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Codellama 70b Instruct 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 31.3× the context window of the other in this pair.
Writer Palmyra X4 has 3025% more context capacity (128K vs 4K tokens). Codellama 70b Instruct is 60% 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 4K).
RAG / high-volume retrieval
Use Codellama 70b Instruct. Input tokens are 60% 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.
| Spec | Codellama 70b Instruct | Writer Palmyra X4 |
|---|---|---|
| Context window | 4,096 tokens (4K) | 128,000 tokens (128K) |
| Max output tokens | 4,096 tokens (4K) | 8,192 tokens (8K) |
| Speed tier | Deep | 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 | Codellama 70b Instruct in | Codellama 70b Instruct out | Writer Palmyra X4 in | Writer Palmyra X4 out |
|---|---|---|---|---|
| Anyscale | $1.00/M | $1.00/M | — | — |
| Aws Bedrock | — | — | $2.50/M | $10.00/M |
Frequently asked questions
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
80% less to send — works with any model