Setup guide - Documented by Continue
Use Kimi in Continue
Continue documents a built-in moonshot provider in config.yaml with apiKey, apiBase and model options. Its default apiBase is the China endpoint and its listed models are discontinued, so set apiBase to Kimi's international base URL and model to kimi-k3.
Last verified 2 steps4 official sources
Steps
- Step 1.
Get an API key from the Kimi API Platform (https://platform.kimi.ai/console/api-keys).
- Step 2.
Add a moonshot model to your Continue config.yaml. Continue documents apiBase as a configurable option; override it to Kimi's international endpoint and use a current model id.
YAMLname: My Config version: 0.0.1 schema: v1 models: - name: Kimi K3 provider: moonshot model: kimi-k3 apiKey: <YOUR_MOONSHOT_API_KEY> apiBase: https://api.moonshot.ai/v1/
Caveats
- This combines Continue's documented moonshot provider and apiBase option with Moonshot's documented base URL (https://api.moonshot.ai/v1) and current model id. Continue's own example uses model: kimi-k2.
- Continue's default apiBase is https://api.moonshot.cn/v1/. Moonshot says platform.kimi.ai keys only work with https://api.moonshot.ai/v1, so the override is needed for international keys.
- Every model Continue's page lists (kimi-k2, kimi-k2.5, moonshot-v1-8k/32k/128k) has been discontinued by Moonshot.
More setup guides
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Related reading
- Kimi K3 Is GA in GitHub Copilot: Pricing, Rollout, and What It Means for Model ChoiceKimi K3 is GA in GitHub Copilot at $3/$15 per million tokens, off by default for Business and Enterprise. Rollout details and what it means for model choice.
- Cloudflare Runs Kimi and GLM at Scale: FP8 KV Caches, INT4 Weights, and a Cache Safety NetCloudflare runs Kimi K2.6 and GLM 5.2 on Workers AI with FP8 KV caches, INT4 weights and a KV cache integrity check. What each technique buys on GPU fleets.
- AMD MI355X vs NVIDIA B200 vs B300 for Open-Weight ServingAMD MI355X vs NVIDIA B200 vs B300 for serving Kimi K3's about 1.5TB of open weights: verified specs, the Wafer benchmark, and cost per token for each.
- Kimi Linear: An Attention Architecture That Outperforms Full AttentionKimi Linear explained: Moonshot's KDA hybrid linear attention beat full attention with 75% less KV cache and up to 6x faster decoding at 1M context.
- Kimi K3 Weights Land on HuggingFace: 2.8T Open Frontier Model You Can Actually DownloadKimi K3 open weights are on HuggingFace: 2.8T parameters, 1M context, native MXFP4 and a ~1.63TB download. The specs, license terms and HN reaction.
- Kimi K3 in 10 Minutes: Moonshot AI's 2.8T Open Model, API Setup, Pricing, and BenchmarksKimi K3 from Moonshot AI is a 2.8T open model with a 1M-token context and an OpenAI-compatible API. How to call it, what it costs, and how it benchmarks.
FAQ
How do I use Kimi in Continue?
Continue documents a built-in moonshot provider in config.yaml with apiKey, apiBase and model options. Its default apiBase is the China endpoint and its listed models are discontinued, so set apiBase to Kimi's international base URL and model to kimi-k3.
Which Kimi model ids work in Continue?
The official docs read for this guide list kimi-k3, kimi-k2.7-code and kimi-k2.6. Requests go to https://api.moonshot.ai/v1/.
Is this an official Kimi and Continue setup?
Continue documents this setup in its own docs. It was last checked against 4 official pages on October 17, 2026.
Sources
Last verified against 4 official pages.
- docs.continue.devhttps://docs.continue.dev/customize/model-providers/more/moonshot
- platform.kimi.aihttps://platform.kimi.ai/docs/api/overview.md
- platform.kimi.aihttps://platform.kimi.ai/docs/models.md
- platform.kimi.aihttps://platform.kimi.ai/docs/guide/codex-kimi.md
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