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Long-context language models via TokenRa

Kimi K3 API Guide

Use one TokenRa API key to evaluate enabled Kimi models from Moonshot AI for research, document analysis, knowledge workflows, and conversational applications. Confirm the active model version, context behavior, and pricing in the console.

Create an API keyRead the integration guide

Kimi K3: 2.8T MoE for long-context Agents

Kimi K3 is an open-weight multimodal reasoning model from Moonshot AI, released on July 16, 2026. Its sparse Mixture-of-Experts architecture has 2.8T total parameters and approximately 104B active parameters per token, with a 1,048,576-token context window.

01

2.8T sparse MoE

896 experts route 16 experts per token alongside 2 shared experts. Total capacity is large, while sparse routing limits per-token activation.

02

1,048,576-token context

Handle large repositories, long documents, multi-turn history, and tool definitions in a single context window.

03

Native multimodal Agents

Read text, images, and video in one model, then use visual feedback, tools, and reasoning to continue an Agent workflow.

Where Kimi K3 API fits

Kimi models are candidates for workloads where context handling matters. Test with the same document sizes, languages, and retrieval noise that users will produce.

  • Research assistants that compare multiple supplied sources and return traceable conclusions.
  • Document review pipelines that extract clauses, entities, risks, or structured records.
  • Knowledge-base question answering with retrieved passages and explicit grounding rules.
  • Long-running agent or support conversations that need carefully managed history.

Kimi K3 architecture and Agent capabilities

Kimi K3 combines Kimi Delta Attention, Attention Residuals, Stable LatentMoE, and quantization-aware training with MXFP4 weights and MXFP8 activations. These are Moonshot AI technical claims; they describe architecture and scaling efficiency, not a guaranteed speedup for every workload.

  • Native multimodal: MoonViT-V2 vision encoding supports text, image, and video input for vision-in-the-loop work.
  • Reasoning: Thinking is always enabled; set reasoning_effort to low, high, or max.
  • Tools: Function calling, tool_choice, and JSON Schema structured output support Agent integrations.
  • Open weights: Kimi K3 uses the Kimi K3 License, not an unrestricted Apache or MIT license.

Integrate Kimi K3 with a controlled context strategy

Use the enabled model ID from the console and send an OpenAI-compatible chat request where supported. Keep document assembly, retrieval, and citation verification in your application.

1

Prepare and label context

Separate instructions from source text, retain source identifiers, and remove irrelevant material before sending the request.

2

Measure grounded answers

Check whether each claim is supported by the supplied context and define how the application handles missing evidence.

3

Control context growth

Trim stale turns, summarize history carefully, cap output, and monitor usage as documents and conversations expand. 

curl https://tokenra.io/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kimi-k3","messages":[{"role":"user","content":"Summarize the supplied research context with source labels."}]}'

Kimi K3 availability, model ID, and pricing

Kimi K3 official reference pricing is $0.30 per 1M cached input tokens, $3.00 per 1M uncached input tokens, and $15.00 per 1M output tokens. TokenRa is a gateway with account-specific channel configuration. A model family appearing on this information page does not guarantee that every version or capability is enabled for every account.

Check the live console before integration

Confirm the exact model identifier, endpoint, supported parameters, billing unit, and current rate in the model directory or console. Treat those live values as authoritative.

Production checklist

  • Keep API keys on the server and separate credentials by environment or workload.
  • Set explicit timeouts, bounded retries, and request tracing before routing user traffic.
  • Evaluate model behavior with representative inputs instead of assuming versions are interchangeable.
  • Monitor usage and cost in the console, then add application-side budgets and limits.
  • Review provider terms, privacy requirements, and output rights for your use case.

Kimi K3 API FAQ

Is Kimi API available through TokenRa?

Availability depends on the channels enabled for your account. Check the live directory and console for the current model identifier and access.

How do I integrate Kimi API?

Create a TokenRa API key, confirm the enabled model identifier, and use the request format documented for that capability. Applicable text models use an OpenAI-compatible endpoint; media models may use asynchronous tasks.

How much does Kimi API cost?

Pricing can vary by model version and active channel. Review the current billing unit and rate in the live model directory or console before testing.

Can I switch to another model later?

Yes. TokenRa keeps credentials and account-level usage in one place. Request fields and model behavior can still differ, so run regression evaluations before changing production traffic.

“Kimi”, “Moonshot AI”, and related names may be trademarks of their respective rights holders. TokenRa is an independent API gateway and this page is not an official vendor page or endorsement.