Compare

Fw Deepseek V4 Pro vs Gpt 4 32k

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

Fw Deepseek V4 Pro

Tool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Openai

Model

Gpt 4 32k

Context window

33K

32,768 tokens · ~25K 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.

Fw Deepseek V4 Pro1M
Gpt 4 32k33K

Fw Deepseek V4 Pro has about 30.5× the context window of the other in this pair.

Fw Deepseek V4 Pro has 2951% more context capacity (1000K vs 32K tokens). Fw Deepseek V4 Pro is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Deepseek V4 Pro. Its 1000K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Fw Deepseek V4 Pro. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Fw Deepseek V4 Pro. Its 384K max output lets you generate complete artifacts in one request.

Full specs

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

SpecFw Deepseek V4 ProGpt 4 32k
Context window1,000,000 tokens (1000K)32,768 tokens (32K)
Max output tokens384,000 tokens (384K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderFw Deepseek V4 Pro inFw Deepseek V4 Pro outGpt 4 32k inGpt 4 32k out
Azure$1.93/M$3.83/M$60.00/M$120.00/M

Frequently asked questions

Fw Deepseek V4 Pro has a larger context window: 1000K tokens vs 32K. 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