Compare

Gpt 4o Realtime Preview 2025 06 03 vs Writer Palmyra X4

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.

Openai

Model

Gpt 4o Realtime Preview 2025 06 03

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Google

Model

Writer Palmyra X4

Tool calling

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.

Gpt 4o Realtime Preview 2025 06 03128K
Writer Palmyra X4128K

Same context window size for both models.

Gpt 4o Realtime Preview 2025 06 03 and Writer Palmyra X4 have identical context windows (128K tokens). Writer Palmyra X4 is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Writer Palmyra X4. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Writer Palmyra X4. Its 8K 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.

SpecGpt 4o Realtime Preview 2025 06 03Writer Palmyra X4
Context window128,000 tokens (128K)128,000 tokens (128K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APIYesNo
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.

ProviderGpt 4o Realtime Preview 2025 06 03 inGpt 4o Realtime Preview 2025 06 03 outWriter Palmyra X4 inWriter Palmyra X4 out
Aws Bedrock$2.50/M$10.00/M
Openai$5.00/M$20.00/M

Frequently asked questions

Writer Palmyra X4 has a larger context window: 128K 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