Compare
Claude 3 5 Sonnet 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.
Claude 3 5 Sonnet has about 1.6× the context window of the other in this pair.
Claude 3 5 Sonnet has 56% more context capacity (200K vs 128K tokens). Gpt 5 1 Chat Latest is 58% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Claude 3 5 Sonnet. Its 200K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Gpt 5 1 Chat Latest. Input tokens are 58% 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 | Claude 3 5 Sonnet | Gpt 5 1 Chat Latest |
|---|---|---|
| Context window | 200,000 tokens (200K) | 128,000 tokens (128K) |
| Max output tokens | N/A | 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 | Claude 3 5 Sonnet in | Claude 3 5 Sonnet out | Gpt 5 1 Chat Latest in | Gpt 5 1 Chat Latest out |
|---|---|---|---|---|
| Google Vertex | $3.00/M | $15.00/M | — | — |
| Gradient | $3.00/M | $15.00/M | — | — |
| Openai | — | — | $1.25/M | $10.00/M |
| Openrouter | $3.00/M | $15.00/M | — | — |
| Replicate | $3.75/M | $18.75/M | — | — |
| Snowflake | — | — | — | — |
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