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MiMo-V2-Omni can seamlessly integrate with major agent frameworks, achieving a leap from understanding to manipulation and significantly lowering the barrier to deploying full-modal agents.","pricing":{"cache_read":0.088,"input":0.44,"output":2.2},"types":"llm","features":"web","input_modalities":"text,image,video,audio","endpoints":"","max_output":0,"context_length":256000,"playground_checked":false},{"model_id":"cohere-command-a","model_name":"Cohere Command A","developer_id":6,"desc":"Command A is Cohere most performant model to date, excelling at tool use, agents, retrieval augmented generation (RAG), and multilingual use cases. 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If you want to experience the full version of the model API without filters, please use the paid version and request the model name ID without \"-free\".","pricing":{"cache_read":0,"input":0,"output":0},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"gpt-4.1-nano-free","model_name":"GPT 4.1 Nano (free)","developer_id":12,"desc":"This free model API comes from the OpenAI model deployed on Azure. To prevent abuse, the external content filter provided by Azure has been enforced, which will result in additional delays. If you want to experience the full version of the model API without filters, please use the paid version and request the model name ID without \"-free\".","pricing":{"cache_read":0,"input":0,"output":0},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"gpt-4o-free","model_name":"GPT 4o (free)","developer_id":12,"desc":"This free model API comes from the OpenAI model deployed on Azure. To prevent abuse, the external content filter provided by Azure has been enforced, which will result in additional delays. If you want to experience the full version of the model API without filters, please use the paid version and request the model name ID without \"-free\".","pricing":{"cache_read":0,"input":0,"output":0},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"coding-glm-5","model_name":"Coding GLM 5","developer_id":5,"desc":"Only supports OpenAI-compatible formats.","pricing":{"input":0.06,"output":0.22},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"coding-glm-5-turbo","model_name":"Coding GLM 5 Turbo","developer_id":5,"desc":"Only supports OpenAI-compatible formats.","pricing":{"input":0.06,"output":0.22},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"glm-4.7","model_name":"GLM 4.7","developer_id":5,"desc":"GLM-4.7 is Zhiyuan's latest flagship model. 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","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm","features":"thinking,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":128000,"context_length":400000,"playground_checked":false},{"model_id":"gpt-5.1-codex-mini","model_name":"GPT-5.1-Codex Mini","developer_id":12,"desc":"GPT-5.1 Codex mini is a smaller, more cost-effective, less-capable version of GPT-5.1-Codex.","pricing":{"cache_read":0.025,"input":0.25,"output":2},"types":"llm","features":"thinking,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":128000,"context_length":400000,"playground_checked":false},{"model_id":"claude-haiku-4-5","model_name":"Claude Haiku 4.5","developer_id":2,"desc":"Claude Haiku 4.5 is a fast, affordable, and highly capable AI model, excelling at coding and agentic tasks. 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It supports precise, targeted image transformations through natural language instructions and leverages built-in world knowledge for both image generation and editing, making it well suited for creative design, content production, advertising, and visual expression workflows.","pricing":{"cache_read":0.3,"input":0.3,"output":2.499},"types":"image_generation,llm","features":"","input_modalities":"image,text","endpoints":"","max_output":8000,"context_length":32800,"playground_checked":false},{"model_id":"grok-4-1-fast-non-reasoning","model_name":"Grok 4.1 Fast","developer_id":9,"desc":"Grok 4.1 is a new conversational model with significant improvements in real-world usability, delivering exceptional performance in creative, emotional, and collaborative interactions. It is more perceptive to nuanced user intent, more engaging to converse with, and more coherent in personality, while fully preserving its core intelligence and reliability. 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It retains the practical capabilities of the Gemini 2.5 family, including configurable reasoning based on budget, integration with tools such as grounding via Google Search and code execution, multimodal input support, and an ultra-long context window of up to 1 million tokens, delivering a strong balance between efficiency, functionality, and cost.","pricing":{"cache_read":0.01,"input":0.1,"output":0.4},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-flash-lite-preview-09-2025","model_name":"Gemini 2.5 Flash Lite Preview 09 2025","developer_id":8,"desc":"gemini-2.5-flash-lite latest preview version","pricing":{"cache_read":0.01,"input":0.1,"output":0.4},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-flash-lite-preview-09-2025-nothink","model_name":"Gemini 2.5 Flash Lite Preview 09 2025 (no think)","developer_id":8,"desc":"gemini-2.5-flash-lite latest preview version","pricing":{"cache_read":0.01,"input":0.1,"output":0.4},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-flash-nothink","model_name":"Gemini 2.5 Flash (no think)","developer_id":8,"desc":"Gemini-2.5-flash defaults to thinking enabled; to disable thinking, request the name gemini-2.5-flash-nothink, which only supports OpenAI-compatible format calls and does not support Gemini SDK; for the native Gemini SDK, please set the parameter budget=0 directly.","pricing":{"cache_read":0.03,"input":0.3,"output":2.499},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1047576,"playground_checked":false},{"model_id":"gemini-2.5-flash-search","model_name":"Gemini 2.5 Flash Search","developer_id":8,"desc":"gemini-2.5-flash-search integrates Google's official search functionality; the search feature will have an additional separate fee log directly incorporated into the scoring, with detailed logs not displayed; this will be fixed and displayed later; only