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Glm 4 5 Air Fp8 vs Google Gemma 3 27b It
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.
Same context window size for both models.
Glm 4 5 Air Fp8 and Google Gemma 3 27b It have identical context windows (128K tokens). Glm 4 5 Air Fp8 is 13% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Glm 4 5 Air Fp8. Input tokens are 13% 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 4 5 Air Fp8 | Google Gemma 3 27b It |
|---|---|---|
| Context window | 128,000 tokens (128K) | 128,000 tokens (128K) |
| Max output tokens | N/A | 8,192 tokens (8K) |
| Speed tier | Balanced | Fast |
| Vision | No | Yes |
| Function calling | Yes | No |
| Extended thinking | No | No |
| Prompt caching | No | 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 4 5 Air Fp8 in | Glm 4 5 Air Fp8 out | Google Gemma 3 27b It in | Google Gemma 3 27b It out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.230/M | $0.380/M |
| Together Ai | $0.200/M | $1.10/M | — | — |
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