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Openai Gpt 6 1 Terra vs Palmyra X5

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

Openai Gpt 6 1 Terra

Image inputTool calling

Context window

922K

922,000 tokens · ~692K words

Model page
Writer

Model

Palmyra X5

Context window

1.0M

1,040,000 tokens · ~780K 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.

Openai Gpt 6 1 Terra922K
Palmyra X51.0M

Palmyra X5 has about 1.1× the context window of the other in this pair.

Palmyra X5 has 12% more context capacity (1040K vs 922K tokens). Palmyra X5 is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Palmyra X5. Its 1040K context fits entire documents without chunking (vs 922K).

  • RAG / high-volume retrieval

    Use Palmyra X5. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Openai Gpt 6 1 Terra. Its 128K 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.

SpecOpenai Gpt 6 1 TerraPalmyra X5
Context window922,000 tokens (922K)1,040,000 tokens (1040K)
Max output tokens128,000 tokens (128K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AJan 2026

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.

ProviderOpenai Gpt 6 1 Terra inOpenai Gpt 6 1 Terra outPalmyra X5 inPalmyra X5 out
Aws Bedrock$1.00/M$5.00/M——
Openrouter——$0.600/M$6.00/M

Frequently asked questions

Palmyra X5 has a larger context window: 1040K tokens vs 922K. 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