TPEE Toptronic® Prompt Engineering Editor

50. DeepSeek V4 - Open Source Excellence from China

Understand DeepSeek's open-weight models and their cost/performance advantages.

By Jacques Botte, founder of Toptronic®. Last updated 17 October 2026.

The lesson

DeepSeek V4 Pro offers a 1 million token context window at $1.32 input / $3.96 output per 1M tokens at peak ($0.66 / $1.98 off-peak) - less than Western frontier models.

DeepSeek V4.1 Flash provides the same 1M context at $0.30 / $1.20 per 1M tokens at peak ($0.15 / $0.60 off-peak). That is roughly 17 times cheaper than GPT-5.5 on INPUT tokens at peak (the output ratio is a different figure, so compare both before you commit), which is a large saving for high-volume applications.

DeepSeek V4 Pro is the reasoning-capable top tier of the V4 line, making it a strong fit for math, logic, debugging and verification work.

All DeepSeek models are available as open weights, meaning they can be downloaded and run locally via Ollama or LM Studio for complete privacy and zero API costs.

For code-heavy work the catalogue carries Codestral 2508, Qwen3 Coder Plus and GPT-5.1 Codex, while DeepSeek's own V4 models stay open-weight so a team can run one locally.

An automotive workshop foreman runs open-weight DeepSeek locally for diagnostic notes, copying the prompt out of TPEE by hand.

A security analyst at a small consultancy keeps incident notes on a local DeepSeek model so client data stays on their own hardware.

A junior data analyst at a utility uses DeepSeek V4.1 Flash to clean a spreadsheet export, building the prompt in TPEE first.

A robotics technician uses DeepSeek V4.1 Flash to review control scripts that carry comments in another language, pasting the TPEE prompt manually.

A software lead at a small studio documents in TPEE why an open-weight DeepSeek model suits a team with a modest hardware budget.

A documentation writer drafts release notes on a low-cost DeepSeek model after building the prompt in TPEE.

A pharmacy technician keeps reference drafts on an offline DeepSeek model so nothing leaves the premises.

A research assistant uses a reasoning DeepSeek variant to check a statistics calculation and asks it to show its working.

A construction estimator runs a local DeepSeek model to draft quantity notes where site connectivity is unreliable.

A customer support manager builds a quote explanation prompt in TPEE, then copies it into a cheap DeepSeek model for high-volume replies.

Check yourself

Question 1: What makes DeepSeek V4 notable?
  1. It is closed source only
  2. Open-weights with 1M context and competitive pricing ($1.32/$3.96 per 1M tokens at peak) — correct
  3. It only runs on Apple devices
  4. It cannot code

Answer: Open-weights with 1M context and competitive pricing ($1.32/$3.96 per 1M tokens at peak)

DeepSeek V4 Pro offers a 1 million token context window, strong coding and reasoning, and costs less than Western frontier models at $1.32/$3.96 per 1M tokens at peak ($0.66/$1.98 off-peak).

Question 2: Which DeepSeek row in the catalogue is the strongest V4 tier?
  1. A gaming console
  2. DeepSeek V4 Pro - 1M context, $1.32/$3.96 per 1M tokens at peak — correct
  3. A type of hard drive
  4. A web browser

Answer: DeepSeek V4 Pro - 1M context, $1.32/$3.96 per 1M tokens at peak

DeepSeek V4 Pro is the strongest tier of DeepSeek's V4 line in the catalogue: 1M-token context, $1.32/$3.96 per 1M tokens at peak ($0.66/$1.98 off-peak), released 13 August 2026.

Question 3: Why choose DeepSeek for coding tasks?
  1. It cannot code
  2. Its V4 models are open-weight, so they can be run locally while you keep your code on your own hardware — correct
  3. It only writes Python
  4. It is the most expensive option

Answer: Its V4 models are open-weight, so they can be run locally while you keep your code on your own hardware

DeepSeek's V4 models are open-weight, so Ollama or LM Studio can run one locally with no per-token charge. For API code specialists the catalogue carries Codestral 2508, Qwen3 Coder Plus and GPT-5.1 Codex.

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