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 | MiMo-V2-Flashmimo-v2-flash | Gemini 3.5 Transcribegemini-3.5-transcribe | GPT-6 Sol Progpt-6-sol-pro |
|---|---|---|---|
| Pricing | |||
| Input | $0.09 / 1M | — Not priced per input token | $2.00 / 1M |
| Output | $0.29 / 1M | — Not priced per output token | $10.00 / 1M |
| Cache Write (5m) | $0.09 / 1M | Not applicable | $2.00 / 1M |
| Cache Write (1h) | $0.09 / 1M | Not applicable | $2.00 / 1M |
| Cache Read | $0.09 / 1M | Not applicable | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 262.1K | 98.3K | 1.1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | Xiaomi | OpenAI | |
| Category | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| Description | |||
| Summary | MiMo-V2-Flash is an open-source Mixture-of-Experts (MoE) foundation model developed by Xiaomi, featuring 309B total parameters with 15B activated per token and a hybrid attention architecture. It supports a 256K context window and a hybrid thinking mode toggle, enabling flexible trade-offs between speed and reasoning depth. The model excels in reasoning, coding, and agentic workflows, ranking #1 globally among open-source models on benchmarks such as SWE-bench Verified and SWE-bench Multilingual. With performance comparable to leading proprietary models like Claude Sonnet 4.5 at a fraction of the cost, MiMo-V2-Flash is well suited for efficient, high-performance deployments. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | GPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost. |