supports OpenAI-compatible formats for invocation, does not support Gemini SDK; for Gemini's native SDK, please set parameters directly using the official search parameters.","pricing":{"cache_read":0.03,"input":0.3,"output":2.499},"types":"llm,search","features":"web,tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-flash-preview-05-20-nothink","model_name":"Gemini 2.5 Flash Preview 05-20 (no think)","developer_id":8,"desc":"Gemini-2.5-flash-preview-05-20 is enabled by default for thinking; to disable it, request the name gemini-2.5-flash-preview-05-20-nothink.Only OpenAI-compatible format calls are supported; Gemini SDK is not supported. For the native Gemini SDK, please set the parameter budget=0 directly.","pricing":{"cache_read":0.03,"input":0.3,"output":2.499},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-flash-preview-05-20-search","model_name":"Gemini 2.5 Flash Preview 05-20 Search","developer_id":8,"desc":"Gemini-2.5 Flash Preview 05-20 Search integrates Google's official search functionality; the search feature will have an additional separate fee log directly integrated into the scoring deduction, with detailed logs not displayed. It will be fixed and displayed later. Only OpenAI-compatible formats are supported for invocation; Gemini SDK is not supported. For Gemini's native SDK, please set parameters directly using the official search parameters.","pricing":{"cache_read":0.03,"input":0.3,"output":2.499},"types":"llm,search","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"DeepSeek-V3-Fast","model_name":"DeepSeek V3 Fast","developer_id":7,"desc":"V3 Ultra-Fast Version,The current price is a limited-time 50% discount and will return to the original price on July 31st. The original price is: input: $0.55/M, output: $2.2/M. The model provider is the Sophnet platform. DeepSeek V3 Fast is a high-TPS, ultra-fast version of DeepSeek V3 0324, featuring full-precision (non-quantized) performance, enhanced code and math capabilities, and faster responses!\n\nDeepSeek V3 0324 is a powerful Mixture-of-Experts (MoE) model with a total parameter count of 671B, activating 37B parameters per token.\nIt adopts Multi-Head Latent Attention (MLA) and the DeepSeekMoE architecture to achieve efficient inference and economical training costs.\nIt innovatively implements a load balancing strategy without auxiliary loss and sets multi-token prediction training targets to enhance performance.\nThe model is pre-trained on 14.8 trillion diverse, high-quality tokens and further optimized through supervised fine-tuning and reinforcement learning stages to fully realize its capabilities.\nComprehensive evaluations show that DeepSeek V3 outperforms other open-source models and rivals leading closed-source models in performance.\nThe entire training process only requires 2.788M H800 GPU hours and remains highly stable, with no irrecoverable loss spikes or rollbacks.","pricing":{"input":0.56,"output":2.24},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":32000,"context_length":32000,"playground_checked":false},{"model_id":"o4-mini","model_name":"O4 Mini","developer_id":12,"desc":"o4-mini is a remarkably smart model for its speed and cost-efficiency. This allows it to support significantly higher usage limits than o3, making it a strong high-volume, high-throughput option for everyone with questions that benefit from reasoning.","pricing":{"cache_read":0.275,"input":1.1,"output":4.4},"types":"llm","features":"thinking,tool,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":100000,"context_length":200000,"playground_checked":false},{"model_id":"qwen3-vl-235b-a22b-thinking","model_name":"Qwen3 VL 235B A22B Thinking","developer_id":13,"desc":"The Qwen3 series open-source models include hybrid models, thinking models, and non-thinking models, with both reasoning capabilities and general abilities reaching industry SOTA levels at the same scale.","pricing":{"input":0.274,"output":2.74},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":33000,"context_length":131000,"playground_checked":false},{"model_id":"kimi-k2-instruct","model_name":"Kimi K2 Instruct","developer_id":15,"desc":"Kimi-K2 is a MoE architecture foundational model with extremely powerful coding and agent capabilities, featuring a total of 1 trillion parameters and activating 32 billion parameters. In benchmark performance tests across major categories such as general knowledge reasoning, programming, mathematics, and agents, the K2 model outperforms other mainstream open-source models.\nThe Kimi-K2 model supports a context length of 128k tokens.\nIt does not support visual capabilities.","pricing":{"input":0.54,"output":2.16},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"kimi-k2-turbo-preview","model_name":"Kimi K2 Turbo Preview","developer_id":15,"desc":"The kimi-k2-turbo-preview model is a high-speed version of kimi-k2, with the same model parameters as kimi-k2, but the output speed has been increased from 10 tokens per second to 40 tokens per second.","pricing":{"cache_read":0.3,"input":1.2,"output":4.8},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":262144,"context_length":262144,"playground_checked":false},{"model_id":"qwen3-vl-30b-a3b-thinking","model_name":"Qwen3 VL 30B A3B Thinking","developer_id":13,"desc":"The Qwen3-VL series’ second-largest MoE model Thinking version offers fast response speed, stronger multimodal understanding and reasoning, visual agent capabilities, and ultra-long context support for long videos and long documents; it features comprehensive upgrades in image/video understanding, spatial perception, and universal recognition abilities, making it capable of handling complex real-world tasks.","pricing":{"input":0.1028,"output":1.028},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":32000,"context_length":128000,"playground_checked":false},{"model_id":"qwen3-vl-30b-a3b-instruct","model_name":"Qwen3 VL 30B A3B Instruct","developer_id":13,"desc":"The Qwen3-VL series’ second-largest MoE model Instruct version offers fast response speed and supports ultra-long contexts such as long videos and long documents; it features comprehensive upgrades in image/video understanding, spatial perception, and universal recognition abilities; it also provides visual 2DD/3D localization