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GLM 5V Turbo vs Switchyard
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
GLM 5V Turbo
Context window
203K
202,752 tokens · ~152K 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.
Switchyard has about 4.9× the context window of the other in this pair.
Switchyard has 393% more context capacity (1000K vs 202K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Switchyard. Its 1000K context fits entire documents without chunking (vs 202K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | GLM 5V Turbo | Switchyard |
|---|---|---|
| Context window | 202,752 tokens (202K) | 1,000,000 tokens (1000K) |
| Max output tokens | 131,072 tokens (131K) | N/A |
| Speed tier | Balanced | Balanced |
| Vision | Yes | No |
| Function calling | Yes | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Apr 2026 | Sep 2026 |
Frequently asked questions
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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