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New accounts get trial credit — enough to run a full eval suite

On our own GPUs

Kimi K3 API

2.8T mixture-of-experts, a 1M-token window, and vision built into the base.

  • Context

    1M

  • Max output

    1M

  • Serving precision

    BF16

  • Licence

    Open weights

Kimi K3 is Moonshot AI’s flagship, released in July 2026. A new mixture-of-experts architecture puts roughly 2.8 trillion parameters behind 16 active experts out of 896, with a 1-million-token context window. Kimi Delta Attention and attention residuals keep memory and throughput manageable on very long inputs. Image and video understanding sit in the base model rather than a bolted-on vision layer, which is why it holds up on agent work that reads screenshots and screen recordings.

Open weights, deployed in facilities we build out and lease directly, operated by us. We resell nobody else’s account quota.

Specifications

Model ID
kimi-k3
Published by
Moonshot AI
How it is served
On our own GPUs
Licence
Open weights
Total parameters
2.8T MoE
Active parameters
16 / 896 experts
Context
1M
Max output
1M
Serving precision
BF16
Input modalities
Text · Image · Video
Released
2026-07-16

What it is good at

A million tokens that actually hold
A whole repository, a few hundred pages of contract, months of conversation history — all in one request, with no chunking and re-stitching. Kimi Delta Attention compresses the KV cache so throughput does not fall off a cliff on long inputs.
Native multimodality, not a bolt-on
Image and video understanding are base-model capabilities. Letting it look at an error screenshot or a recorded UI flow before deciding the next step beats OCR-then-text, and that is exactly what agent workloads need.
Long-horizon agent work
K3 is built for long-running coding and agent workloads. Multi-step planning, tool calling and retry-after-failure hold together noticeably better than the previous generation.

Where it fits

  • Repository-scale code comprehension, refactors and migrations
  • Extraction and comparison across long contracts, filings and paper sets
  • GUI agents and automated testing that read screenshots or recordings
  • Assistants that must keep dozens of turns of history intact

Three steps

Change one line of base_url, put the model ID below in the `model` field, and leave the rest of your code alone.

model: "kimi-k3"

curl https://api.nexaix.net/v1/chat/completions \
  -H "Authorization: Bearer $NEXAIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Summarise this contract." }
    ]
  }'

About this model

Can I really use the full 1M-token context?

Yes. We do not truncate context and we do not quietly shrink the window. Do note that cost and time-to-first-token rise linearly with input length, so benchmark with realistically long samples before you settle on a production configuration.

Is your Kimi K3 the same model as the one in the official app?

We deploy the officially released weights and serve at the precision published on this page — no quantization downgrade. The official app may add product-layer behaviour (browsing, plugins, system prompts) that is not part of the model itself and will not appear on the API.

How do I use the multimodal input?

Pass image_url or base64 content using the standard OpenAI multimodal message format — the same fields your existing code already uses. Video goes in as a sampled frame sequence; limits are in the docs.

Also worth a look

Run your own eval on it

Take the trial credit, run the evals you already have against this model, and then decide whether it gets your production traffic.