DeepSeek vs ChatGPT: What Each One Actually Costs in 2026
The short answer
DeepSeek is between 11× and 54× cheaper per token. ChatGPT has the broader product — image generation, voice, video, a consumer app people already use. Whether that gap matters depends entirely on whether you are buying an API or a subscription.
If you are building on an API and your workload is text, DeepSeek V4-Flash costs $0.14 per million input tokens against GPT-5.4's $2.50. That is not a discount, it is a different order of magnitude, and it is the entire reason this comparison exists.
If you want an assistant that generates images, holds a voice conversation, browses the web and runs code in a sandbox, DeepSeek does not compete. It ships two text models.
Last verified 25 July 2026 against DeepSeek's API documentation, OpenAI's developer pricing page and chatgpt.com/pricing. We re-check this page weekly and date every change.
What each company actually ships right now
DeepSeek
Two models, both text-only, both released 24 April 2026:
| Model | Total params | Active per token | Context | Max output | Concurrency |
|---|---|---|---|---|---|
| deepseek-v4-flash | 284B | ~13B | 1M | 384K | 2,500 |
| deepseek-v4-pro | 1.6T | ~49B | 1M | 384K | 500 |
Mixture-of-experts, MIT licence, open weights. The legacy aliases deepseek-chat and deepseek-reasoner were retired on 24 July 2026 at 15:59 UTC — if your integration still calls them it is broken right now. See the migration guide →
OpenAI
| Model | Context | Max output |
|---|---|---|
| gpt-5.6-sol | 1.05M | 128K |
| gpt-5.6-terra | 1.05M | 128K |
| gpt-5.6-luna | 1.05M | 128K |
All three support vision, function calling, web search, file search and computer use. Alongside them OpenAI ships gpt-image-2 for images, the gpt-realtime-2.1 family for audio, sora-2 for video and gpt-5.3-codex for coding.
That breadth is the real difference. DeepSeek gives you text at a low price. OpenAI gives you a platform.
API pricing, side by side
Per 1M tokens, standard tier, short context:
| Model | Input | Cached input | Output |
|---|---|---|---|
| DeepSeek V4-Flash | $0.14 | $0.0028 | $0.28 |
| DeepSeek V4-Pro | $0.435 | $0.003625 | $0.87 |
| GPT-5.6 Sol | $5.00 | $0.50 | $30.00 |
| GPT-5.6 Terra | $2.50 | $0.25 | $15.00 |
| GPT-5.6 Luna | $1.00 | $0.10 | $6.00 |
| GPT-5.5 | $5.00 | $0.50 | $30.00 |
| GPT-5.4 | $2.50 | $0.25 | $15.00 |
| GPT-5.4 mini | $0.75 | $0.075 | $4.50 |
| GPT-5.4 nano | $0.20 | $0.02 | $1.25 |
Two things worth knowing that most comparisons miss.
OpenAI charges roughly double above a context threshold. The pricing page splits every flagship model into "short context" and "long context" columns — GPT-5.6 Sol goes from $5.00/$30.00 to $10.00/$45.00. If you are working with long documents, the headline price is not the price you pay. DeepSeek charges one rate across the full 1M window.
Cache-hit pricing is where the gap is widest. DeepSeek's cache-hit input is $0.0028 for Flash — 2% of the cache-miss rate. OpenAI's cached input is 10% of standard. On a workload with heavy prompt reuse, the effective difference is far larger than the headline suggests.
The multiples, spelled out
| Comparison | Input | Output |
|---|---|---|
| GPT-5.4 vs V4-Flash | 17.9× more | 53.6× more |
| GPT-5.4 vs V4-Pro | 5.7× more | 17.2× more |
| GPT-5.6 Sol vs V4-Pro | 11.5× more | 34.5× more |
Full per-token breakdown and a cost calculator on our DeepSeek pricing page, and the architecture detail on DeepSeek V4 explained.
Consumer plans
| ChatGPT plan | Price (US) | Notes |
|---|---|---|
| Free | $0 | Limited GPT-5.5 Instant, limited messages/uploads/image generation |
| Go | $8/mo | 10× more messages, uploads and image creation than Free; longer memory. OpenAI notes pricing is localized in some markets |
| Plus | $20/mo | GPT-5.6 reasoning models, projects, scheduled tasks, custom GPTs |
| Pro | $100/mo or $200/mo | Two separate tiers — the $200 tier runs GPT-5.5 Pro with 20× Plus limits |
DeepSeek's consumer side is chat.deepseek.com — free, but it requires an account.
US prices as of 25 July 2026; OpenAI localizes pricing in some markets.
Where each one genuinely wins
Choose DeepSeek when you are running text at volume, cost per token is the binding constraint, you want open weights you can self-host, or you need a 1M context window without a long-context surcharge.
Choose ChatGPT when you need images, audio, video or browsing; when you want one vendor for the whole stack; when your team needs a polished consumer app; or when enterprise controls, SSO and data-residency guarantees are procurement requirements.
The honest framing: these are not competing for the same buyer. DeepSeek competes on price per token for text. OpenAI competes on being the default. Most teams that pick DeepSeek do so for a specific high-volume workload, not to replace ChatGPT.
Why this page has no benchmark table
Almost every DeepSeek-versus-ChatGPT comparison online carries a table of SWE-bench, MMLU and HumanEval scores. We checked those figures and most of them cannot be traced to the vendor that supposedly published them, or to any named leaderboard.
We would rather tell you where to check than print a number we cannot stand behind:
- Artificial Analysis publishes dated, methodology-documented leaderboards. On GPQA Diamond it currently has GPT-5.6 Sol at 94.1% and GPT-5.5 (xhigh) at 93.5%.
- DeepSeek publishes its own comparison table on the V4-Pro model card. Note that it benchmarks against Opus-4.6 Max, GPT-5.4 xHigh and Gemini-3.1-Pro High — the models current at V4's April release, not today's.
- OpenAI publishes model-specific evaluations in its release posts.
If a comparison page shows you a benchmark score with no link and no date, treat it as decoration.