# Gemini Omni 1.2 Flash vs GLM 5.2

As of November 12, 2026, Gemini Omni 1.2 Flash costs 7% more per input token than GLM 5.2 ($1.50 vs $1.40 per 1M) and has a 128K-token context window versus 1M.

- Page: https://models.litellm.ai/compare/gemini-omni-1.2-flash-vs-glm-5.2
- Gemini Omni 1.2 Flash: https://models.litellm.ai/models/gemini-omni-1.2-flash
- GLM 5.2: https://models.litellm.ai/models/glm-5.2

## Side by side

| | Gemini Omni 1.2 Flash | GLM 5.2 |
|---|---|---|
| Input, per 1M tokens | $1.50 | $1.40 |
| Output, per 1M tokens | $9.00 | $4.40 |
| Cache read, per 1M tokens | – | $0.26 |
| Cache write, per 1M tokens | – | $0 |
| Context window | 131,072 tokens | 1,000,000 tokens |
| Max output | 65,536 tokens | 128,000 tokens |
| Image input | Yes | Yes |
| Tool calling | – | Yes |
| Structured output (JSON) | – | Yes |
| Prompt caching | – | Yes |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 2 | 27 |
| Price shown is from | Google AI Studio | Z.ai |
| Added to LiteLLM | November 5, 2026 | June 2026 |

Headline prices in USD per 1M tokens, from LiteLLM's model price file.

## Cheapest provider for each

- Gemini Omni 1.2 Flash: Google AI Studio at $1.50 input per 1M tokens (`gemini/gemini-omni-1.2-flash`)
- GLM 5.2: CLF AI Gateway at $0.56 input per 1M tokens (`clf_ai_gateway/glm-5.2`)

## When to pick which

- GLM 5.2: Costs 7% less per input token: $1.40 vs $1.50 per 1M.
- GLM 5.2: Larger context window: 1,000,000 vs 131,072 tokens.

## FAQ

### Which is cheaper, Gemini Omni 1.2 Flash or GLM 5.2?

GLM 5.2 is cheaper at 3 input tokens per output token: $2.15 per 1M tokens blended ($1.40 input, $4.40 output), versus $3.375 ($1.50 and $9.00) for Gemini Omni 1.2 Flash, as of November 12, 2026.

### Which has the larger context window, Gemini Omni 1.2 Flash or GLM 5.2?

GLM 5.2 has the larger context window: 1,000,000 tokens, versus 131,072 for Gemini Omni 1.2 Flash.

### Can I switch from Gemini Omni 1.2 Flash to GLM 5.2 without changing my code?

Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "gemini/gemini-omni-1.2-flash" to "zai/glm-5.2", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.