capabilities, making it capable of handling complex real-world tasks.","pricing":{"input":0.1028,"output":0.4112},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":32000,"context_length":128000,"playground_checked":false},{"model_id":"qwen3-vl-235b-a22b-instruct","model_name":"Qwen3 VL 235B A22B Instruct","developer_id":13,"desc":"The Qwen3 series open-source models include hybrid models, thinking models, and non-thinking models, with both reasoning capabilities and general abilities reaching industry SOTA levels at the same scale.","pricing":{"input":0.274,"output":1.096},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":33000,"context_length":131000,"playground_checked":false},{"model_id":"kimi-k2-0711","model_name":"Kimi K2 0711","developer_id":15,"desc":"Kimi-K2 is a MoE architecture foundational model with extremely powerful coding and agent capabilities, featuring a total of 1 trillion parameters and activating 32 billion parameters. In benchmark performance tests across major categories such as general knowledge reasoning, programming, mathematics, and agents, the K2 model outperforms other mainstream open-source models.\nThe Kimi-K2 model supports a context length of 128k tokens.\nIt does not support visual capabilities.","pricing":{"input":0.54,"output":2.16},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":131000,"context_length":131000,"playground_checked":false},{"model_id":"DeepSeek-OCR","model_name":"DeepSeek Ocr","developer_id":7,"desc":"DeepSeek-OCR is a vision-language model launched by DeepSeek AI, focusing on optical character recognition (OCR) and “contextual optical compression.” The model is designed to explore the limits of compressing contextual information from images, efficiently processing documents and converting them into structured text formats such as Markdown. The model requires an image as input.","pricing":{"input":0.02,"output":0.02},"types":"llm","features":"","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":8000,"playground_checked":false},{"model_id":"ernie-5.0-thinking-exp","model_name":"ERNIE 5.0 Thinking Exp","developer_id":25,"desc":"ERNIE 5.0 is the next-generation natively multimodal foundation model in the ERNIE family. Built on a unified multimodal architecture, it jointly learns from text, images, audio, and video to deliver broad multimodal capabilities.\n\nERNIE 5.0 features significantly upgraded core capabilities and shows strong performance across benchmarks, with notable gains in multimodal understanding, instruction following, creative writing, factual accuracy, and agent planning with tool use.","pricing":{"cache_read":0.82192,"input":0.82192,"output":3.28768},"types":"llm","features":"thinking","input_modalities":"text,image","endpoints":"","max_output":64000,"context_length":119000,"playground_checked":false},{"model_id":"gpt-4.1","model_name":"GPT 4.1","developer_id":12,"desc":"The latest flagship multimodal model supports million-token context, with encoding capability (SWE-bench 54.6%) and instruction-following (Scale AI 38.3%) performance significantly surpassing GPT-4o, while reducing costs by 26%, making it suitable for complex tasks. Its automatic caching mechanism offers a 75% cost reduction on cache hits.","pricing":{"cache_read":0.5,"input":2,"output":8},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"grok-4","model_name":"Grok 4","developer_id":9,"desc":"Grok, their latest and greatest flagship model, offers unparalleled performance in natural language, math, and reasoning – the perfect jack of all trades.\nThe current pointing model version is grok-4-0709.","pricing":{"cache_read":0.825,"input":3.3,"output":16.5},"types":"llm","features":"function_calling,structured_outputs,thinking","input_modalities":"text,image","endpoints":"","max_output":64000,"context_length":256000,"playground_checked":false},{"model_id":"grok-4-fast-non-reasoning","model_name":"Grok 4 Fast","developer_id":9,"desc":"Grok-4-fast is a cost-effective inference model developed by xAI that delivers cutting-edge performance with excellent token efficiency. The model features a 2 million token context window, advanced Web and X search capabilities, and a unified architecture supporting both \"inference\" and \"non-inference\" modes. Compared to Grok 4, it reduces thinking tokens by an average of 40% and lowers the price by 98% while achieving the same performance.","pricing":{"cache_read":0.05,"input":0.2,"output":0.5},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":30000,"context_length":2000000,"playground_checked":false},{"model_id":"grok-4-fast-reasoning","model_name":"Grok 4 Fast (reasoning)","developer_id":9,"desc":"Grok-4-fast is a cost-effective inference model developed by xAI that delivers cutting-edge performance with excellent token efficiency. The model features a 2 million token context window, advanced Web and X search capabilities, and a unified architecture supporting both \"inference\" and \"non-inference\" modes. Compared to Grok 4, it reduces thinking tokens by an average of 40% and lowers the price by 98% while achieving the same performance.","pricing":{"cache_read":0.05,"input":0.2,"output":0.5},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":30000,"context_length":2000000,"playground_checked":false},{"model_id":"aihubmix-router","model_name":"Aihubmix Router","developer_id":12,"desc":"New model routing capability; request aihubmix-router to automatically route models based on question complexity, so everyone no longer needs to manually switch models; in our tests comparing the use of the model router versus only using GPT-4.1, we observed up to 60% cost savings while maintaining similar accuracy.  \nThe context length of the model router depends on the base model used for each prompt. Input size is 200,000, output size is 32,768.  \nCurrently, there are four routing models: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, o4-mini.  \nPricing: Due to our current billing structure system, requests through aihubmix-router are billed at the price of gpt-4.1-mini regardless of which final model is used; future billing will be based on the actual model invoked.  \nEveryone is welcome to try it out; the interface will return the name of the actual called model.","pricing":{"cache_read":0.1,"input":0.4,"output":1.6},"types":"llm","features":"","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"gpt-4.1-mini","model_name":"GPT 4.1 Mini","developer_id":12,"desc":"Lightweight, high-performance model with million-token context and near-flagship-level encoding and image understanding capabilities, while reducing costs by 83%. It is suitable for rapid development and small to medium-sized applications. The automatic caching mechanism provides a 75% cost reduction on cache hits.","pricing":{"cache_read":0.1,"input":0.4,"output":1.6},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"gpt-4.1-nano","model_name":"GPT 4.1 Nano","developer_id":12,"desc":"Ultra-lightweight model with million-token context, optimized for speed and low latency, costing only $0.10 per million input tokens. It is suitable for edge computing and real-time interaction. The automatic caching mechanism offers a 75% cost reduction on cache hits.","pricing":{"cache_read":0.025,"input":0.1,"output":0.4},"types":"llm","features":"tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":32768,"context_length":1047576,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-05-06","model_name":"Gemini 2.5 Pro Preview 05-06","developer_id":8,"desc":"gemini-2.5-pro latest model","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm","features":"thinking,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-03-25","model_name":"Gemini 2.5 Pro Preview 03-25","developer_id":8,"desc":"Supports high concurrency.  \nThe Gemini 2.5 Pro preview version is here, with higher limits for production testing.  \nGoogle's latest and most powerful model;","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm","features":"thinking,tools,function_calling,structured_outputs,long_context","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-05-06-search","model_name":"Gemini 2.5 Pro Preview 05-06 Search","developer_id":8,"desc":"Integrated with Google's official search function.","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm,search","features":"thinking,web","input_modalities":"text,image,audio,video","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-03-25-search","model_name":"Gemini 2.5 Pro Preview 03-25 Search","developer_id":8,"desc":"Integrated with Google's official search function.","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm,search","features":"thinking,web,tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"qwen3-max-preview","model_name":"Qwen3 Max Preview","developer_id":13,"desc":"Qwen3-Max-Preview is the latest preview model in the Qwen3 series. This version is functionally equivalent to Qwen3-Max-Thinking — simply set extra_body={\"enable_thinking\": True} to enable the thinking mode. Compared to the Qwen2.5 series, it delivers significant improvements in overall general capabilities, including English–Chinese text understanding, complex instruction following, open-ended reasoning, multilingual processing, and tool-use proficiency. The model also exhibits fewer hallucinations and stronger overall reliability.","pricing":{"cache_read":0.1692,"input":0.846,"output":3.384},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"qwen3-next-80b-a3b-thinking","model_name":"Qwen3 Next 80B A3B Thinking","developer_id":13,"desc":"Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that excels by outputting structured 'thinking' traces (Chain-of-Thought) by default.\n\nDesigned for hard, multi-step problems, it is ideal for tasks like math proofs, code synthesis, logic puzzles, and agentic planning. Compared to other Qwen3 variants, it offers greater stability during long reasoning chains and is tuned to follow complex instructions without getting repetitive or off-task.\n\nThis model is perfectly suited for agent frameworks, tool use (function calling), and benchmarks where a step-by-step breakdown is required. It leverages throughput-oriented techniques for fast generation of detailed, procedural outputs.","pricing":{"input":0.142,"output":1.42},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":256000,"context_length":256000,"playground_checked":false},{"model_id":"qwen3-next-80b-a3b-instruct","model_name":"Qwen3 Next 80B A3B Instruct","developer_id":13,"desc":"Qwen3-Next-80B-A3B-Instruct is an instruction-tuned model in the Qwen3-Next series, optimized for delivering fast, stable, and direct final answers without showing its reasoning steps (\"thinking traces\").\n\nUnlike chain-of-thought models, it focuses on generating consistent, instruction-following outputs, making it ideal for production environments. It excels at complex tasks like reasoning and coding while maintaining high throughput and stability, especially with ultra-long inputs and multi-turn dialogues.\n\nEngineered for efficiency, its performance rivals larger Qwen3 systems, making it perfectly suited for RAG, tool use, and agentic workflows where deterministic results are critical.","pricing":{"input":0.138,"output":0.552},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":256000,"context_length":256000,"playground_checked":false},{"model_id":"qwen3-max","model_name":"Qwen3 Max","developer_id":13,"desc":"The Tongyi Qianwen 3 series Max model has undergone special upgrades in intelligent agent programming and tool invocation compared to the preview version. The officially released model this time reaches SOTA level in the field and is adapted to more complex intelligent agent scenarios.","pricing":{"cache_read":0.09016,"cache_write":0.5635,"input":0.4508,"output":1.8032},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":65536,"context_length":262144,"playground_checked":false},{"model_id":"qwen3-235b-a22b-thinking-2507","model_name":"Qwen3 235B A22B Thinking 2507","developer_id":13,"desc":"The open-source thinking model based on Qwen3 has significantly improved in logical ability, general capability, knowledge enhancement, and creative ability compared to the previous version (Tongyi Qianwen 3-235B-A22B). It is suitable for high-difficulty and strong reasoning scenarios.","pricing":{"input":0.28,"output":2.8},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":262144,"context_length":262144,"playground_checked":false},{"model_id":"qwen3-235b-a22b-instruct-2507","model_name":"Qwen3 235B A22B Instruct 