grok-4.20-multi-agentGrok 4.20 Multi-Agent is a specialized variant of xAI's Grok 4.20 designed for collaborative, agent-based workflows. It enables multiple agents to operate in parallel, coordinating tool use and synthesizing information to handle complex, multi-step tasks. Optimized for deep research and large-scale problem solving, the model supports configurable reasoning effort: 4 agents for low/medium settings and up to 16 agents for high/xhigh settings, enabling scalable parallel reasoning and execution.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="grok-4.20-multi-agent", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="grok-4.20-multi-agent",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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Grok Build 0.1 is xAI's fast coding model designed specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized for interactive coding agents, tool use, and multi-step development tasks. Powering the Grok Build CLI, the model features a 256K token context window with effectively no text output limit, making it well suited for long-horizon coding, automation, and continuous development workflows. Currently available in early access.
Grok 4.3 is a reasoning-focused model from xAI designed for agentic workflows, instruction following, and high factual accuracy tasks. It supports text and image inputs with text output, with reasoning always active and not configurable by effort level. The model features a 1M-token context window with effectively no output token limit, making it well suited for long-document analysis, deep research, and multi-step agentic workflows. It uses tiered pricing, with higher rates applied to requests exceeding 200K total tokens.
Grok 4.20 is xAI's newest flagship model, designed for high-performance reasoning with industry-leading speed and strong agentic tool-calling capabilities. It emphasizes strict prompt adherence and low hallucination rates, delivering highly precise and reliable responses. Optimized for agent workflows and real-time applications, Grok 4.20 provides consistent, truthful outputs while maintaining fast inference and robust task execution.
Grok 4.20 Multi-Agent Beta is a specialized variant of xAI's Grok 4.20 designed for collaborative, agent-based workflows. It enables multiple agents to operate in parallel, coordinating tool use and information synthesis to handle complex tasks. Optimized for deep research and multi-step problem solving, the model supports parallel reasoning, coordinated execution, and structured knowledge synthesis across large and complex workflows.
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Grok 4.1 Fast is xAI's top tool-calling model, designed for practical agent workflows like customer support and deep research. It features a massive 2M-token context window, and developers can toggle reasoning on or off via the API.
Grok 4.20 Beta is xAI's newest flagship model, designed for high-performance reasoning with industry-leading speed and strong agentic tool-calling capabilities. It emphasizes strict prompt adherence and low hallucination rates, enabling highly reliable and precise responses across complex tasks. Optimized for agent workflows and real-time applications, Grok 4.20 Beta delivers consistent, truthful outputs while maintaining fast inference and strong task execution reliability.
Grok 4 Fast is xAI's cost-efficient multimodal model with a massive 2M-token context window. It’s available in both reasoning and non-reasoning modes, allowing developers to toggle deeper thinking when needed. Designed for scalable performance, it balances speed, capability, and price — with reasoning controllable via the reasoning_enabled API parameter.
Initialized observational baseline with no recorded failures