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 3.8 Flashgemini-3.8-flash | GPT-4o Mini Transcribegpt-4o-mini-transcribe | GLM 5.3glm-5.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.75 / 1M | $0.625 / 1M | $1.40 / 1M |
| Output | $3.75 / 1M | $0.625 / 1M | $4.40 / 1M |
| Cache Write (5m) | $0.75 / 1M | Not applicable | $1.40 / 1M |
| Cache Write (1h) | $0.75 / 1M | Not applicable | $1.40 / 1M |
| Cache Read | $0.75 / 1M | Not applicable | $1.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 128K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | No |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | OpenAI | Z.AI | |
| Category | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments. | 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. |