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Glm 5p2 vs Kimi K2 0711
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 8× the context window of the other in this pair.
Glm 5p2 has 700% more context capacity (1048K vs 131K tokens). Kimi K2 0711 is 64% 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 131K).
RAG / high-volume retrieval
Use Kimi K2 0711. Input tokens are 64% 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 | Glm 5p2 | Kimi K2 0711 |
|---|---|---|
| Context window | 1,048,576 tokens (1048K) | 131,072 tokens (131K) |
| Max output tokens | 131,072 tokens (131K) | 131,072 tokens (131K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | N/A | Jul 2025 |
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 | Glm 5p2 in | Glm 5p2 out | Kimi K2 0711 in | Kimi K2 0711 out |
|---|---|---|---|---|
| Deepinfra | — | — | $0.500/M | $2.00/M |
| Fireworks | $1.40/M | $4.40/M | $0.600/M | $2.50/M |
| Hyperbolic | — | — | $2.00/M | $2.00/M |
| Novita | — | — | $0.570/M | $2.30/M |
| Together Ai | — | — | $1.00/M | $3.00/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