni-tts is a high-performance unified audio generation model that achieves precise control over speech attributes and enables single-channel synthesis of speech, environmental sounds, and music. Powered by a custom 12.5Hz continuous tokenizer and Patch-by-Patch compression, it delivers competitive inference efficiency (3.1Hz). Additionally, the model features robust text normalization capabilities for the accurate and natural narration of complex mathematical and chemical expressions. - 🔊 **Fine-grained Vocal Control:** The model supports precise control over speech rate, pitch, volume, emotion, and dialect through simple commands. Notably, its accuracy for Cantonese dialect control is as high as **93%**, and its emotion control accuracy reaches **46.7%**, surpassing CosyVoice3. - 🌌 **Intelligent Voice Design:** Features 100+ premium built-in voices and supports zero-shot voice design through natural language descriptions. Its performance on the Instruct-TTS-Eval-zh benchmark is on par with Qwen3-TTS. - 🎶 **Immersive Unified Generation:** The industry’s first autoregressive model to jointly generate speech, ambient sound, and music in a single channel. Built on a custom 12.5Hz continuous tokenizer and a DiT head architecture, it delivers a seamless, "in-the-scene" auditory experience. - ⚡ **High-efficiency Inference:** Introduces a "Patch-by-Patch" compression strategy that reduces the LLM inference frame rate to 3.1Hz. This significantly cuts latency and enables podcast-style audio generation while preserving naturalness and audio detail. - 🧪 **Professional Text Normalization:** The model accurately parses and narrates complex formats, including mathematical expressions and chemical equations, ensuring natural-sounding output for specialized applications.
Input Text→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ming-omni-tts-16.8B-A3B
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-32B-AWorld
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3.8-Flash-Next-singprobe
inclusionAI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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Realtime-Venus
inclusionAI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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-1T
InclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-2.5-1T
inclusionAI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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-2.6-1T
inclusionAI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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-flash-2.0
InclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-flash-linear-2.0
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-lite
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-lite-2506
inclusionAI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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-lite-2507
inclusionAI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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-lite-distill-preview
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ring-mini-2.0
inclusionAI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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.