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Gpt 4o Realtime Preview vs O1 Mini
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.
Model
Gpt 4o Realtime Preview
Context window
128K
128,000 tokens · ~96K words
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.
Same context window size for both models.
Gpt 4o Realtime Preview and O1 Mini have identical context windows (128K tokens). O1 Mini is 78% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use O1 Mini. Input tokens are 78% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use O1 Mini. Its 65K 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 | Gpt 4o Realtime Preview | O1 Mini |
|---|---|---|
| Context window | 128,000 tokens (128K) | 128,000 tokens (128K) |
| Max output tokens | 4,096 tokens (4K) | 65,536 tokens (65K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | Yes |
| Prompt caching | Yes | Yes |
| Batch API | Yes | Yes |
| 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 | Gpt 4o Realtime Preview in | Gpt 4o Realtime Preview out | O1 Mini in | O1 Mini out |
|---|---|---|---|---|
| Azure | — | — | $1.21/M | $4.84/M |
| Openai | $5.00/M | $20.00/M | — | — |
| Replicate | — | — | $1.10/M | $4.40/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