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Compare these two on your own workload

Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.

DeepSeek Flash (V4.1) vs GPT-4o mini

Open-weight economics against OpenAI's older cheap model.

Prices verified 30 Oct 2026

OpenAI retires GPT-4o mini on 14 Jan 2027; the named replacement is GPT-6 Luna.

Input / 1M$0.150
Output / 1M$0.600
Cached input / 1M$0.00300
Batchn/a
Context1.0M tokens
Blended (3:1)$0.262
Input / 1M$0.150
Output / 1M$0.600
Cached input / 1M$0.075
Batch50% off
Context128K tokens
Blended (3:1)$0.262

Monthly cost by workload shape

The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.

WorkloadDeepSeek Flash (V4.1)GPT-4o miniDifference
Support chat
200/day · 1500 in / 300 out
$2.47$2.47Same costModel it →
RAG / search answers
20K/day · 3000 in / 400 out
$420.07$420.07Same costModel it →
Agent / tool-use loop
8.0K/day · 2500 in / 600 out
$178.99$178.99Same costModel it →
Batch summarization
100K/day · 5000 in / 500 out
$3,196.20$3,196.20Same costModel it →
Code assistant
15K/day · 2000 in / 800 out
$356.15$356.15Same costModel it →

Frequently asked questions

Which is cheaper, DeepSeek Flash (V4.1) or GPT-4o mini?

Neither, on list price: both charge $0.150 input and $0.600 output per 1M tokens, so they cost the same at every input:output ratio. Caching, batch and long-context terms decide the bill.

What is the price difference between DeepSeek Flash (V4.1) and GPT-4o mini?

DeepSeek Flash (V4.1): $0.150 input, $0.600 output per 1M tokens. GPT-4o mini: $0.150 input, $0.600 output. That is 1.0x on input and 1.0x on output.

Does prompt caching change the answer?

DeepSeek Flash (V4.1) caches input at $0.00300/1M and GPT-4o mini caches at $0.075/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.

This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.

Rate cards decide nothing on their own.

Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.

See Finitizer Tokenomics