OpenAI vs Google AI, head-to-head. 61 GPT models against 27 Gemini models compared on composite scores, pricing, context windows, and capabilities. Scores updated hourly from live data.
88 models from OpenAI and Google, sorted by composite score
| Dimension | OpenAI (GPT) | Google (Gemini) |
|---|---|---|
| Total Models | 61 | 27 |
| Top Score | 91 | 68 |
| Average Score | 56 | 54 |
| Max Context Window | 1.1M | 1.0M |
| Input Price Range | $0.030 - $150.00 | $0.020 - $2.00 |
| Output Price Range | $0.140 - $600.00 | $0.040 - $12.00 |
| Vision Models | 40 / 61 | 22 / 27 |
| Reasoning Models | 30 / 61 | 13 / 27 |
| Function Calling | 54 / 61 | 15 / 27 |
| JSON Mode | 59 / 61 | 24 / 27 |
| Web Search | 29 / 61 | 0 / 27 |
| Free Models | 2 | 5 |
| Open Source | 21 | 12 |
The rivalry between OpenAI's GPT family and Google's Gemini lineup is one of the most consequential in AI. Both providers maintain large model portfolios covering everything from lightweight inference to frontier-class reasoning.
Based on our composite scoring system (which weighs capability breadth, pricing efficiency, context capacity, recency, output limits, and versatility), OpenAI currently holds the edge with a higher top model score (91 vs 68). However, average score across the full lineup tells a different story: OpenAI averages 56 while Google averages 54.
On pricing, Google generally offers more aggressive free-tier options (5 free models vs 2 from OpenAI). For output tokens, OpenAI ranges from $0.140 to $600.00 per million, while Google ranges from $0.040 to $12.00 per million.
On context windows, Google leads with a maximum of 1.0M tokens versus OpenAI's 1.1M. Larger context windows are critical for tasks like document analysis, long-form content generation, and codebase understanding.
Bottom line: choose GPT models if you need the widest ecosystem integration and the most mature API tooling. Choose Gemini if you value larger context windows, competitive free tiers, or tight integration with Google Cloud services. Use the head-to-head comparisons above to match specific models to your workload.