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Codellama 34b Instruct vs o3
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.
o3 has about 48.8× the context window of the other in this pair.
o3 has 4782% more context capacity (200K vs 4K tokens). Codellama 34b Instruct is 50% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use o3. Its 200K context fits entire documents without chunking (vs 4K).
RAG / high-volume retrieval
Use Codellama 34b Instruct. Input tokens are 50% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use o3. Its 100K 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 34b Instruct | o3 |
|---|---|---|
| Context window | 4,096 tokens (4K) | 200,000 tokens (200K) |
| Max output tokens | 4,096 tokens (4K) | 100,000 tokens (100K) |
| Speed tier | Balanced | Deep |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | No | Yes |
| Release date | N/A | Apr 2025 |
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 34b Instruct in | Codellama 34b Instruct out | o3 in | o3 out |
|---|---|---|---|---|
| Anyscale | $1.00/M | $1.00/M | — | — |
| Azure | — | — | $2.00/M | $8.00/M |
| Openai | — | — | $2.00/M | $8.00/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