How much does DeepSeek cost vs GPT?

DeepSeek is cheaper than GPT on both input and output token pricing, but the gap matters most when your workload is output-heavy. If you mostly send prompts and get short replies, the bill stays small either way, but long generations make the difference obvious fast.

Start with the only number that matters: per 1M tokens

Token pricing is easy to mess up because vendors quote separate input and output rates. For a real comparison, you need the blended cost for your workload, not a vague headline number.

Here are the current figures we can actually use:

That blended column is computed as 75% input and 25% output, which is the same weighting used by the AI Cost Index for its Frontier and Budget baskets. It is a practical way to compare models when your app does more prompting than generating.

Worked example: the same task on DeepSeek and GPT

Let’s make this concrete. Say your coding agent uses 900,000 input tokens and 300,000 output tokens in a month, which is a 3:1 input-output mix.

deepseek-chat:

Input cost = 900,000 / 1,000,000 × $0.28 = $0.252
Output cost = 300,000 / 1,000,000 × $0.42 = $0.126
Total = $0.378

gpt-4o:

Input cost = 900,000 / 1,000,000 × $2.5 = $2.25
Output cost = 300,000 / 1,000,000 × $10 = $3.00
Total = $5.25

Same token volume, same task shape, very different bill. In this example, GPT-4o costs $4.872 more for the month, and DeepSeek comes out at about 7.9% of the GPT-4o cost. That is why token mix matters more than model branding when you are building something that runs often.

If you want a broader view of how these prices fit into the market, the live model list is the place to check current per-model pricing across the catalog.

Where DeepSeek wins, and where it doesn’t

The cheap-vs-expensive story is not just about price. It is about which tasks can tolerate a lower-cost model without dragging down product quality.

• Good fit for DeepSeek: repetitive code edits, extraction, summarization, lightweight agent steps, and high-volume internal automation.
• Better fit for GPT-4o: user-facing flows where response quality, instruction following, and robustness matter more than shaving cents.
• Watch out for output-heavy tasks: chatty agents, long-form rewrites, and tool loops can make output pricing dominate the bill.
• Check latency and success rate too: a cheaper model that retries more often can erase the savings.

That last point matters because raw token price is only one dimension of cost. If a model is cheap but flaky, you pay in retries, latency, and engineering time. MyTokenTracker records cost, input/output/cache/reasoning tokens, latency, and success, so you can compare models by actual task outcome instead of vibes.

How DeepSeek compares with the rest of the stack

If you are choosing between models for a coding workflow, DeepSeek sits in the budget lane. GPT-4o-mini is also budget-friendly, while GPT-4o is much more expensive. Anthropic and Gemini span a wider range, depending on which model you pick and what the task needs.

The table is not saying “cheap is always better.” It is saying that if your task is mostly mechanical, DeepSeek can be a strong default. If your task is user-facing, complex, or expensive to get wrong, you may accept a higher token rate to reduce failures.

For a macro view of how these prices line up over time, the State of AI page is useful for spotting whether the market is actually getting cheaper or just rearranging the same costs into new product names.

Why output tokens can erase the savings

Developers often focus on input cost because prompts feel tangible. But output is where some models quietly get expensive, especially when you ask for long explanations, code blocks, or multi-step reasoning.

Using the prices above, output is 1.5 times the input rate for deepseek-chat, 4 times the input rate for gpt-4o-mini, and 4 times the input rate for gpt-4o. That means a model with a cheap prompt price can still become costly if it talks too much.

Here is the practical rule:

• If your app sends lots of context and gets short answers, input pricing matters most.
• If your app asks for long completions, output pricing can dominate the bill.
• If your app uses tools and retries, latency and success rate become part of the real cost.

This is why a “cheap” model can still surprise you on the invoice. The token mix is the bill.

How to track this in your own stack

If you want to know what DeepSeek or GPT is actually costing you, don’t estimate from memory. Read the logs your coding agents already keep locally and compute the real 30-day total at API prices.

Run npx mytokentracker to get a local cost table with no account needed. Usage data stays on your machine, and the tool prints a 30-day view using API prices. If you want a synced dashboard, create a free account and run npx mytokentracker init, which uploads history and then syncs every 30 minutes.

It works with Claude Code, Codex, GitHub Copilot CLI, Antigravity, OpenCode, Amp, Qwen Code, and more. If you use wrappers, MyTokenTracker can also capture OpenAI, Anthropic, Gemini, and Mistral directly. For anything else, there is a single events API POST. The point is to make cost visible by provider, model, platform, and use-case, not to guess from a monthly card charge.

If you care about open data, the dataset is available under CC BY 4.0, and there is also a public API for people who want to build their own dashboards or internal reports.

FAQ

Is DeepSeek always cheaper than GPT?

Not always in every possible workload, but on the current prices here, deepseek-chat is cheaper than gpt-4o on both input and output tokens, and cheaper than gpt-4o-mini on blended cost for a 3:1 input-output mix. The exact winner still depends on your token mix and how much output your app generates.

Should I optimize for input price or output price?

Optimize for the side that dominates your workload. If your app sends huge prompts, input price matters more. If it generates long answers or code, output price can dominate. In practice, you should watch both, then verify with real usage data instead of assuming the prompt side is the whole story.

What is the fastest way to know my real AI cost?

Use your local usage logs and calculate the last 30 days at model prices. That gives you actual spend by model, platform, and task shape. If you want a quick start, run npx mytokentracker and inspect the table it prints. If you want a dashboard, use npx mytokentracker init to sync history.

Want to see your own token bill instead of guessing? Install MyTokenTracker and check your real 30-day cost in a minute.