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 | GPT-6 Solgpt-6-sol | MiniMax M2minimax-m2 | Gemini 3.5 Transcribegemini-3.5-transcribe |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | $0.15 / 1M | — Not priced per input token |
| Output | $10.00 / 1M | $0.45 / 1M | — Not priced per output token |
| Cache Write (5m) | $2.00 / 1M | $0.15 / 1M | Not applicable |
| Cache Write (1h) | $2.00 / 1M | $0.15 / 1M | Not applicable |
| Cache Read | $2.00 / 1M | $0.15 / 1M | Not applicable |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1.1M | 196.6K | 98.3K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | OpenAI | MiniMax | |
| Category | chat | chat | voice |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-25 |
| Description | |||
| Summary | GPT-6 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applications | MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. |