Compare
Anthropic Claude vs Gpt 5 1 Chat Latest
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.
Gpt 5 1 Chat Latest has about 1.3× the context window of the other in this pair.
Gpt 5 1 Chat Latest has 28% more context capacity (128K vs 100K tokens). Gpt 5 1 Chat Latest is 84% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Gpt 5 1 Chat Latest. Its 128K context fits entire documents without chunking (vs 100K).
RAG / high-volume retrieval
Use Gpt 5 1 Chat Latest. Input tokens are 84% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Gpt 5 1 Chat Latest. Its 16K 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 | Anthropic Claude | Gpt 5 1 Chat Latest |
|---|---|---|
| Context window | 100,000 tokens (100K) | 128,000 tokens (128K) |
| Max output tokens | 8,191 tokens (8K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Balanced |
| Vision | No | Yes |
| Function calling | No | No |
| Extended thinking | No | Yes |
| Prompt caching | No | Yes |
| Batch API | Yes | 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 | Anthropic Claude in | Anthropic Claude out | Gpt 5 1 Chat Latest in | Gpt 5 1 Chat Latest out |
|---|---|---|---|---|
| Aws Bedrock | $8.00/M | $24.00/M | — | — |
| Openai | — | — | $1.25/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