Compare
Glm 5p1 Fast vs Qwen2p5 32b
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 5p1 Fast has about 1.5× the context window of the other in this pair.
Glm 5p1 Fast has 54% more context capacity (202K vs 131K tokens). Qwen2p5 32b is 67% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Glm 5p1 Fast. Its 202K context fits entire documents without chunking (vs 131K).
RAG / high-volume retrieval
Use Qwen2p5 32b. Input tokens are 67% 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 5p1 Fast | Qwen2p5 32b |
|---|---|---|
| Context window | 202,800 tokens (202K) | 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 | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | 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 | Glm 5p1 Fast in | Glm 5p1 Fast out | Qwen2p5 32b in | Qwen2p5 32b out |
|---|---|---|---|---|
| Fireworks | $2.80/M | $8.80/M | $0.900/M | $0.900/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