DeepSeek V4 Flash GA (deepseek-v4-flash-ga-260731) Chat Completions API reference and playground: OpenAI-compatible, 1M context, thinking toggle and implicit caching.
POST
/
v1
/
chat
/
completions
Chat completion: DeepSeek V4 Flash text generation
Use the playground on the right to test directly: put Bearer sk-your-api-key in
Authorization. The default example already disables deep thinking
(thinking.disabled), so you get a fast response on send.
This model thinks a lot by default — even a one-line question emits hundreds of reasoning
tokens first. Keep "thinking": {"type": "disabled"} from the example while debugging.
For capabilities, pricing and caching, see the
DeepSeek V4 Flash overview.
When thinking is on, give max_tokens room (reasoning counts toward the output quota) — 3000+ recommended
response_format has no effect: passing json_schema returns 200 while ignoring the schema entirely. Use tools for structured output
n is silently ignored: passing n=2 returns 200 with exactly one element in choices
Text-only model — passing image content blocks returns Model do not support image input
No parameters needed — an identical long prefix hits on the second request:
Round
prompt_tokens
cached_tokens
Hit rate
1
15,634
0
—
2
15,634
15,616
99.9%
3
15,634
15,616
99.9%
Cached tokens bill at $0.028 per million. Keep timestamps, random IDs and other variable
content at the end of the prompt — mixing them into the prefix drops the hit rate to zero.
Reasoning depth tier. Only minimal is deterministic (reasoning tokens always 0); low/medium/high/max do not form a monotonic ladder, and within-tier variance exceeds between-tier differences
Function Call tool list in standard OpenAI format. Tool arguments are genuinely constrained — use this instead of response_format when you need structured output
Usage. completion_tokens_details.reasoning_tokens is reasoning spend; prompt_tokens_details.cached_tokens is the implicit cache hit
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