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R1 0528 vs Gpt 4o Search Preview 2025 03 11
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 Search Preview 2025 03 11
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.
R1 0528 has about 1.3× the context window of the other in this pair.
R1 0528 has 28% more context capacity (163K vs 128K tokens). R1 0528 is 90% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use R1 0528. Its 163K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use R1 0528. Input tokens are 90% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use R1 0528. Its 163K 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 | R1 0528 | Gpt 4o Search Preview 2025 03 11 |
|---|---|---|
| Context window | 163,840 tokens (163K) | 128,000 tokens (128K) |
| Max output tokens | 163,840 tokens (163K) | 16,384 tokens (16K) |
| Speed tier | Deep | Balanced |
| Vision | No | Yes |
| Function calling | Yes | Yes |
| Extended thinking | Yes | No |
| Prompt caching | Yes | Yes |
| Batch API | No | Yes |
| Release date | May 2025 | 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 | R1 0528 in | R1 0528 out | Gpt 4o Search Preview 2025 03 11 in | Gpt 4o Search Preview 2025 03 11 out |
|---|---|---|---|---|
| Deepinfra | $0.500/M | $2.15/M | — | — |
| Fireworks | $3.00/M | $8.00/M | — | — |
| Hyperbolic | $0.250/M | $0.250/M | — | — |
| Lambda | $0.200/M | $0.600/M | — | — |
| Nebius | $0.800/M | $2.40/M | — | — |
| Novita | $0.700/M | $2.50/M | — | — |
| Openai | — | — | $2.50/M | $10.00/M |
| Openrouter | $0.500/M | $2.15/M | — | — |
Frequently asked questions
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Use a smaller model.
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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