DeepSeek V4 Flash vs GPT-4o mini
DeepSeek V4 Flash is an open-weight chat model you can call on this endpoint; GPT-4o mini is a closed model from OpenAI. Below: what each costs for three realistic workloads, the limits that apply, and when to choose which.
OpenAI figures are public list prices and documented limits, checked 2026-09-16 against the provider page. Inference APIs prices are read live from the price list.
- Cost: It depends on the shape of the workload: DeepSeek V4 Flash is cheaper for chat assistant and generation-heavy, GPT-4o mini for long prompts or rag.
- Limits: OpenAI: Rate limits scale with cumulative spend through usage tiers 1–5. Here: no per-minute or per-day caps.
Side by side
| DeepSeek V4 Flash | GPT-4o mini | |
|---|---|---|
| Served by | Inference APIs | OpenAI |
| Model author | DeepSeek | OpenAI |
| Model id | deepseek-ai/DeepSeek-V4-Flash | gpt-4o-mini |
| Input price | $0.19 / 1M tokens | $0.15 / 1M tokens |
| Output price | $0.38 / 1M tokens | $0.60 / 1M tokens |
| Context window | 1M tokens | 128K tokens |
| Capabilities | Chat, Reasoning, Coding, Tool calling | Chat, Vision, Tool calling, JSON mode |
| Weights | Open | Closed |
| Status | Available | Available |
| Limits | No per-minute or per-day request or token caps; usage draws on a prepaid balance | Rate limits scale with cumulative spend through usage tiers 1–5. |
| Time to first token | 0.85 s | not measured by us |
| Output speed | 54 tokens / s | not measured by us |
What three workloads cost
List prices applied to the same work. The cheaper side of each row is in bold.
| Workload | DeepSeek V4 Flash | GPT-4o mini | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $2.66 | $3.00 | 11% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $17.10 | $15.00 | 12% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $8.55 | $12.75 | 33% |
It depends on the shape of the workload: DeepSeek V4 Flash is cheaper for chat assistant and generation-heavy, GPT-4o mini for long prompts or rag. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.
When to choose which
- You need more than 128K tokens of context (it takes 1M)
- You want open weights: the same model can be moved to another host or self-hosted later
- Cost per token matters most; it is the cheapest chat model here
- You process long documents and want a 1M-token context
- You want open weights, or the option to move the same model to another host
- You need to send images; DeepSeek V4 Flash is text-only
- You need image input
- You are already on OpenAI and want one bill and one SDK default
Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each.
Measured speed
Medians of three streamed runs on 2026-09-17, public endpoint, a prompt that asks for about 120 words. Time to first token is the first token of any kind, reasoning included; output speed counts every generated token from that point. Reasoning models then think before the visible answer starts: DeepSeek V4 Flash began answering after 20.1 s at default settings, which you can shorten with the reasoning controls on each model page. Expect ±30% with time of day. We did not measure OpenAI and do not quote other people's numbers; read "not measured" as unknown, not slow. Method and raw numbers: speed measurements.
Switching from OpenAI
Both speak the OpenAI wire format. The edit is the base URL, the key and the model id; then re-run your own prompts, because it is a different model.
import os
from openai import OpenAI
# before: OpenAI
# client = OpenAI(base_url="https://api.openai.com/v1", api_key=os.environ["OPENAI_API_KEY"])
# MODEL = "gpt-4o-mini"
# after: Inference APIs
client = OpenAI(base_url="https://api.inferenceapis.com/v1", api_key=os.environ["INFERENCE_API_KEY"])
MODEL = "deepseek-ai/DeepSeek-V4-Flash"
resp = client.chat.completions.create(model=MODEL, messages=[{"role": "user", "content": "Hello"}], max_tokens=500)More detail, including Node, LangChain and LiteLLM: switching OpenAI-compatible providers.
FAQ
Which is cheaper, DeepSeek V4 Flash or GPT-4o mini?
It depends on the shape of the workload: DeepSeek V4 Flash is cheaper for chat assistant and generation-heavy, GPT-4o mini for long prompts or rag. The table above uses list prices: DeepSeek V4 Flash $0.19 / 1M tokens input, $0.38 / 1M tokens output; GPT-4o mini $0.15 / 1M tokens input, $0.60 / 1M tokens output.
Can I switch from GPT-4o mini to DeepSeek V4 Flash without rewriting code?
Yes, if you call it through an OpenAI-compatible client. Change the base URL to https://api.inferenceapis.com/v1, swap the API key, and set the model to deepseek-ai/DeepSeek-V4-Flash. Because it is a different model, re-run your prompts and evaluations before moving production traffic.
What limits apply to GPT-4o mini?
Rate limits scale with cumulative spend through usage tiers 1–5. On Inference APIs there are no per-minute or per-day request or token caps; usage draws on a prepaid balance.
Why does DeepSeek V4 Flash use more output tokens than the visible answer?
It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.