DeepSeek vs ChatGPT: The Cost Gap and What to Move

DeepSeek vs ChatGPT: which one should you use?

You are asking two questions at once. As a chatbot for daily use, ChatGPT has the deeper product surface. As a model behind a feature or workflow, DeepSeek is the one to test first for high-volume, well-specified work, because it publishes open weights under the MIT license, accepts OpenAI and Anthropic SDK formats, and prices frontier-tier tokens far below OpenAI's flagship.

The better move is to stop treating this as a one-time pick. The teams that save money route each task to the cheapest model that handles it, and keep that routing in software they can change. That software is where Major fits. You build the routing app once on Major, and swapping DeepSeek for ChatGPT, or the reverse, becomes a configuration change instead of a rewrite.

What DeepSeek is

DeepSeek names three things: the company, the consumer chatbot, and the open-weight model family builders use. The current family is V4, with deepseek-v4-flash and deepseek-v4-pro on the API. Both list a 1M-token context window. DeepSeek-V4-Pro ships on Hugging Face as downloadable weights under the MIT license.

The API is OpenAI-shaped. tools accepts up to 128 function definitions, tool_choice can force a named function, and response_format supports JSON output. OpenAI-format requests go to https://api.deepseek.com, and Anthropic-format requests go to https://api.deepseek.com/anthropic. Moving an existing client usually means changing a base URL, a key, and a model string.

What ChatGPT is

ChatGPT is the product. The OpenAI API is what builders use, and the two are priced, versioned, and governed separately. The API publishes a ladder of models, with gpt-6-astra at the top and the gpt-5.6 line below it, plus separate lines for realtime audio, images, embeddings, moderation, and code. Batch mode halves the cost of work that can wait.

That breadth matters when one product needs several modalities from a single vendor. It does not settle which model should classify 100,000 support tickets overnight.

DeepSeek vs ChatGPT: the differences that matter

• Cost at the frontier tier: the gap is wide. DeepSeek's pro model costs a small fraction of OpenAI's flagship per million tokens on the vendors' published rate cards.
• Cost at the budget tier: the gap is narrow. OpenAI's smallest model in Batch mode can land at or below DeepSeek's cheapest rate.
• Timing: DeepSeek prices by time of day. Weekday windows of 01:00-04:00 and 06:00-10:00 UTC run at standard rates, and other hours run at a discount. A nightly job costs less than the same job at midday.
• Open weights: MIT-licensed weights let you run the model in your own network, pin a version, and fine-tune without a vendor tuning API. A model this size needs real inference infrastructure, so most teams still use the hosted API and treat the weights as an exit path.
• Product breadth: OpenAI covers more modalities and tooling in one place.
• Data handling: ask both vendors where data is processed, how long prompts and completions are retained, who the subprocessors are, and whether zero retention is available. Some jurisdictions and organizations restrict DeepSeek; check your own policy rather than relying on a blog post. Self-hosting the weights removes the vendor from the data path and adds operational work.

Rate cards change often. Pull both pricing pages the day you budget, and price your own token mix: input and output tokens, cache hits, and batch eligibility all move the total.

Which model fits which workload

Treat each line as a hypothesis and test it on your own traffic.

• High-volume classification and tagging: start with a DeepSeek flash-tier model in discount hours. The task is well specified and cheap to validate.
• Structured extraction: DeepSeek flash with response_format JSON, validated downstream.
• Long-context reasoning over large documents: DeepSeek pro, for its 1M-token window.
• Customer-facing chat and multimodal features: an OpenAI mid-tier model, where product tooling matters more than token price.
• Code generation in a developer loop: an OpenAI code model, where latency and editor integrations count.
• Low-volume, high-stakes judgment calls: the OpenAI flagship, where capability justifies the cost.
• Strict data residency: self-hosted DeepSeek weights.

The part nobody plans for: switching

Changing a base URL takes a minute. Rebuilding the workflow around it takes weeks, because the ticket taxonomy, retry rules, escalation logic, and system-of-record writes usually live inside prompts, notebooks, or one-off scripts. The model was never the expensive thing to change. The logic wrapped around it was.

This is the same reason re-reasoning every run is costly. If a model decides the same routing question on every call, you pay for it on every call and you cannot audit why a ticket went where it did.

Build the model-routing app on Major

Major is the enterprise platform where agents build the software they run on. For this problem, the agent works out the routing rules once, builds an app that runs them, and then runs the app instead of re-reasoning each task.

• Connectors: Zendesk or Intercom for tickets, Slack for escalations, and Snowflake or the managed Postgres database for results. Connect model providers through scoped credentials so DeepSeek and OpenAI keys never sit in a prompt.
• App that holds state: a routing table (task type, model, max cost per run, fallback model), a run log of every call with tokens and outcome, and a review queue for low-confidence results.
• Agent and its judgment: the agent classifies unfamiliar task types, proposes a model for each, and decides when a result needs a person. Repeatable work such as fetching, formatting, validating JSON, and writing back runs as deterministic code.
• Trigger: a scheduled workflow that runs the batch at DeepSeek's discount hours, plus an event trigger for urgent tickets that go straight to the stronger model.
• Governance: role-based access to the routing table, audit logs on every model call and write, and approval in Slack before the routing rule changes.
• Switching: change one row in the routing table and the whole workflow moves to a different model, with the history intact.

Because the repeatable steps are code, cost is front-loaded while the agent builds the app, then flat as volume grows. The model still handles the judgment calls. It handles fewer of them.

Build your routing app on Major and keep the model choice reversible.

Start building on Major

Describe the routing workflow you want, and Major's agent builds the governed app that sends each task to the right model and logs every call. Swap models later without rebuilding the workflow.

• Start building free
• Book a demo

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