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GPT-5.2 Pro vs Jp Anthropic Claude Opus 4 7
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-5.2 Pro
Context window
272K
272,000 tokens · ~204K words
Model
Jp Anthropic Claude Opus 4 7
Context window
1M
1,000,000 tokens · ~750K 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.
Jp Anthropic Claude Opus 4 7 has about 3.7× the context window of the other in this pair.
Jp Anthropic Claude Opus 4 7 has 267% more context capacity (1000K vs 272K tokens). Jp Anthropic Claude Opus 4 7 is 73% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Jp Anthropic Claude Opus 4 7. Its 1000K context fits entire documents without chunking (vs 272K).
RAG / high-volume retrieval
Use Jp Anthropic Claude Opus 4 7. Input tokens are 73% 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 | GPT-5.2 Pro | Jp Anthropic Claude Opus 4 7 |
|---|---|---|
| Context window | 272,000 tokens (272K) | 1,000,000 tokens (1000K) |
| Max output tokens | 128,000 tokens (128K) | 128,000 tokens (128K) |
| Speed tier | Balanced | Deep |
| Vision | Yes | Yes |
| Function calling | Yes | Yes |
| Extended thinking | Yes | Yes |
| Prompt caching | No | Yes |
| Batch API | No | Yes |
| Release date | Dec 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 | GPT-5.2 Pro in | GPT-5.2 Pro out | Jp Anthropic Claude Opus 4 7 in | Jp Anthropic Claude Opus 4 7 out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $5.50/M | $27.50/M |
| Openrouter | $21.00/M | $168.00/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