zerogpu/zerogpu-router/chat-deepseek-v4-1-flash

Claude Code and agent skill by zerogpu/zerogpu-router

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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 twice chat on input and output alike, over eight times its context.

Install

npx vskill@latest install zerogpu/zerogpu-router/chat-deepseek-v4-1-flash

SKILL.md

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 middle of the three 1M-context models on the platform — three times /zerogpu-router:chat-glm-5-3-flash on input and over three times on output, and roughly three quarters of /zerogpu-router:chat-gpt-4-1-mini on both. 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-glm-5-3-flash 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.

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