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CostPerPrompt

DeepSeek V4 Flash Vision Exp API Pricing

DeepSeek · context window 1M · prices updated 2026-08-23

Input per 1M tokens
$0.22
Output per 1M tokens
$0.66
Cached input per 1M
$0.007
97% cheaper than fresh input

Where DeepSeek V4 Flash Vision Exp sits on price

At $0.66 per million output tokens, DeepSeek V4 Flash Vision Exp is a mid tier model — 3.4× cheaper than the median output price of $2.25, which puts it cheaper than 77% and pricier than 23% of the models we track. It sits 5th cheapest of the 14 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
1,515,152
One full context fill
$0.2307
Cheaper than
77% 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 Flash Vision Exp

These three workloads are the ones teams actually run on a mid-priced model — a $0.66/1M 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. $0.002 $30.36
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.001 $297
Long-context analysis — 200 runs/day (250K in / 4K out) Only possible on large-context models; input cost is nearly the whole bill. $0.0576 $346

Model your exact traffic in the API cost calculator — it preloads DeepSeek V4 Flash Vision Exp with caching and batch options.

DeepSeek V4 Flash Vision Exp price history

DeepSeek last cut DeepSeek V4 Flash Vision Exp's output price on Aug 23, 2026 (-50% per output token), from $0.44/$1.32 to $0.22/$0.66 per 1M in/out. That is one of 2 repricings in the 2 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 21, 2026 (tracking began) $0.22 $0.66
Aug 22, 2026 $0.44 $1.32 +100%
Aug 23, 2026 $0.22 $0.66 -50%

Change-points from our daily price snapshots (tracking since Aug 21, 2026; intraday moves between snapshots are not captured).

DeepSeek V4 Flash Vision Exp vs Gemma 2 27B

The closest-priced alternative from another vendor is Gemma 2 27B (Google) — priced within a rounding error on output, with $0.43 more 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 Gemma 2 27B pricing →

Cheaper alternatives

More DeepSeek models

Frequently asked questions

How much does the DeepSeek V4 Flash Vision Exp API cost?

DeepSeek V4 Flash Vision Exp costs $0.22 per million input tokens and $0.66 per million output tokens, with cached input at $0.007 per million (97% cheaper). That works out to roughly 1,515,152 output tokens per dollar.

What does the support chatbot workload cost on DeepSeek V4 Flash Vision Exp?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.002 per request and $30.36 per month on DeepSeek V4 Flash Vision Exp. Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill DeepSeek V4 Flash Vision Exp's 1M context window?

Sending 1M of input in a single request costs $0.2307 at $0.22 per million tokens — before any output. With prompt caching that same fill drops to about $0.0073 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is DeepSeek V4 Flash Vision Exp worth the price?

DeepSeek V4 Flash Vision Exp sits in the middle of the market (5 of 14 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.