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LFM2.5-1.2B-Thinking (free) 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.

Liquid

Model

LFM2.5-1.2B-Thinking (free)

Context window

33K

32,768 tokens · ~25K 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.

LFM2.5-1.2B-Thinking (free)33K
Writer Palmyra X4128K

Writer Palmyra X4 has about 3.9× the context window of the other in this pair.

Writer Palmyra X4 has 290% more context capacity (128K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Writer Palmyra X4. Its 128K context fits entire documents without chunking (vs 32K).

Full specs

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

SpecLFM2.5-1.2B-Thinking (free)Writer Palmyra X4
Context window32,768 tokens (32K)128,000 tokens (128K)
Max output tokensN/A8,192 tokens (8K)
Speed tierDeepBalanced
VisionNoNo
Function callingNoYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateJan 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.

ProviderLFM2.5-1.2B-Thinking (free) inLFM2.5-1.2B-Thinking (free) outWriter Palmyra X4 inWriter Palmyra X4 out
Aws Bedrock$2.50/M$10.00/M

Frequently asked questions

Writer Palmyra X4 has a larger context window: 128K 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