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Kimi K3.1 Pro vs Kimi K3 : tests de performance, fonctionnalités, tarifs et lequel est le meilleur ?
Comparez Kimi K3.1 Pro et Kimi K3 sur les benchmarks, la programmation, le raisonnement, le contexte, la tarification de l’API et la vitesse. Découvrez quel modèle est le meilleur et quand effectuer une mise à niveau.
Qu'est-ce que Kimi K3.1 : date de sortie, fuites et fonctionnalités attendues
Explorez les rapports sur la date de sortie de Kimi K3.1, les niveaux de raisonnement divulgués, le contexte 1M et des indices sur Agent Swarm, ainsi que des comparaisons avec K3 et la préparation de l'API.
Comment déployer Kimi K3 en local ?
Comment déployer Kimi K3 localement avec vLLM, SGLang, llama.cpp et des quantifications GGUF, y compris les exigences matérielles, les méthodes de déploiement et les alternatives hébergées.
Grok 4.6 vs Kimi K3 : comparaison complète
Choisissez Grok 4.6 pour une API d'agent hébergée économique; choisissez Kimi K3 pour un contexte de 1M, des poids ouverts et le contrôle de l'infrastructure.
Kimi K3 Auto-hébergement vs API en 2026 : matériel et coûts
L’auto-hébergement de Kimi K3 nécessite 8+ GPU GB300 ou MI350X/MI355X et environ 1,56 TB de poids. Comparez les tarifs de l’API, les conditions de licence et les coûts au seuil de rentabilité.
![Could you clarify which “Kimi K3” you mean and who the provider is? For example:
- Moonshot AI’s Kimi models (OpenAI‑compatible API)?
- A different “Kimi K3” product (e.g., device/service) with its own API?
If it’s an OpenAI‑compatible LLM API (common for Kimi by Moonshot), here’s a provider‑agnostic checklist you can follow while you confirm exact endpoints and model names:
1) Obtain credentials
- Sign up on the provider’s console.
- Create an API key.
- Note the base URL and the exact model name (e.g., something like kimi‑k3 or the provider’s official model ID).
2) Make a chat completion request
- Method: POST
- URL: <API_BASE_URL>/v1/chat/completions (confirm exact path in docs)
- Headers:
- Authorization: Bearer <YOUR_API_KEY>
- Content-Type: application/json
- JSON body (example):
{
"model": "<MODEL_NAME>",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, Kimi K3!"}
],
"stream": false
}
- Response: JSON with choices[0].message.content containing the assistant reply.
3) Streaming (if supported)
- Same endpoint and headers.
- Set "stream": true in the JSON body.
- Read Server‑Sent Events (SSE) chunks until [DONE].
4) Error handling
- 401/403: invalid or missing API key.
- 429: rate limited; implement retries with backoff.
- 5xx: transient server errors; retry safely.
5) Safety and usage tips
- Keep your API key secret (use server‑side calls or a secure proxy).
- Set request‑level parameters (temperature, top_p, max_tokens) per your use case.
- Log request IDs from response headers (if provided) for support.
6) SDKs
- If the provider offers official SDKs (JavaScript/Python/Go, etc.), install the package, set the API key via environment variable, and call a ChatCompletion method with the same model and messages structure.
If you can share:
- The provider/documentation link
- The exact model ID and base URL
- Your preferred language (Python/JS/etc.)
…I can give you a drop‑in example tailored to the actual Kimi K3 API.](https://resource.cometapi.com/How to Use Kimi K3 API.webp)
Could you clarify which “Kimi K3” you mean and who the provider is? For example: - Moonshot AI’s Kimi models (OpenAI‑compatible API)? - A different “Kimi K3” product (e.g., device/service) with its own API? If it’s an OpenAI‑compatible LLM API (common for Kimi by Moonshot), here’s a provider‑agnostic checklist you can follow while you confirm exact endpoints and model names: 1) Obtain credentials - Sign up on the provider’s console. - Create an API key. - Note the base URL and the exact model name (e.g., something like kimi‑k3 or the provider’s official model ID). 2) Make a chat completion request - Method: POST - URL: <API_BASE_URL>/v1/chat/completions (confirm exact path in docs) - Headers: - Authorization: Bearer <YOUR_API_KEY> - Content-Type: application/json - JSON body (example): { "model": "<MODEL_NAME>", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello, Kimi K3!"} ], "stream": false } - Response: JSON with choices[0].message.content containing the assistant reply. 3) Streaming (if supported) - Same endpoint and headers. - Set "stream": true in the JSON body. - Read Server‑Sent Events (SSE) chunks until [DONE]. 4) Error handling - 401/403: invalid or missing API key. - 429: rate limited; implement retries with backoff. - 5xx: transient server errors; retry safely. 5) Safety and usage tips - Keep your API key secret (use server‑side calls or a secure proxy). - Set request‑level parameters (temperature, top_p, max_tokens) per your use case. - Log request IDs from response headers (if provided) for support. 6) SDKs - If the provider offers official SDKs (JavaScript/Python/Go, etc.), install the package, set the API key via environment variable, and call a ChatCompletion method with the same model and messages structure. If you can share: - The provider/documentation link - The exact model ID and base URL - Your preferred language (Python/JS/etc.) …I can give you a drop‑in example tailored to the actual Kimi K3 API.
Apprenez à utiliser la Kimi K3 API via CometAPI. Inclut la configuration, la tarification, le streaming, l’effort de raisonnement, l’entrée visuelle, etc.