Compare
Anthropic Claude vs GPT-4 Turbo Preview
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-4 Turbo 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.
GPT-4 Turbo Preview has about 1.3× the context window of the other in this pair.
GPT-4 Turbo Preview has 28% more context capacity (128K vs 100K tokens). Anthropic Claude is 20% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use GPT-4 Turbo Preview. Its 128K context fits entire documents without chunking (vs 100K).
RAG / high-volume retrieval
Use Anthropic Claude. Input tokens are 20% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Anthropic Claude. Its 8K 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-4 Turbo Preview |
|---|---|---|
| Context window | 100,000 tokens (100K) | 128,000 tokens (128K) |
| Max output tokens | 8,191 tokens (8K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | Yes | Yes |
| Release date | N/A | Jan 2024 |
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-4 Turbo Preview in | GPT-4 Turbo Preview out |
|---|---|---|---|---|
| Aws Bedrock | $8.00/M | $24.00/M | — | — |
| Openai | — | — | $10.00/M | $30.00/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