Context Window

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Qwen-Max vs Writer 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.

Alibaba

Model

Qwen-Max

Tool calling

Context window

31K

30,720 tokens · ~23K words

Model page
Google

Model

Writer Palmyra X5

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Qwen-Max31K
Writer Palmyra X51M

Writer Palmyra X5 has about 32.6× the context window of the other in this pair.

Token calculator →Memory savings →

Writer Palmyra X5 has 3155% more context capacity (1000K vs 30K tokens). Writer Palmyra X5 is 62% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Writer Palmyra X5. Its 1000K context fits entire documents without chunking (vs 30K).

  • RAG / high-volume retrieval

    Use Writer Palmyra X5. Input tokens are 62% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecQwen-MaxWriter Palmyra X5
Context window30,720 tokens (30K)1,000,000 tokens (1000K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateFeb 2025N/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.

ProviderQwen-Max inQwen-Max outWriter Palmyra X5 inWriter Palmyra X5 out
Alibaba Cloud$1.60/M$6.40/M——
Aws Bedrock——$0.600/M$6.00/M

Frequently asked questions

Writer Palmyra X5 has a larger context window: 1000K tokens vs 30K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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.

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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