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Claude 3 5 Sonnet vs Glm 5p2
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.
Glm 5p2 has about 5.2× the context window of the other in this pair.
Glm 5p2 has 424% more context capacity (1048K vs 200K tokens). Glm 5p2 is 53% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Glm 5p2. Its 1048K context fits entire documents without chunking (vs 200K).
RAG / high-volume retrieval
Use Glm 5p2. Input tokens are 53% 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 | Glm 5p2 |
|---|---|---|
| Context window | 200,000 tokens (200K) | 1,048,576 tokens (1048K) |
| Max output tokens | N/A | 131,072 tokens (131K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | Yes | 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 | Glm 5p2 in | Glm 5p2 out |
|---|---|---|---|---|
| Fireworks | — | — | $1.40/M | $4.40/M |
| Google Vertex | $3.00/M | $15.00/M | — | — |
| Gradient | $3.00/M | $15.00/M | — | — |
| Openrouter | $3.00/M | $15.00/M | — | — |
| Replicate | $3.75/M | $18.75/M | — | — |
| Snowflake | $3.00/M | $15.00/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