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Kimi K2.6 Now Available for Telnyx Inference API

22, Apr 2026

Kimi K2.6 is now available on the Telnyx Inference API through the OpenAI-compatible Chat Completions endpoint. Built by Moonshot AI, K2.6 advances on K2.5 with stronger long-horizon task adherence, tighter constraint following, and improved third-party API understanding.

What's new in Telnyx

  • Kimi K2.6 model: Moonshot AI's latest open-weight model, accessible via the Inference API with model ID moonshotai/Kimi-K2.6.
  • Stronger agentic performance: K2.6 completes multi-step tasks in fewer iterations with higher constraint adherence than K2.5, reducing retry loops in production agents.
  • Drop-in integration: Works with the existing OpenAI-compatible Chat Completions API. No new endpoints, no SDK updates required.

Why it matters

  • Running inference on Telnyx keeps your LLM calls on the same private backbone as your STT and TTS pipelines, cutting the latency hops that come with routing to a separate provider.
  • K2.6's stronger task adherence means agentic workflows that rely on multi-step reasoning and external API calls complete more reliably with fewer retries.
  • Evaluating open-weight models without changing your integration means zero migration cost when switching between models on the platform.

Example use cases

  • Voice AI teams that need LLM inference and telephony on the same network to reduce round-trip latency for real-time agent calls.
  • Developers building multi-step agentic pipelines where task completion consistency matters at production scale.
  • Engineering teams evaluating open-weight models for production without committing to a proprietary inference provider.

Getting started

  1. Open the LLM Playground in Mission Control.
  2. Select moonshotai/Kimi-K2.6 from the model dropdown.
  3. Send a prompt to test the model, or integrate it into your application using the Chat Completions API.
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_TELNYX_API_KEY",
    base_url="https://api.telnyx.com/v2/ai/inference"
)

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.6",
    messages=[{"role": "user", "content": "Explain the advantage of on-network inference for voice AI agents."}]
)

print(response.choices[0].message.content)

Learn more in the Inference API docs or the pricing page.