Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | Gemini 3.1 Flash TTS Previewgemini-3.1-flash-tts-preview | GLM 5.3glm-5.3 |
|---|---|---|
| Pricing | ||
| Input | $27.50 / 1M | $1.40 / 1M |
| Output | $0 / 1M | $4.40 / 1M |
| Cache Write (5m) | Not applicable | $1.40 / 1M |
| Cache Write (1h) | Not applicable | $1.40 / 1M |
| Cache Read | Not applicable | $1.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 8.2K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | No | No |
| Function Calling | No | Yes |
| JSON Mode | Yes | Yes |
| Streaming | No | Yes |
| Catalogue | ||
| Provider | Z.AI | |
| Category | voice | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Gemini 3.1 Flash TTS Preview is Google's next-generation text-to-speech model, delivering a major upgrade over Gemini 2.5 Flash TTS. It converts text into natural audio across 70+ languages, with significantly expanded language coverage and improved quality. The model introduces 200+ inline audio control tags (e.g., [whispers], [laughs], [excited]) for fine-grained control over emotion, tone, and pacing, along with support for two speakers with independent voice and style settings. It outputs 24 kHz / 16-bit PCM audio, includes SynthID watermarking, and supports a 32K token context window. Designed for expressive and controllable voice generation, it is well suited for dialogue systems, storytelling, character-driven content, and advanced audio production workflows. | GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution. |