DeepSeek and OpenAI should be compared as exact API workloads, with thinking behavior, caching and processing conditions kept visible. A low published input rate is useful evidence only when the selected model completes the task reliably.
Side by side
This table compares each provider’s lowest documented baseline input-price model. Baseline prices use standard or short-context conditions and exclude separate free-tier and off-peak rows when paid standard rates exist. The full pricing reference preserves all documented conditions.
| Attribute | deepseek-flash | gpt-5.6-luna |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Official identifier | deepseek-flash | gpt-5.6-luna |
| Input USD / 1M | $0.3 | $0.2 |
| Output USD / 1M | $1.2 | $1.2 |
| Cached input USD / 1M | $0.006 | $0.02 |
| Context tokens | 1,000,000 | 1,050,000 |
| Maximum output tokens | 384,000 | 128,000 |
| Modalities | text, image | text |
| OpenAI-compatible endpoint | Yes | Yes |
| SDK languages | See official documentation | Python, JavaScript, Go, Java, C# |
| Reference | deepseek-flash | gpt-5.6-luna |
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Read the source and pricing condition beside a value before comparing it. The table describes published records; it does not establish which model will perform best on your task.
Pricing differences
DeepSeek’s current pricing distinguishes cache hits and misses and includes time-dependent pricing conditions. Keep the applicable schedule with the estimate. Official documentation.
OpenAI’s token categories and additional operation charges depend on the chosen model and feature. Compare the full request rather than one isolated input row. Official documentation.
For a reasoning task, measure the generation actually billed and the effort needed to correct failures. Keep an interactive daytime task separate from an offline job that can wait for an eligible processing condition. Do not treat the lowest conditional row as an all-day default.
DeepSeek pricing and OpenAI pricing explain the full charge categories. Use the cost calculator with the same observed workload for both.
Limits and free tiers
DeepSeek documents its request admission and service behavior in a separate limits reference. Read that contract before applying a limiter copied from another provider. Official documentation.
OpenAI publishes model and usage-tier rate constraints. Check the current project and organization settings used by the actual worker. Official documentation.
Account balance and throughput require separate checks. A valid credential is not evidence of a permanent free allowance, and a larger balance does not repair a malformed request. Review the account state before classifying a stopped job.
Models and modalities
The DeepSeek quickstart identifies its current request models and compatible access path. Copy the current identifier rather than infer it from an older family name. Official documentation.
OpenAI’s broader task catalog can be relevant when the same application also needs a separate media or embedding operation. That breadth should not decide a text-only task by itself. Compare the required operation and judge the model on the output your application must validate.
Developer experience (SDKs, compatibility, docs)
DeepSeek documents OpenAI-compatible access. Compatibility reduces initial client changes, while thinking-mode behavior still needs explicit handling. Official documentation.
The thinking guide explains the mode’s request and result considerations. Keep reasoning-related fields with the selected model configuration rather than scatter assumptions across the application. Official documentation.
When comparing with OpenAI Responses, define an internal result containing the final answer, usage, completion state and any tool actions. Preserve the underlying provider diagnostic so a shared wrapper does not erase the information needed to fix failures.
Which to pick for … (3 scenarios)
A budget-sensitive text pipeline. Evaluate both on extraction or classification examples with clear expected properties. Pick the candidate with the lowest cost per accepted result, including correction work.
A coding or reasoning workflow. Compare difficult tasks under explicit reasoning and output settings. Test whether the result can be validated, rather than interpret longer generated reasoning as proof of correctness.
An application with hosted tools. Choose the documented tool surface your workflow requires. If moving to a text endpoint would require recreating retrieval and orchestration, include that implementation effort in the decision.
Choose with a saved evaluation set and an explicit operating budget. DeepSeek setup and reference and OpenAI setup and reference link the first-request steps.
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Frequently asked questions
Can I use an OpenAI client with DeepSeek?
Is the lowest listed DeepSeek row always applicable?
How should reasoning workloads be compared?
Does an older model alias prove the current underlying model?
Will adding funds resolve every rejected request?
Sources
- Ds Api documentation ↗
- Ds Price documentation ↗
- Ds Limit documentation ↗
- Ds Think documentation ↗
- Oa Price documentation ↗
- Oa Limit documentation ↗
- Oa Api documentation ↗
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