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Explore models served by NEAR AI. Compare capabilities, context, privacy, and pricing across its available routes.
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Claude Fable 5 is Anthropic's Mythos-class model released for general use, built for demanding reasoning and long-horizon agentic work. It takes text and image input and produces text output, with a 1M-token context window, up to 128K output tokens and always-on adaptive thinking.
Gemini 3.5 Flash is a Flash model in Google's Gemini 3 series, optimized for real-world tasks at higher speed and lower cost. It supports thinking. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window and up to 65K output tokens.
GPT-5.5 is OpenAI's GPT-5 series model for coding and complex professional work. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to xhigh.
DeepSeek V4.1 Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters (8B active on input, 16B on output) and a 1M-token context window. It natively processes images and text, with strong reasoning, coding, and agentic performance.
Grok 4.6 is xAI's model for coding, agentic tasks and knowledge work, designed for long, multi-step work such as research, codebase tasks and turning an idea into a working application. It takes text and image input and produces text output, with a 500K-token context window, function calling, structured outputs and low to xhigh reasoning effort.
Grok 4.7 is xAI's model for software engineering and professional knowledge work, especially long-running tasks. Compared with Grok 4.6 it uses a larger base model trained longer on harder tasks and checks its own output more carefully, and it takes text and image input with a 500K-token context window and low to xhigh reasoning effort.
Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work such as long code refactors, front-end development, and finance and analysis tasks. It features a 1M token context window, 128K max output tokens, always-on adaptive thinking, and strong multimodal capabilities, and tends to be more concise in its plans and summaries.
Claude Opus 4.6 is Anthropic's Opus model for demanding coding, long-running agentic work and knowledge work such as research, financial analysis and building documents. It takes text and image input and produces text output, with a 1M-token context window, up to 128K output tokens and adaptive thinking with effort controls.
Claude Opus 4.7 is Anthropic's Opus model for advanced software engineering, especially complex, long-running coding tasks. It builds on Opus 4.6 with more precise instruction following, verification of its own outputs and higher-resolution vision, and offers a 1M-token context window, up to 128K output tokens and adaptive thinking.
Claude Opus 5 is Anthropic's Opus model for daily professional work, with gains over Opus 4.8 in software engineering, knowledge work and scientific research. It checks its own work and iterates until a task succeeds, and takes text and image input with a 1M-token context window, up to 128K output tokens and adaptive thinking.
Claude Opus 5.5 is Anthropic's Opus model for long-running agentic coding and knowledge work. Compared with Opus 5 it uses fewer tokens per task and writes more clearly, and it takes text and image input with a 1M-token context window, up to 128K output tokens and always-on adaptive thinking.
Claude Sonnet 4.5 is Anthropic's balanced model offering strong performance on coding, reasoning, and general tasks with good speed and cost efficiency.
Claude Sonnet 4.6 is Anthropic's best combination of speed and intelligence, offering strong performance on coding, reasoning, and general tasks with excellent speed and cost efficiency. It features a 1M token context window and 64K max output tokens.
Claude Sonnet 5 is Anthropic's Sonnet model built for agentic work: planning, using tools such as browsers and terminals, coding, reasoning and knowledge work. It takes text and image input and produces text, with a 1M-token context window, up to 128K output tokens and adaptive thinking, and improves on Sonnet 4.6.
GPT-5.2 is OpenAI's GPT-5 series model for complex professional work. It takes text and image input and produces text output, with a 400K-token context window, up to 128K output tokens and reasoning effort from none to xhigh.
GPT-5.4 is OpenAI's GPT-5 series model for coding and complex professional work. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to xhigh.
GPT-5.4 Mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding, and tool use.
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, built for complex professional work such as reasoning, coding and agentic workflows. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to max.
GPT-6 Astra is the top tier of OpenAI's GPT-6 family, built for demanding work such as complex reasoning, coding, computer use, research and document creation. It takes text and image input and produces text, with a 1.05M-token context window (up to 922K input and 128K output tokens) and configurable reasoning effort.
GPT-6 Luna is OpenAI's most efficient GPT-6 model for focused, high-volume tasks. It is suited for chat, classification, and lightweight agentic workflows, with a 1.05M token context window (922K input, 128K output), text and image inputs, and capable reasoning for its price tier.
GPT-6 Sol is built for complex coding and agentic workflows. It is OpenAI's GPT-6 coding and agent model, with a 1.05M token context window (922K input, 128K output), support for text and image inputs, and reasoning effort from none through max.
