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DeepSeek V4 Flash vs Openai Gpt 4o

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Deepseek

Model

DeepSeek V4 Flash

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Openai

Model

Openai Gpt 4o

Context window

128K

128,000 tokens · ~96K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

DeepSeek V4 Flash1.0M
Openai Gpt 4o128K

DeepSeek V4 Flash has about 8.2× the context window of the other in this pair.

DeepSeek V4 Flash has 719% more context capacity (1048K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V4 Flash. Its 1048K context fits entire documents without chunking (vs 128K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDeepSeek V4 FlashOpenai Gpt 4o
Context window1,048,576 tokens (1048K)128,000 tokens (128K)
Max output tokens384,000 tokens (384K)N/A
Speed tierFastBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoYes
Release dateApr 2026N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderDeepSeek V4 Flash inDeepSeek V4 Flash outOpenai Gpt 4o inOpenai Gpt 4o out
Gradient

Frequently asked questions

DeepSeek V4 Flash has a larger context window: 1048K tokens vs 128K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model