待翻译：Celeris-1 Magnus: Fast hybrid diffusion model for agentic work

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Celeris-1 Magnus: the fastest model for agentic use cases CELERIS-1 MAGNUS The fastest model for agentic work. Magnus is built for agents that need to think, use tools, and get things done, without waiting around. Get an API key See the benchmarks AGENT · LIVEREAL TASK, REAL SPEED PERFORMANCE Highest solve rate in the field. All 97 tasks of τ³-bench banking, head-to-head against gpt-5.6-sol, gpt-5.6-luna and gemini-3.7-flash, reasoning effort as published. 01 · τ³-BENCH BANKING Figure 1. Solve rate vs median time per task, 97 tasks, official grader. Up and to the left is better. Celeris-1 without agent training: 5.3%. SOLVE RATE41.2% VS GPT-5.6-SOL+3.1 pts TIME PER TASK · P5055 s NO AGENT TRAINING5.3% Methodology → Leads the hardest agent benchmark 41.2% on τ³-bench banking, ahead of gpt-5.6-sol, with the field's best solve rate. A reasoning dial, per request One flag between fast and thorough. 13.4 extra points when thinking is on, for 6 seconds. Fast where it counts 55 seconds median per completed task, quicker than every model within ten points of it. THE FAMILY Celeris-1 or Magnus? Celeris-1 is built for answers; Magnus is built for tasks. The API is the same, so switching is a one-word change. Celeris-1 Ultra-fast generation for chat, search and real-time interfaces. ModalityText Context window131,072 tokens Max output2,048 default Best atInstant answers Learn more Celeris-1 Magnus Agentic work: tool loops, long tasks, structured actions. ModalityText + tools Context window131,072 tokens Max output16,384 tokens Best atAgents that finish Get an API key GET STARTED Start building. The API is OpenAI-compatible. Point your SDK at inference.celeris.ai and keep the agent code you already wrote. 01 Create an account Sign up at console.celeris.ai. No waitlist, no sales call. 02 Get an API key Keys are live immediately and billed per token. 03 Point your SDK Set the base URL and model id. Your existing agent code runs as is. agent.pyPYTHON from openai import OpenAI client = OpenAI( base_url="https://inference.celeris.ai/celeris-1-magnus/v1", api_key="ck_…", ) resp = client.chat.completions.create( model="celeris-1-magnus", messages=[{"role": "user", "content": "Move my Friday payment to Monday"}], tools=my_tools, # the reasoning dial: on when the task earns it extra_body={"chat_template_kwargs": {"enable_thinking": True}}, ) Start building with Celeris. Experience the new paradigm of inference. Get Started See the benchmarks