GPT-6.1 Sol delivers near-Astra performance at a lower cost for complex coding, computer use, and professional work. It has a 1.05M token context window (922K input, 128K output), support for text and image inputs, and reasoning effort from low through max.
Claude Haiku 4.5 is Anthropic's small, fast model for real-time, low-latency work such as chat assistants, customer support agents and pair programming. It takes text and image input and produces text output, with a 200K-token context window, up to 64K output tokens and extended thinking.
Gemini 2.5 Flash is Google's thinking model built for price-performance, suited to large-scale processing, low-latency, high-volume tasks and agentic use cases. It takes text, image, video and audio input and produces text output, with a 1M-token context window and up to 65K output tokens.
Gemini 3.1 Flash-Lite is Google's low-latency, cost-effective multimodal model for high-frequency, lightweight tasks such as high-volume agentic workflows and simple data extraction. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window, up to 65K output tokens and thinking support.
Gemini 2.5 Flash-Lite is Google's cost-efficient multimodal model in the Gemini 2.5 family, built for high-frequency, lightweight tasks such as high-volume classification and simple data extraction. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window and thinking support.
Gemini 2.5 Pro is Google's thinking model for reasoning over complex problems in code, math and STEM. It can analyze large datasets, codebases and documents using long context, taking text, image, video, audio and PDF input and producing text output, with a 1M-token context window and up to 65K output tokens.
GPT-4.1 is OpenAI's non-reasoning model that excels at instruction following and tool calling, with broad knowledge across domains. It takes text and image input and produces text output, with a 1M-token context window, up to 32K output tokens and low latency without a reasoning step.
GPT-4.1 Mini is a smaller, faster version of OpenAI's GPT-4.1, built for instruction following and tool calling without a reasoning step. It takes text and image input and produces text output, with a 1M-token context window and up to 32K output tokens.
GPT-4.1 Nano is the fastest, most cost-efficient version of OpenAI's GPT-4.1, built for instruction following and tool calling without a reasoning step. It takes text and image input and produces text output, with a 1M-token context window and up to 32K output tokens.
GPT-5 is OpenAI's reasoning model for coding, reasoning and agentic tasks across domains, with configurable reasoning effort (minimal, low, medium and high). It takes text and image input and produces text output, with a 400K-token context window and up to 128K output tokens.
GPT-5 Mini is a faster, more cost-efficient version of OpenAI's GPT-5 reasoning model, suited to well-defined tasks, precise prompts and high-volume, low-latency workloads. It takes text and image input and produces text output, with a 400K-token context window and up to 128K output tokens.
GPT-5 Nano is the fastest, lowest-cost version of OpenAI's GPT-5 reasoning model, suited to summarization and classification tasks. It takes text and image input and produces text output, with a 400K-token context window and up to 128K output tokens.
GPT-5.1 is OpenAI's GPT-5 series model for coding and agentic tasks, with configurable reasoning effort that includes a non-reasoning setting. It takes text and image input and produces text output, with a 400K-token context window and up to 128K output tokens.
GPT-5.4 Nano is the lowest-cost GPT-5.4-class model from OpenAI, designed for simple, high-volume tasks where speed and cost matter most, such as classification and data extraction. It takes text and image input and produces text output, with a 400K-token context window and up to 128K output tokens.
GPT-5.6 Luna is OpenAI's GPT-5.6 model for cost-sensitive, high-volume workloads, roughly matching the nano tier of earlier GPT-5 families. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to max.
o3 is OpenAI's well-rounded reasoning model for complex tasks across domains. It takes text and image input and produces text output, with a 200K-token context window and up to 100K output tokens.
o3-mini is OpenAI's small reasoning model, offering strong reasoning at the cost and latency targets of o1-mini. It takes text input and produces text output, with a 200K-token context window and up to 100K output tokens.
o4-mini is OpenAI's fast, cost-efficient reasoning model, optimized for efficient performance in coding and visual tasks. It takes text and image input and produces text output, with a 200K-token context window and up to 100K output tokens.
Access 39 models served through NEAR AI on AnonRouter's privacy-first gateway, including DeepSeek V4.1 Flash, Grok 4.6, and Grok 4.7. Each NEAR AI route carries its own privacy label. Its TEE routes terminate TLS inside an attested confidential-compute enclave. Its relayed routes to OpenAI, Anthropic, Google and other labs are not TEE routes: NEAR AI forwards the prompt to that lab, so they are labelled Anonymous.