2507","developer_id":13,"desc":"Qwen3-235B-A22B-Instruct-2507","pricing":{"input":0.28,"output":1.12},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":262144,"context_length":262144,"playground_checked":false},{"model_id":"qwen3-coder-30b-a3b-instruct","model_name":"Qwen3 Coder 30B A3B Instruct","developer_id":13,"desc":"The code generation model based on Qwen3 has powerful Coding Agent capabilities, achieving state-of-the-art performance compared to open-source models.The model adopts tiered pricing.","pricing":{"cache_read":0.2,"input":0.2,"output":0.8},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":262000,"context_length":2000000,"playground_checked":false},{"model_id":"qwen3-coder-480b-a35b-instruct","model_name":"Qwen3 Coder 480B A35B Instruct","developer_id":13,"desc":"The code generation model based on Qwen3 has powerful Coding Agent capabilities, achieving state-of-the-art performance compared to open-source models.The model adopts tiered pricing.","pricing":{"cache_read":0.82,"input":0.82,"output":3.28},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":262000,"context_length":262000,"playground_checked":false},{"model_id":"qwen3-coder-plus","model_name":"Qwen3 Coder Plus","developer_id":13,"desc":"The code generation model based on Qwen3 has powerful Coding Agent capabilities, excels in tool invocation and environment interaction, and can achieve autonomous programming with outstanding coding abilities while also possessing general capabilities.The model adopts tiered pricing.","pricing":{"cache_read":0.108,"input":0.54,"output":2.16},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"qwen3-coder-flash","model_name":"Qwen3 Coder Flash","developer_id":13,"desc":"Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling and environment interaction, combining coding proficiency with versatile general-purpose abilities.","pricing":{"cache_read":0.136,"input":0.136,"output":0.544},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":65536,"context_length":256000,"playground_checked":false},{"model_id":"qwen3-coder-plus-2025-07-22","model_name":"Qwen3 Coder Plus 2025 07-22","developer_id":13,"desc":"The code generation model based on Qwen3 has powerful Coding Agent capabilities, excels in tool invocation and environment interaction, and can achieve autonomous programming with outstanding coding abilities while also possessing general capabilities.The model adopts tiered pricing.","pricing":{"cache_read":0.54,"input":0.54,"output":2.16},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":65536,"context_length":128000,"playground_checked":false},{"model_id":"qwen3-235b-a22b","model_name":"Qwen3 235B A22B","developer_id":13,"desc":"Qwen3-235B-A22B is a massive 235B parameter Mixture-of-Experts (MoE) model that operates with the efficiency of a 22B model. Its standout feature is the ability to seamlessly switch between a \"thinking\" mode for complex reasoning and a \"non-thinking\" mode for fast conversation, offering both world-class power and practical speed.","pricing":{"input":0.28,"output":1.12},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":128000,"context_length":131100,"playground_checked":false},{"model_id":"DeepSeek-V3","model_name":"DeepSeek V3","developer_id":7,"desc":"It has been automatically upgraded to the latest released version, 250324.\nAutomatically upgraded to the latest released version 250324.","pricing":{"input":0.272,"output":1.088},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":1638000,"context_length":1638000,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-06-05-search","model_name":"Gemini 2.5 Pro Preview 06-05 Search","developer_id":8,"desc":"Integrated with Google's official search function.","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm,search","features":"thinking,web,tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"ernie-5.0-thinking-preview","model_name":"ERNIE 5.0 Thinking Preview","developer_id":25,"desc":"The new generation Wenxin model, Wenxin 5.0, is a native full-modal large model that adopts native full-modal unified modeling technology, jointly modeling text, images, audio, and video, possessing comprehensive full-modal capabilities. Wenxin 5.0's basic abilities are comprehensively upgraded, performing excellently on benchmark test sets, especially in multimodal understanding, instruction compliance, creative writing, factual accuracy, intelligent agent planning, and tool application.","pricing":{"cache_read":0.822,"input":0.822,"output":3.288},"types":"llm","features":"thinking,structured_outputs,function_calling","input_modalities":"text","endpoints":"","max_output":64000,"context_length":183000,"playground_checked":false},{"model_id":"inclusionAI/Ling-1T","model_name":"Ling 1t","developer_id":29,"desc":"Ling-1T is the first flagship non-thinking model in the “Ling 2.0” series, featuring 1 trillion total parameters and approximately 50 billion active parameters per token. Built on the Ling 2.0 architecture, Ling-1T is designed to push the limits of efficient inference and scalable cognition. Ling-1T-base was pretrained on over 20 trillion high-quality, reasoning-intensive tokens, supports up to a 128K context length, and incorporates an Evolutionary Chain of Thought (Evo-CoT) process during mid-stage and post-stage training. This training regimen greatly enhances the model’s efficiency and depth of reasoning, enabling Ling-1T to achieve top performance across multiple complex reasoning benchmarks, balancing accuracy and efficiency.","pricing":{"input":0.548,"output":2.192},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"inclusionAI/Ring-1T","model_name":"Ring 1t","developer_id":29,"desc":"Ring-1T is an open-source idea model with a trillion parameters released by the Bailing team. It is based on the Ling 2.0 architecture and the Ling-1T-base foundational model for training, with a total parameter count of 1 trillion, an active parameter count of 50 billion, and supports up to a 128K context window. The model is trained via large-scale verifiable reward reinforcement learning (RLVR), combined with the self-developed Icepop reinforcement learning stabilization method and the efficient ASystem reinforcement learning system, significantly improving the model’s deep reasoning and