Use this model when
- The workload fits the text or chat tasks shown in this model's current catalog record.
- The input fits within the published 131.1K-token context record, with output and system overhead budgeted separately.
kimi-vl-a3b-thinking:freeKimi VL A3B Thinking (Free) by Moonshot AI.
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="kimi-vl-a3b-thinking:free", 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="kimi-vl-a3b-thinking:free",# 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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Kimi K2 Instruct is a trillion-parameter MoE model from Moonshot AI, with 32B active parameters per step. Built for strong agentic behavior, it excels at tool use, reasoning, and code generation, leading major benchmarks in coding, logic, and tool-use tasks. It supports up to 128K context and uses a specialized training setup (including MuonClip) to stabilize very large MoE training.
Kimi K2 Thinking is Moonshot AI's most advanced open reasoning model, built on a trillion-parameter MoE with 32B active parameters and a 256K context window. It's optimized for long-horizon, step-by-step reasoning with dynamic tool use, enabling sustained autonomous research, coding, and writing over hundreds of turns. It sets new open-source records on benchmarks like HLE and LiveCodeBench, and maintains stable multi-agent, tool-heavy workflows (200–300 calls) while balancing deep reasoning with efficient inference.
Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, designed for long-horizon software engineering and agentic development workflows. Built on a native multimodal Mixture-of-Experts (MoE) architecture, it supports text, image, and video inputs and operates exclusively in thinking mode, preserving reasoning across multi-turn interactions. With approximately 1T total parameters and 32B activated per token, plus a 256K-token context window, K2.7 Code excels at end-to-end programming tasks, agentic task decomposition, repository-scale reasoning, and extended coding conversations, making it well suited for advanced coding agents and long-context development workflows.
Kimi K2-Instruct-0905 is the newest and strongest Kimi K2 model — a trillion-parameter MoE system with 32B active parameters. It delivers major gains in agentic coding, better frontend development results, and an expanded 256K context window for longer tasks.
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