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Gemma 7b It 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.
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.
Writer Palmyra X4 has about 15.6× the context window of the other in this pair.
Writer Palmyra X4 has 1462% more context capacity (128K vs 8K tokens). Gemma 7b It is 98% cheaper on input.
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 8K).
RAG / high-volume retrieval
Use Gemma 7b It. Input tokens are 98% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gemma 7b It | Writer Palmyra X4 |
|---|---|---|
| Context window | 8,192 tokens (8K) | 128,000 tokens (128K) |
| Max output tokens | 8,192 tokens (8K) | 8,192 tokens (8K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | N/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.
| Provider | Gemma 7b It in | Gemma 7b It out | Writer Palmyra X4 in | Writer Palmyra X4 out |
|---|---|---|---|---|
| Anyscale | $0.150/M | $0.150/M | — | — |
| Aws Bedrock | — | — | $2.50/M | $10.00/M |
| Fireworks | $0.200/M | $0.200/M | — | — |
| Groq | $0.050/M | $0.080/M | — | — |
Frequently asked questions
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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
80% less to send — works with any model