natural language reasoning capabilities. Ring-1T achieves leading performance among open-source models on high-difficulty reasoning benchmarks such as mathematics competitions (e.g., IMO 2025), code generation (e.g., ICPC World Finals 2025), and logical reasoning.","pricing":{"input":0.548,"output":2.192},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"codex-mini-latest","model_name":"Codex Mini","developer_id":12,"desc":"Only supports v1/responses API calls.https://docs.aihubmix.com/en/api/Responses-API\ncodex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI. For direct use in the API, we recommend starting with gpt-4.1.","pricing":{"cache_read":0.375,"input":1.5,"output":6},"types":"llm","features":"","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"ernie-4.5-turbo-latest","model_name":"ERNIE 4.5 Turbo","developer_id":25,"desc":"Wenxin 4.5 Turbo also has significant improvements in hallucination reduction, logical reasoning, and coding capabilities. Compared to Wenxin 4.5, it is faster and more affordable.","pricing":{"input":0.11,"output":0.44},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":12000,"context_length":135000,"playground_checked":false},{"model_id":"inclusionAI/Ling-flash-2.0","model_name":"Ling Flash 2.0","developer_id":29,"desc":"Ling-flash-2.0 is a language model from inclusionAI with a total of 100 billion parameters, of which 6.1 billion are activated per token (4.8 billion non-embedding). As part of the Ling 2.0 architecture series, it is designed as a lightweight yet powerful Mixture-of-Experts (MoE) model. It aims to deliver performance comparable to or even exceeding that of 40B-level dense models and other larger MoE models, but with a significantly smaller active parameter count. The model represents a strategy focused on achieving high performance and efficiency through extreme architectural design and training methods.","pricing":{"input":0.136,"output":0.544},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"inclusionAI/Ling-mini-2.0","model_name":"Ling Mini 2.0","developer_id":29,"desc":"Ling-mini-2.0 is a small-sized, high-performance large language model based on the MoE architecture. It has a total of 16 billion parameters, but only activates 1.4 billion parameters per token (non-embedding 789 million), achieving extremely high generation speed. Thanks to the efficient MoE design and large-scale high-quality training data, despite activating only 1.4 billion parameters, Ling-mini-2.0 still demonstrates top-tier performance on downstream tasks comparable to dense LLMs under 10 billion parameters and even larger-scale MoE models.","pricing":{"input":0.068,"output":0.272},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"inclusionAI/Ring-flash-2.0","model_name":"Ring Flash 2.0","developer_id":29,"desc":"Ring-flash-2.0 is a high-performance thinking model deeply optimized based on the Ling-flash-2.0-base. It uses a mixture-of-experts (MoE) architecture with a total of 100 billion parameters, but only activates 6.1 billion parameters per inference. The model employs the original Icepop algorithm to solve the instability issues of large MoE models during reinforcement learning (RL) training, enabling its complex reasoning capabilities to continuously improve over long training cycles. Ring-flash-2.0 has achieved significant breakthroughs on multiple high-difficulty benchmarks, including mathematics competitions, code generation, and logical reasoning. Its performance not only surpasses top dense models under 40 billion parameters but also rivals larger open-source MoE models and closed-source high-performance thinking models. Although the model focuses on complex reasoning, it also performs exceptionally well on creative writing tasks. Furthermore, thanks to its efficient architecture, Ring-flash-2.0 delivers high performance with low-latency inference, significantly reducing deployment costs in high-concurrency scenarios.","pricing":{"input":0.136,"output":0.544},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":0,"context_length":0,"playground_checked":false},{"model_id":"jina-deepsearch-v1","model_name":"Jina Deepsearch V1","developer_id":22,"desc":"DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started.  \nThe stream will return the thinking process.","pricing":{"input":0.05,"output":0.05},"types":"llm,search","features":"thinking,web,deepsearch","input_modalities":"text,image","endpoints":"","max_output":0,"context_length":1000000,"playground_checked":false},{"model_id":"qwen-mt-turbo","model_name":"Qwen Mt Turbo","developer_id":13,"desc":"Based on the comprehensive upgrade of Qwen3, this flagship translation large model supports bidirectional translation across 92 languages. It offers fully enhanced model performance and translation quality, along with more stable terminology customization, format fidelity, and domain-prompting capabilities, making translations more accurate and natural.","pricing":{"input":0.192,"output":0.534912},"types":"llm","features":"","input_modalities":"text","endpoints":"","max_output":8000,"context_length":16000,"playground_checked":false},{"model_id":"qwen-mt-plus","model_name":"Qwen Mt Plus","developer_id":13,"desc":"Based on the comprehensive upgrade of Qwen3, this flagship translation large model supports bidirectional translation across 92 languages. It offers fully enhanced model performance and translation quality, along with more stable terminology customization, format fidelity, and domain-prompting capabilities, making translations more accurate and natural.","pricing":{"input":0.492,"output":1.476},"types":"llm","features":"","input_modalities":"text","endpoints":"","max_output":8000,"context_length":16000,"playground_checked":false},{"model_id":"llama-4-maverick","model_name":"Llama 4 Maverick","developer_id":11,"desc":"Llama 4 Maverick is a high-capacity Mixture-of-Experts (MoE) model from Meta, featuring 400B total parameters and 128 experts, while activating an efficient 17B parameters per inference. Engineered for peak performance, it excels at advanced multimodal tasks.\n\nMaverick natively supports text and image input, producing multilingual text and code. With a 1-million-token context window and instruction tuning, it is optimized for complex image reasoning and general-purpose assistant-like interactions.