Choosing between DeepSeek V3 and DeepSeek V4 comes down to two questions: does your workload need the newer model’s capability, and what does the difference cost at your volume? Both generations are served here through one OpenAI-compatible endpoint, so switching between them is a one-word change. This guide lays out the current line-up, live pricing, and a decision rule you can apply without running a benchmark suite.
DeepSeek ships several models concurrently rather than replacing one with the next, so “V3 vs V4” is really a choice among four active options with different price and capability profiles.
Live rates from the AIWave pricing endpoint:
| Model ID | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
deepseek-v3 | $0.154 | $0.308 |
deepseek-v4-flash | $0.638 | $1.914 |
deepseek-v4-pro | $1.914 | $5.742 |
deepseek-reasoner | $1.09 | $2.17 |
Rates read from the AIWave pricing endpoint on 2026-07-26. Check live pricing before budgeting — providers revise rates.
Two things stand out. First, deepseek-v4-flash is not a stripped-down afterthought —
it is the most cost-effective way to run V4-generation output. Second, the reasoning model carries a real premium,
which is the correct trade only when the task genuinely requires multi-step deduction.
DeepSeek documents its architecture and evaluation results publicly. Rather than repeat vendor benchmark tables here, the honest summary is this: V4 improves on V3 in coding and multi-step reasoning, and the gap widens as tasks get harder. For short, well-specified tasks — classification, extraction, summarisation of a single document — the difference is frequently invisible in production output. For the vendor’s own numbers, see the DeepSeek API documentation and the DeepSeek GitHub organisation.
We are deliberately not publishing our own benchmark figures. Anyone can generate a table; almost nobody can reproduce one. What we can give you is exact pricing and a method for deciding.
deepseek-v4-flash.deepseek-v4-pro. If those clear, route by
difficulty instead of upgrading everything.Step five is the part teams skip, and it is where most of the savings live. See how a multi-model router is built for the routing implementation.
from openai import OpenAI
client = OpenAI(api_key="sk-YOUR_KEY", base_url="https://aiwave.live/v1")
for model in ("deepseek-v3", "deepseek-v4-flash", "deepseek-v4-pro"):
r = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "Refactor this function for readability: ..."}],
temperature=0,
)
print(model, r.usage.total_tokens)
print(r.choices[0].message.content[:400])The request body is identical across all three. That is the entire point of running them behind one gateway: comparison costs you a loop, not an integration.
Take a coding assistant serving one developer full-time: roughly 20M input and 4M output tokens a month. Multiply those volumes by the rates in the table above and the arithmetic is straightforward — the cheap V4 model lands in single-digit dollars, the pro model several times that. Run your own volumes through the same multiplication rather than trusting a generic estimate; token consumption varies enormously by prompt design.
For a broader cost picture across providers, see AI API cost comparison 2026.
For simple, high-volume tasks where V4-flash is not meaningfully cheaper, V3 remains a reasonable choice. Check current rates on the pricing page — the ordering changes as providers revise prices.
No. Sign up with email or GitHub and call DeepSeek through an OpenAI-compatible endpoint. See accessing Chinese AI models without a Chinese phone number.
Yes. Every model here accepts the same request body, so the model name is a configuration value.
Every claim above traces back to one of these. Pricing figures come from the AIWave pricing endpoint on 2026-07-26; capability claims come from the vendors’ own documentation.