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CostPerPrompt

DeepSeek V4 Pro 0813 (batch) API Pricing

DeepSeek · context window 1M · prices updated 2026-09-15

Input per 1M tokens
$0.66
Output per 1M tokens
$1.98
Cached input per 1M
$0.022
97% cheaper than fresh input

Where DeepSeek V4 Pro 0813 (batch) sits on price

At $1.98 per million output tokens, DeepSeek V4 Pro 0813 (batch) is a mid tier model — right at the median output price of $2.08, which puts it cheaper than 54% and pricier than 46% of the models we track. It sits 14th cheapest of the 18 DeepSeek models we track. Output is only 3.0× the input price, an unusually flat spread: long prompts hurt about as much as long answers, so prompt size is where the savings are.

Output tokens per $1
505,051
One full context fill
$0.6921
Cheaper than
54% of tracked models

Cached input is 97% cheaper. On an input-heavy workload you need roughly a 26% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on DeepSeek V4 Pro 0813 (batch)

These three workloads are the ones teams actually run on a mid-priced model — a $1.98/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
Support chatbot — 500 conversations/day (5K in / 1.4K out) Conversation history re-sent each turn — the classic prompt-caching win. $0.0061 $91.08
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.003 $891
Long-context analysis — 200 runs/day (250K in / 4K out) Only possible on large-context models; input cost is nearly the whole bill. $0.1729 $1038

Model your exact traffic in the API cost calculator — it preloads DeepSeek V4 Pro 0813 (batch) with caching and batch options.

DeepSeek V4 Pro 0813 (batch) price history

DeepSeek last cut DeepSeek V4 Pro 0813 (batch)'s output price on Sep 9, 2026 (-50% per output token), from $1.32/$3.96 to $0.66/$1.98 per 1M in/out. That is the only repricing in the 18 days we have tracked it — worth knowing before you hard-code today's rate into a budget.

Date Input /1M Output /1M Output change
Aug 28, 2026 (tracking began) $1.32 $3.96
Sep 9, 2026 $0.66 $1.98 -50%

Change-points from our daily price snapshots (tracking since Aug 28, 2026; intraday moves between snapshots are not captured).

DeepSeek V4 Pro 0813 (batch) vs Qwen3.6 27B

The closest-priced alternative from another vendor is Qwen3.6 27B (Alibaba (Qwen)) — priced within a rounding error on output, with $0.36 less per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Qwen3.6 27B pricing →

Cheaper alternatives

More DeepSeek models

Frequently asked questions

How much does the DeepSeek V4 Pro 0813 (batch) API cost?

DeepSeek V4 Pro 0813 (batch) costs $0.66 per million input tokens and $1.98 per million output tokens, with cached input at $0.022 per million (97% cheaper). That works out to roughly 505,051 output tokens per dollar.

What does the support chatbot workload cost on DeepSeek V4 Pro 0813 (batch)?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.0061 per request and $91.08 per month on DeepSeek V4 Pro 0813 (batch). Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill DeepSeek V4 Pro 0813 (batch)'s 1M context window?

Sending 1M of input in a single request costs $0.6921 at $0.66 per million tokens — before any output. With prompt caching that same fill drops to about $0.0231 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is DeepSeek V4 Pro 0813 (batch) worth the price?

DeepSeek V4 Pro 0813 (batch) sits in the middle of the market (14 of 18 by price within DeepSeek). The honest test is a routing experiment: send the same 200 real requests to this model and to a tier below, and compare failure rate against the price gap — most teams find a majority of traffic never needed the pricier model.