\n\nReleased under the Llama 4 Community License, Maverick is ideal for research and commercial applications demanding state-of-the-art multimodal understanding and high throughput.","pricing":{"input":0.2,"output":0.2},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":32000,"context_length":1048576,"playground_checked":false},{"model_id":"llama-4-scout","model_name":"Llama 4 Scout","developer_id":11,"desc":"Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages.\n\nDesigned for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.","pricing":{"input":0.2,"output":0.2},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":131000,"context_length":131000,"playground_checked":false},{"model_id":"DeepSeek-R1","model_name":"DeepSeek R1","developer_id":7,"desc":"DeepSeek R1 is a new open-source model with performance on par with OpenAI's o1 and features fully open reasoning tokens. It is a 671B-parameter Mixture-of-Experts (MoE) model that activates 37B parameters during inference.","pricing":{"input":0.4,"output":2},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":1638000,"context_length":1638000,"playground_checked":false},{"model_id":"gpt-4o-search-preview","model_name":"GPT 4o Search Preview","developer_id":12,"desc":"Using the Chat Completions API, you can directly access the fine-tuned models and tool used by Search in ChatGPT.\n\nWhen using Chat Completions, the model always retrieves information from the web before responding to your query. To use web_search_preview as a tool that models like gpt-4o and gpt-4o-mini invoke only when necessary, switch to using the Responses API.\n\nCurrently, you need to use one of these models to use web search in Chat Completions:\n\ngpt-4o-search-preview\ngpt-4o-mini-search-preview\nWeb search parameter example\nimport OpenAI from \"openai\";\nconst client = new OpenAI();\n\nconst completion = await client.chat.completions.create({\n    model: \"gpt-4o-search-preview\",\n    web_search_options: {},\n    messages: [{\n        \"role\": \"user\",\n        \"content\": \"What was a positive news story from today?\"\n    }],\n});\n\nconsole.log(completion.choices[0].message.content);\nOutput and citations\nThe API response item in the choices array will include:\n\nmessage.content with the text result from the model, inclusive of any inline citations\nannotations with a list of cited URLs\nBy default, the model's response will include inline citations for URLs found in the web search results. In addition to this, the url_citation annotation object will contain the URL and title of the cited source, as well as the start and end index characters in the model's response where those sources were used.","pricing":{"cache_read":1.25,"input":2.5,"output":10},"types":"llm,search","features":"web,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":16384,"context_length":128000,"playground_checked":false},{"model_id":"gpt-4o-mini-search-preview","model_name":"GPT 4o Mini Search Preview","developer_id":12,"desc":"Using the Chat Completions API, you can directly access the fine-tuned models and tool used by Search in ChatGPT.\n\nWhen using Chat Completions, the model always retrieves information from the web before responding to your query. To use web_search_preview as a tool that models like gpt-4o and gpt-4o-mini invoke only when necessary, switch to using the Responses API.\n\nCurrently, you need to use one of these models to use web search in Chat Completions:\n\ngpt-4o-search-preview\ngpt-4o-mini-search-preview\nWeb search parameter example\nimport OpenAI from \"openai\";\nconst client = new OpenAI();\n\nconst completion = await client.chat.completions.create({\n    model: \"gpt-4o-search-preview\",\n    web_search_options: {},\n    messages: [{\n        \"role\": \"user\",\n        \"content\": \"What was a positive news story from today?\"\n    }],\n});\n\nconsole.log(completion.choices[0].message.content);\nOutput and citations\nThe API response item in the choices array will include:\n\nmessage.content with the text result from the model, inclusive of any inline citations\nannotations with a list of cited URLs\nBy default, the model's response will include inline citations for URLs found in the web search results. In addition to this, the url_citation annotation object will contain the URL and title of the cited source, as well as the start and end index characters in the model's response where those sources were used.","pricing":{"cache_read":0.075,"input":0.15,"output":0.6},"types":"llm,search","features":"web,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":16384,"context_length":128000,"playground_checked":false},{"model_id":"claude-3-7-sonnet","model_name":"Claude 3.7 Sonnet","developer_id":2,"desc":"Support for the thinking parameter through the original Claude SDK.","pricing":{"input":3.3,"output":16.5},"types":"llm","features":"thinking,tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":128000,"context_length":200000,"playground_checked":false},{"model_id":"ernie-4.5","model_name":"ERNIE 4.5","developer_id":25,"desc":"Wenxin Large Model 4.5 is a next-generation native multimodal foundational model independently developed by Baidu. It achieves collaborative optimization through joint modeling of multiple modalities, demonstrating excellent multimodal understanding capabilities; it possesses more advanced language abilities, with comprehensive improvements in comprehension, generation, logic, and memory, as well as significant enhancements in hallucination reduction, logical reasoning, and coding capabilities.ERNIE-4.5-21B-A3B is an aligned open-source model with a MoE structure, having a total of 21 billion parameters and 3 billion activated parameters.","pricing":{"input":0.068,"output":0.272},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":64000,"context_length":160000,"playground_checked":false},{"model_id":"ernie-4.5-turbo-vl","model_name":"ERNIE 4.5 Turbo VL","developer_id":25,"desc":"The new version of the Wenxin Yiyan large model significantly improves capabilities in image understanding, creation, translation, and coding. It supports a context length of up to 32K tokens for the first time, with a notable reduction in the latency of the first