Google has launched its “most capable AI model” this week adding a crucial chapter to the Gen AI Battle against OpenAI’s Popular ChatGPT4.
Google claims Gemini is capable of generalizing and combining many forms of information, such as text, code, audio, images, and videos, with ease.
Gemini is Google’s most flexible model and can operate well on a wide range of platforms, including mobile phones and data centers. Its cutting-edge features will greatly improve how developers and business clients use AI to create and grow.
Are you ready for Gemini vs ChatGPT4?
Gemini's 3 Sizes
- Gemini Ultra: It is largest and most powerful model for really difficult tasks.
- Gemini Pro: It is optimal model for scaling over a variety of jobs.
- Gemini Nano: It is most efficient model for on-device activities.
Capabilities Comparison
Lets compare Gemini’s capabilities to understand text, image, audio and video on different benchmarks.
Text
| Capability | Benchmark | Description(Score Higher is Better) | Gemini Ultra | Gemini Pro | ChatGPT-4 | ChatGPT-3.5 |
|---|---|---|---|---|---|---|
| General | MMLU | Representation of questions in 57 subjects | 90.04% | 79.13% | 87.29% | 70% |
| Reasoning | Big-Bench Hard | Diverse set of challenging tasks requiring multi-step reasoning | 83.6% | 75.0% | 83.1% | 66.6% |
| DROP | Reading comprehension | 82.4 | 74.1 | 80.9 | 64.1 | |
| HellaSwag | Commonsense reasoning for everyday tasks | 87.8% | 84.7% | 95.3% | 85.5% | |
| Math | GSM8K | Basic arithmetic manipulations (incl. Grade School math problems) | 94.4% | 86.5% | 92.0% | 57.1% |
| MATH | Challenging math problems (incl. algebra, geometry, pre-calculus, and others) | 53.2% | 32.6% | 52.9% | 34.1% | |
| Code | HumanEval | Python code generation | 74.4% | 67.7% | 67.0% | 48.1% |
Reference: Google DeepMind
Multimodal
| Capability | Benchmark | Description(Score Higher is Better) | Gemini Ultra | Gemini Pro | Gemini Nano | ChatGPT-4 |
|---|---|---|---|---|---|---|
| Image | MMMU | Multi-discipline college-level reasoning problems | 59.4% | 47.9% | 32,6% | 56.8% |
| VQAv2 | Natural image understanding | 77.8% | 77.8% | 67.5 | 77.2% | |
| TextVQA | OCR on natural images | 82.3% | 74.6% | 65.9% | 78.0% | |
| DocVQA | Document understanding | 90.9% | 88.1% | 74.3% | 88.4% | |
| Infographic VQA | Infographic understanding | 80.3% | 75.2% | 54.5% | 75.1% | |
| MathVista | Mathematical reasoning in visual contexts | 53.0% | 45.2% | 30.6% | 49.9% | |
| Video | VATEX | English video captioning | 62.7 | 57.4 | - | 56.0 |
| Perception Test MCQA | Video question answering | 54.7% | 51.1% | - | 46.3% | |
| Audio | CoVoST 2 (21 languages) | Automatic speech translation | 40.1 | 40.1 | 35.4 | 29.1 |
| FLEURS (62 languages) | Automatic speech recognition | 7.6% | 7.6% | 14.2% | 17.6% |
Reference: Google DeepMind
Reliability, Scalability & Efficiency
Gemini is trained on Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e. Google created Gemini to be the most dependable, scalable, and effective model to train and service.
Compared to previous, smaller, and less powerful devices, Gemini operates noticeably faster on TPUs.
Responsibility and Safety
Regarding bias and toxicity, Gemini features the most thorough safety assessments of any Google AI model to date.
Google developed specialized safety classifiers to recognize, categorize, and remove information that contains harmful stereotypes or violent content, for example, in order to minimize harm. This multi-layered strategy is intended to make Gemini safer and more inclusive for all users when combined with strong filtering.
Conclusion
By looking at various benchmark scores it seems like Gemini has beaten ChatGPT4 in text, image, audio and video capabilities.
Google already started rolling out Gemini in its products and platforms.
So we need to wait until people start using Gemini personally and based on user feedback only we can conclude if Gemini is better than ChatGPT4.