GPT-6, as a hypothetical model, aims to provide significant improvements over previous models in terms of context window size, reasoning ability, coding proficiency, and reduced hallucination rates. However, it also comes with higher memory and storage requirements. Inflection AI Pi 3 is an existing model with multilingual support, customizable finetuning, and safety measures, but it has a limited input size and community support is not yet available. The choice between the two depends on the specific needs and priorities of the user, considering the speculative nature of GPT-6's capabilities.
| Attribute | Inflection AI Pi 3 | GPT-6 |
|---|---|---|
| Context window length (tokens) | 8K tokens (limited to 4000 characters input). Older versions had 1000 tokens. | Likely to be significantly larger than previous models, potentially in the range of 200K to 1M tokens or more. |
| Finetuning capabilities and cost | Proprietary fine-tuning system (reinforcement learning from employee feedback). Customizable pricing based on business needs. | Expected to offer robust finetuning options. The cost will depend on the amount of data and training time. |
| Multilingual support (languages and performance) | English, Spanish, French, German, Italian, and Portuguese. | Should support a wide range of languages with improved accuracy and fluency compared to earlier models. |
| API availability and pricing | Commercial API available. Pi and Productivity models: $2.50 per 1M input tokens, $10 per 1M output tokens. | An API would likely be available with tiered pricing based on usage, with costs potentially higher for finetuned models. |
| Hallucination rate (assessed on benchmark datasets) | Inflection-2.5 performs at more than 94% of the average performance of GPT-4. Pi should avoid hallucinations. | Aiming for a lower hallucination rate than previous models through improved training data and techniques. |
| Reasoning ability (measured by complex tasks) | Gemini 2.5 Pro Experimental is capable of reasoning over complex problems in code, math, and STEM, as well as analyzing large datasets, codebases, and documents using long context. | Enhanced performance on complex reasoning tasks, including logic puzzles and nuanced questions. |
| Coding proficiency (languages and benchmark scores) | Inflection-2.5 more than doubled the score of its predecessor in a test that comprised coding tasks. | Support for multiple programming languages with high benchmark scores on coding tasks. |
| Safety measures and content moderation policies | Strict internal controls over user data. Technical measures to protect personal information. Should not be used for harmful, abusive, or illegal topics. | Robust measures to prevent the generation of harmful or biased content, including content filtering and monitoring. |
| Customization options and tools | Builds and fine-tunes AI models tailored to specific organizational needs. | Tools for prompt engineering, parameter adjustments, and the creation of custom GPTs. |
| Inference speed (tokens per second) | Inflection 3 Pi: 40.70tps. Inflection 3 Productivity: 47.17tps. Llama 4 Maverick model on NVIDIA: >1,000 TPS per user. | Faster inference speeds (tokens per second) compared to previous generations, potentially varying based on hardware configuration. |
| Memory and storage requirements | Not available | Substantial memory and storage would be needed, requiring high-end CPUs, GPUs, and large amounts of RAM. |
| Community support and documentation quality | Check back soon for updates. | Comprehensive documentation and community support resources would be expected. |