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The easiest way to compare models isn’t the price list — it’s a real invoice. Here’s the usage export from one day of Claude Code: an ordinary day with an agent reading the repo, editing files and running tests. It added up to 405 million tokens and $167.24:
Billing lineTokens for the dayPriceCost
Input (cache hit)395,374,336$0.30 / MTok$118.61
Input (cache miss)8,509,057$3.00 / MTok$25.53
Output1,540,240$15.00 / MTok$23.10
Total405,423,633—$167.24
One line is worth pausing on. Almost the entire volume — 395 million tokens out of 405 — is cache reads, not fresh input. Claude caches the repeated context: the system prompt, the contents of open files, the conversation history. Reading it costs $0.30 per million instead of $3.00 — ten times less. Without caching, the same day would have run about $1,235. So $167 is already a heavily discounted number, not the sticker price. The question from here is simple: what do those exact same tokens cost on DeepSeek?

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The same day on DeepSeek V4 Pro

Same three lines, priced with deepseek-v4-pro — the top of the line, the closest match to Sonnet by class of work:
Billing lineTokens for the dayPriceCost
Input (cache hit)395,374,336$0.004 / MTok$1.58
Input (cache miss)8,509,057$0.435 / MTok$3.70
Output1,540,240$0.87 / MTok$1.34
Total405,423,633—$6.62
$167.24 versus $6.62 — for the same work, that’s 25× cheaper.

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The same day on DeepSeek V4 Flash

deepseek-v4-flash is the cheapest model in the line, and it’s fast; it’s what you reach for on a steady stream of simple tasks:
Billing lineTokens for the dayPriceCost
Input (cache hit)395,374,336$0.01 / MTok$3.95
Input (cache miss)8,509,057$0.14 / MTok$1.19
Output1,540,240$0.28 / MTok$0.43
Total405,423,633—$5.57
Here the gap is even wider — roughly 30×.

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The result in one table

ModelCost of the dayDifference
Claude Sonnet$167.24—
DeepSeek V4 Pro$6.62~25× cheaper
DeepSeek V4 Flash$5.57~30× cheaper

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Where the gap comes from

It comes from two things, and both favor DeepSeek. Price per token. Fresh input on Sonnet is $3.00 per million; on Pro it’s $0.435 — nearly seven times less. Output is $15.00 against $0.87, a gap of more than seventeen times. Output is where the saving shows most: the longer the model’s answers, the wider the invoices spread apart. Price of a cache read. Cache carries the load here — 97.5% of the tokens. And even on cache reads DeepSeek is cheaper: $0.004 per million on Pro against $0.30 on Sonnet. For this day, cache reads cost $118.61 on Claude and $1.58 on Pro. Stack one on top of the other, and on an identical set of tokens the gap reaches 25–30×.
Prices are current as of testing and the vendors can change them. Current per-token rates are on the Pricing page and at www.ruapi.ai. What matters here is the order of the difference between the models, not the exact dollar figures.

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Which one to use

Cheaper isn’t the same as “always better.” It depends on the task.
  • deepseek-v4-pro — the everyday workhorse: code, refactoring, reasoning, agents. Quality stays close to the top models while costing a fraction. If Claude Code or a similar agent is running for you right now, this is the first thing to swap in.
  • deepseek-v4-flash — for volume: classification, tagging, short replies, rough drafts. Where price and speed matter more than top quality.
  • Claude — when you need to read images (DeepSeek has no vision) or top quality on the hardest tasks. One RuAPI token covers everything, so the models happily coexist in a single project.
DeepSeek models are text-only: they don’t read images, diagrams or screenshots. If you need vision, look at Claude, Gemini or GLM-5V. More on the line itself is on the DeepSeek API page.

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How to switch

If your project already talks to RuAPI over the OpenAI-compatible protocol, there’s one line to change — the model ID. The base_url and key stay the same: 
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_RUAPI_KEY",
    base_url="https://www.ruapi.ai/v1",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",   # instead of claude-sonnet-4-6
    messages=[{"role": "user", "content": "Refactor this function and explain what you changed"}],
)
print(resp.choices[0].message.content)
Setup from scratch — the key and a first request in Python and curl — is in the Quickstart.

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Next

DeepSeek API

Pro and Flash: how they differ and what they do.

Claude models

Lines, versions, and when Claude earns its price.

How billing works

Pay per token used, with a log of every request.

Quickstart

base_url, key and your first request.