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 19 September 2026.

The lesson

DeepSeek V4 Pro offers a 1 million token context window with extremely competitive permanent pricing at $0.435 input / $0.87 output per 1M tokens (May 2026) - dramatically less than Western frontier models.

DeepSeek V4 Flash provides the same 1M context at just $0.14 / $0.28 per 1M tokens. That is about 35 times cheaper than GPT-5.5 on INPUT tokens (the output ratio is a different figure, so compare both before you commit), which is a large saving for high-volume applications.

DeepSeek-R1 is a reasoning-specialized model that shows its chain-of-thought, similar to o3-mini, making it excellent for math, logic, debugging, and verification tasks.

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.

DeepSeek Coder variants excel at programming tasks, supporting 80+ programming languages with strong project-level code understanding - ideal for development workflows.

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 Coder to clean a spreadsheet export, building the prompt in TPEE first.

A robotics technician uses DeepSeek Coder 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 extremely competitive pricing ($0.435/$0.87 per 1M tokens) — correct
  3. It only runs on Apple devices
  4. It cannot code

Answer: Open-weights with 1M context and extremely competitive pricing ($0.435/$0.87 per 1M tokens)

DeepSeek V4 Pro offers a 1 million token context window, strong coding/reasoning, and costs dramatically less than Western frontier models at $0.435/$0.87 per 1M tokens.

Question 2: What is DeepSeek-R1?
  1. A gaming console
  2. A reasoning-specialized model that shows its chain-of-thought — correct
  3. A type of hard drive
  4. A web browser

Answer: A reasoning-specialized model that shows its chain-of-thought

DeepSeek-R1 is a reasoning model similar to o3-mini, explicitly showing its thinking process for better verifiability.

Question 3: Why choose DeepSeek for coding tasks?
  1. It cannot code
  2. DeepSeek Coder variants excel at programming and are open-weight for local deployment — correct
  3. It only writes Python
  4. It is the most expensive option

Answer: DeepSeek Coder variants excel at programming and are open-weight for local deployment

DeepSeek Coder models are specifically trained for programming tasks and available as open weights for local use via Ollama/LM Studio.

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