Comparison page
DeepSeek V4 Flash vs GPT-5.4 mini
Compare two lower-cost options for repetitive production workloads and prompt-heavy apps.
Side-by-side pricing
The table uses the same example request on both models so you can compare low-cost production tasks without changing the prompt shape.
Input 1,000 / Output 250
| Model | Input / 1M | Cached input / 1M | Output / 1M | Example request | Source |
|---|---|---|---|---|---|
DeepSeek V4 Flash DeepSeek's lower-cost general model with cache-hit discounts for repeated contexts. | $0.140 | $0.0028 | $0.280 | $0.0002 | DeepSeek pricing |
GPT-5.4 mini Latest mini model in the GPT-5.4 family for coding and agentic workflows. | $0.750 | No public rate | $4.50 | $0.0019 | OpenAI pricing |
When to choose each model
DeepSeek V4 Flash
Choose DeepSeek V4 Flash when you want the economics or capability profile of DeepSeek and you can justify the published rate.
GPT-5.4 mini
Choose GPT-5.4 mini when you need the second option in this comparison and want to test whether it lowers spend without hurting the workflow.
Use cases
- • Routing routine prompts to the cheapest acceptable model.
- • Finding the low-cost default for a user-facing workflow.
- • Estimating savings from a cheaper model tier.
If the prompt is noisy or repetitive, run it through the prompt optimizer first.
If the brief is too loose, use the context engineer to tighten the instructions before you compare models again.
For the broader pricing strategy, read the LLM cost optimization pillar and the cost-per-million-tokens cheat sheet.
FAQ
Which model is the cheaper baseline?
DeepSeek V4 Flash is cheaper on standard token pricing, though the right choice still depends on your accuracy target and deployment model.
Why compare a DeepSeek model with GPT-5.4 mini?
Because these are both practical lower-cost defaults, and many teams use them to decide where to place routine traffic.
Do cache-hit rates matter here?
Yes. DeepSeek's cache-hit pricing can materially change repeated-request costs, so it is worth factoring in when you estimate usage.