Gemini 4 Carbon (High)
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Anthropic Claude Opus 系列模型,适用于复杂推理、分析和编码任务。支持文本和图像输入、文本输出。
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GPT-6.1 Terra is the mid-tier model in OpenAI's GPT-6.1 series, priced below GPT-6.1 Sol. It is suited for everyday agentic, coding, and writing work, is particularly strong at understanding screenshots...
面向复杂推理和编码任务的 OpenAI 模型。支持文本和图像输入、文本输出。
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Anthropic Claude Sonnet 系列模型,适用于推理、分析和编码任务。支持文本和图像输入、文本输出。
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Anthropic Claude Haiku 系列模型,适用于推理、分析和编码任务。支持文本和图像输入、文本输出。
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专为最复杂的端到端任务而设计。它可以用于复杂的推理、编程、计算机操作、研究以及文档编写等任务。 reasoning.effort 支持 low 、 medium 、 high 、 xhigh 和 max 功能。
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特殊优惠组该模型由部分资格账号,配合特定参数请求 Gemini 3.8 Flash 实现,已检测上游确实路由到 Gemini 4 这俩是 Gemini4 内部不同的检查点,据说 b 比 a 好一点,但是 b 现在的思考等级低一些, 具体智商得客户实际测试感受一下了
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deepseek-v4.1-flash-1017 是 DeepSeek V4.1 Flash 10 月 17 日快照版,MoE 多模态模型,支持图文输入、工具调用、百万级长上下文,自带思考模式,主打高吞吐、高性价比,适合 Agent、代码、长文档和识图场景。
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Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...
Kimi-K3.1-Pro is Moonshot’s flagship domestically developed multimodal large model, supporting an ultra-long context of up to one million tokens. It can process text, images, and lengthy documents, and boasts powerful reasoning, coding, and tool‑calling capabilities, making it ideal for in-depth analysis, complex agent tasks, and the interpretation of charts and blueprints.
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Claude Fable 5.1 在 Claude Fable 5 的基础上进行了扩展,输入和输出价格保持不变,缓存读取成本降至四分之一,并带来了更强的长时间运行的智能体编码、多步骤研究,以及文档、电子表格和幻灯片处理能力。对于大多数工作负载
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MiniMax-M3.1-Flash is a multimodal model from MiniMax. Compared with M3, it delivers significant improvements across multiple benchmarks, particularly in complex agentic tasks, coding, and cybersecurity. It is built for complex,...
Gemini 3.8 Flash 是我们最智能的 Flash 模型,专为长远软件工程、自主代理和复杂企业工作流程设计——所有这些都具备 Flash 的速度和成本效益。
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GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding...
600B total parameters · 27B active parameters · Up to 1M context Introduction · Capabilities · Specifications · Performance · Deployment · Resources · License Step 5 Preview is StepFun's flagship model for agentic work. It delivers frontier-level performance across software engineering and professional knowledge work, with particular strength in finance.
面向复杂推理和编码任务的 OpenAI 模型。支持文本和图像输入、文本输出及多语言能力。
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NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...
Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers...
Hugging Face | GitHub | Launch Blog | Documentation | Technical Report (coming) Beam is an open model built by Reflection. Beam takes text as input and generates text as output. This release includes the instruction-tuned model in BF16, FP8, and NVFP4 numerics. Beam uses a sparse mixture-of-experts (MoE) architecture with 501B total parameters and 23B active parameters. It supports a context window of 1M tokens and was developed predominantly for English. - **Token-efficient reasoning:** Reaches comparable solution quality to leading open models using approximately 3x fewer inference-time reasoning tokens.
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Adjust provider, category or input/output filters. New models are added continuously.
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Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
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