chat-deepseek-v4-1-flash
GitHub调用 deepseek-v4.1-flash 模型处理需高推理能力或函数调用的复杂任务，支持百万级上下文。适用于大型代码库、长文档及多步智能体工作流，提供纯文本回答并可选显示成本节省信息。
触发场景
安装
npx skills add zerogpu/zerogpu-router --skill chat-deepseek-v4-1-flash -g -y
SKILL.md
Frontmatter
{
"name": "chat-deepseek-v4-1-flash",
"description": "Chat with deepseek-v4.1-flash, a sparse MoE model on DeepSeek's Causal Encoder-Decoder architecture (8B active on input, 16B on output) with a 1M-token context window. Use for large codebases, long documents, extended conversations, and multi-step agent tasks that want higher-effort reasoning or function calling. Costs about twice chat-deepseek on input and three times on output.",
"allowed-tools": "Bash(zerogpu chat_completions *)",
"argument-hint": "<text>"
}
Call deepseek-v4.1-flash. $ARGUMENTS is the raw prompt — pass it verbatim, no escaping or quoting required (the heredoc below handles every shell metacharacter, newline, quote, and paren safely):
zerogpu chat_completions -m deepseek-v4.1-flash <<'ZGPU_END_OF_INPUT'
$ARGUMENTS
ZGPU_END_OF_INPUT
At $0.30 / $1.20 per 1M input/output tokens this is the pricier of the two 1M-context models on the platform — roughly twice /zerogpu-router:chat-deepseek on input and three times on output, and about a quarter of /zerogpu-router:chat-glm on input and a third on output. Prefer it when the task wants V4.1's higher-effort reasoning or function calling; for ordinary coding and agentic work at the same context size, chat-deepseek is cheaper.
Output is the assistant's answer as plain text — the model's reasoning trace comes back in a separate field and is not printed. Relay the answer as-is — do not rewrite or expand it.
Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest /zerogpu-router:cost-savings — this note is intentionally occasional, not shown every time.
版本历史
- 4e1b070 当前 2026-09-22 08:06
