DeepSeek API Pricing 2026: V4 Flash and Pro Peak vs Off-Peak Rates
Current DeepSeek API prices after the August 17 increase: V4 Flash and V4 Pro peak/off-peak token rates, cache costs, schedules, model IDs, and cost examples.
DeepSeek’s current V4 API uses time-of-day pricing. The new rates took effect at 2026-08-16 16:00 UTC, or midnight on August 17 in Beijing. Peak hours are 01:00-04:00 and 06:00-10:00 UTC; every other hour receives a 50% off-peak discount.
The current model IDs are deepseek-v4-flash and deepseek-v4-pro. Both have a 1M-token context window, 384K maximum output, thinking and non-thinking modes, JSON output, tool calls, Responses API, and OpenAI- and Anthropic-compatible endpoints.
Current DeepSeek Prices
USD per million tokens:
| Model and billing period | Cached input | Cache-miss input | Output |
|---|---|---|---|
deepseek-v4-flash, off-peak | $0.007 | $0.22 | $0.66 |
deepseek-v4-flash, peak | $0.014 | $0.44 | $1.32 |
deepseek-v4-pro, off-peak | $0.022 | $0.66 | $1.98 |
deepseek-v4-pro, peak | $0.044 | $1.32 | $3.96 |
DevTk.AI’s calculator uses the conservative peak rate. For workloads that can be scheduled, calculate a second forecast with the off-peak rate.
Peak Schedule
| Billing period | UTC | Beijing time |
|---|---|---|
| Peak window 1 | 01:00-04:00 | 09:00-12:00 |
| Peak window 2 | 06:00-10:00 | 14:00-18:00 |
| Off-peak | All other hours | All other hours |
The 2x statement compares the current peak and off-peak prices. It does not mean every old price simply doubled. For example, V4 Flash’s former $0.28/M output rate became $0.66/M off-peak and $1.32/M peak.
Current Versions and IDs
| API ID | Served version | Context | Max output |
|---|---|---|---|
deepseek-v4-flash | DeepSeek-V4-Flash-0731 | 1M | 384K |
deepseek-v4-pro | DeepSeek-V4-Pro-0813 | 1M | 384K |
The old deepseek-chat and deepseek-reasoner aliases were retired on July 24, 2026. Use the explicit V4 IDs in current integrations.
Cost Formula and Cache Fields
cost =
prompt_cache_hit_tokens * cached_input_price / 1,000,000
+ prompt_cache_miss_tokens * cache_miss_input_price / 1,000,000
+ completion_tokens * output_price / 1,000,000
DeepSeek exposes the relevant usage counters:
usage.prompt_cache_hit_tokens
usage.prompt_cache_miss_tokens
Caching is best effort. Stable system prompts, repository rules, tool schemas, and conversation prefixes have the best chance of reuse. Monitor the actual cache-hit ratio rather than budgeting for a perfect cache.
Cost Examples
For 2M cache-miss input plus 500K output tokens across multiple requests:
| Model and period | Cost |
|---|---|
| V4 Flash, off-peak | $0.77 |
| V4 Flash, peak | $1.54 |
| V4 Pro, off-peak | $2.31 |
| V4 Pro, peak | $4.62 |
With 85% of input served from cache, the same workload becomes approximately $0.408 off-peak or $0.816 peak on V4 Flash. The result still depends on actual cache behavior and output-token use.
OpenAI-Compatible Example
from openai import OpenAI
client = OpenAI(
api_key="your-deepseek-api-key",
base_url="https://api.deepseek.com"
)
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{"role": "system", "content": "You are a careful coding assistant."},
{"role": "user", "content": "Explain this repository structure."}
],
)
print(response.choices[0].message.content)
For Anthropic-format coding agents:
export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic
export ANTHROPIC_AUTH_TOKEN=your-deepseek-api-key
Is DeepSeek Still Cheap?
V4 Flash remains a useful low-cost model, especially off-peak and with repeated long context. It is no longer the universal price floor: Mistral Small 4 can be cheaper for short ordinary traffic, and GPT-5.6 Luna can be cheaper than V4 Flash during peak hours for some token mixes.
V4 Pro should be an escalation route. Its value depends on reducing failed tool loops, retries, or human repair enough to offset the higher price.
Read the full post-increase value analysis and use the AI Pricing Calculator for your workload.
Official sources checked August 17, 2026: DeepSeek pricing, V4 Pro 0813 and pricing announcement, context caching, and Anthropic API compatibility.