token.","pricing":{"input":0.4,"output":1.2},"types":"llm","features":"tools,function_calling,structured_outputs","input_modalities":"text,image","endpoints":"","max_output":16000,"context_length":139000,"playground_checked":false},{"model_id":"mimo-v2-flash-free","model_name":"MiMo V2 Flash (free)","developer_id":31,"desc":"MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. It adopts a MoE architecture with 309B total parameters and 15B active parameters per inference, balancing performance and efficiency. The model features a hybrid attention architecture, supports a hybrid-thinking toggle, and offers a 256K context window, enabling strong capabilities in complex reasoning, code generation, and agent-based scenarios. On SWE-bench Verified and SWE-bench Multilingual, MiMo-V2-Flash ranks #1 among open-source models globally, delivering performance comparable to Claude Sonnet 4.5 while costing only about 3.5% as much.","pricing":{"cache_read":0,"input":0,"output":0},"types":"llm","features":"web","input_modalities":"text","endpoints":"","max_output":256000,"context_length":256000,"playground_checked":false},{"model_id":"o3-mini","model_name":"O3 Mini","developer_id":12,"desc":"OpenAI's latest fast inference model excels at STEAM tasks and offers exceptional cost-effectiveness. Official support for cache hits reduces input prices by half.","pricing":{"cache_read":0.55,"input":1.1,"output":4.4},"types":"llm","features":"thinking","input_modalities":"text,image","endpoints":"","max_output":100000,"context_length":200000,"playground_checked":false},{"model_id":"doubao-seed-1-6","model_name":"Doubao Seed 1.6","developer_id":4,"desc":"Doubao-Seed-1.6 is a brand new multimodal deep reasoning model that supports four types of reasoning effort: minimal, low, medium, and high. It offers stronger model performance, serving complex tasks and challenging scenarios. It supports a 256k context window, with output length up to a maximum of 32k tokens.","pricing":{"cache_read":0.036,"input":0.18,"output":1.8},"types":"llm","features":"thinking，tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":32000,"context_length":256000,"playground_checked":false},{"model_id":"doubao-seed-1-6-flash","model_name":"Doubao Seed 1.6 Flash","developer_id":4,"desc":"Doubao-Seed-1.6-flash is an extremely fast multimodal deep thinking model, with TPOT requiring only 10ms. It supports both text and visual understanding, with its text comprehension skills surpassing the previous generation lite model and its visual understanding on par with competitor's pro series models. It supports a 256k context window and an output length of up to 16k tokens.","pricing":{"cache_read":0.0088,"input":0.044,"output":0.44},"types":"llm","features":"thinking，tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":33000,"context_length":256000,"playground_checked":false},{"model_id":"doubao-seed-1-6-lite","model_name":"Doubao Seed 1.6 Lite","developer_id":4,"desc":"Doubao-Seed-1.6-lite is a brand new multimodal deep reasoning model that supports adjustable reasoning effort, with four modes: Minimal, Low, Medium, and High. It offers better cost performance, making it the best choice for common tasks, with a context window of up to 256k.","pricing":{"cache_read":0.0164,"input":0.082,"output":0.656},"types":"llm","features":"thinking，tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":32000,"context_length":256000,"playground_checked":false},{"model_id":"doubao-seed-1-6-thinking","model_name":"Doubao Seed 1.6 Thinking","developer_id":4,"desc":"The Doubao-Seed-1.6-thinking model has significantly enhanced reasoning capabilities. Compared with Doubao-1.5-thinking-pro, it has further improvements in fundamental abilities such as coding, mathematics, and logical reasoning, and now also supports visual understanding. It supports a 256k context window, with output length supporting up to 16k tokens.","pricing":{"cache_read":0.036,"input":0.18,"output":1.8},"types":"llm","features":"thinking，tools,function_calling,structured_outputs","input_modalities":"text,image,video","endpoints":"","max_output":32000,"context_length":256000,"playground_checked":false},{"model_id":"gpt-oss-120b","model_name":"gpt-oss-120b","developer_id":12,"desc":"gpt-oss-120b is a 117B-parameter open-weight Mixture-of-Experts (MoE) language model from OpenAI, designed for high-reasoning, agentic, and general-purpose production use cases. Activating just 5.1B parameters per pass, it is optimized to run on a single H100 GPU with native MXFP4 quantization. The model features configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.","pricing":{"input":0.18,"output":0.9},"types":"llm","features":"thinking,function_calling,structured_outputs","input_modalities":"text","endpoints":"","max_output":32768,"context_length":131072,"playground_checked":false},{"model_id":"gemini-2.5-pro-preview-06-05","model_name":"Gemini 2.5 Pro Preview 06-05","developer_id":8,"desc":"Google’s latest multimodal flagship model, combining exceptional coding and reasoning capabilities. Its massive 1 million token context window (soon to expand to 2 million) places it at the top of the WebDevArena and LMArena leaderboards. It is particularly well-suited for developing aesthetically pleasing and highly functional interactive web applications, code transformation, and complex workflows. The newly introduced \"reasoning budget\" feature cleverly balances cost and performance, while optimized tool calls and response styles further enhance development efficiency, making it the ideal choice for rapid prototyping and advanced coding.","pricing":{"cache_read":0.125,"input":1.25,"output":10},"types":"llm","features":"thinking,tools,function_calling,structured_outputs,long_context","input_modalities":"text,image,audio,video","endpoints":"","max_output":65536,"context_length":1048576,"playground_checked":false},{"model_id":"Qwen/Qwen2.5-VL-72B-Instruct","model_name":"Qwen2.5 VL 72B Instruct","developer_id":13,"desc":"Qwen2.5-VL is a visual language model from the Qwen2.5 series, equipped with strong visual understanding and reasoning capabilities. It can recognize objects, analyze text and charts, understand key events in long videos, and accurately locate targets within images. 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