DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp. It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time.

ChatDeepSeek1M tokens$0.1635 / $0.654 · 1M

deepseek/deepseek-v4.1-flash-1017

Contexto
1M tokens
Máx. completion
393,216
Tools
Sí
JSON
Sí
Lanzamiento
2026-10-17

Llámalo desde Geek Hub

El mismo SDK de OpenAI. Cambia el base URL y el id del modelo.

curl https://api.geekhub.mx/v1/chat/completions \
  -H "Authorization: Bearer $GEEKHUB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v4.1-flash-1017",
    "messages": [{"role": "user", "content": "Hola"}]
  }'
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Preguntas frecuentes

¿Qué es DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp. It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time. DeepSeek V4.1 Flash 1017 corre en el API de Geek Hub (compatible con OpenAI). Id: deepseek/deepseek-v4.1-flash-1017.
¿DeepSeek V4.1 Flash 1017 es gratis?
No. El input cuesta $0.1635 / 1M tokens y el output $0.654 / 1M tokens en Geek Hub (markup incluido).
¿Cuál es el contexto de DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 tiene una ventana de 1M tokens. Soporta hasta 393,216 tokens de completion.
¿DeepSeek V4.1 Flash 1017 soporta tool calling y structured outputs?
DeepSeek V4.1 Flash 1017 acepta tools y tool_choice para function calling. También soporta structured outputs con un JSON schema en response_format.
¿Cuándo se lanzó DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 se lanzó el 17 de octubre de 2026.

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