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 Sol Progpt-6-sol-pro | Perceptron Mk1.5perceptron-mk1.5 | Gemini Embedding 2gemini-embedding-2-preview |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | $0.15 / 1M | $0.60 / 1M |
| Output | $10.00 / 1M | $1.50 / 1M | $2.40 / 1M |
| Cache Write (5m) | $2.00 / 1M | $0.15 / 1M | $0.60 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.15 / 1M | $0.60 / 1M |
| Cache Read | $2.00 / 1M | $0.15 / 1M | $0.60 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1.1M | 36.9K | 8.2K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | No |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | OpenAI | Perceptron | |
| Category | chat | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| Description | |||
| Summary | 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. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications. |