Both Gemini and Meta AI offer powerful multimodal capabilities and extensive integration options. Meta AI stands out with its larger context window and competitive pricing, while Gemini benefits from its deep integration with the Google ecosystem. The choice depends on specific needs, such as context length requirements, integration preferences, and budget considerations.
| Attribute | Gemini | Meta AI |
|---|---|---|
| Model Size and Parameter Count | Gemini 1.0 comes in three sizes: Ultra (largest, most capable), Pro, and Nano (for on-device tasks). Exact parameter count isn't publicly disclosed. | Meta AI models range from 1 billion to 2 trillion parameters. LLaMA 3.1 boasts 405 billion parameters. Llama 4 Scout has 17 billion active parameters (109 billion total), Llama 4 Maverick has 17 billion active parameters (400 billion total), and Llama 4 Behemoth has 288 billion active parameters (2 trillion total). |
| Training Data Sources and Volume | Pre-trained on a variety of modalities (text, code, audio, image, and video). Google applies quality filters to the datasets used. | Trained on publicly available information. LLaMA 65B and LLaMA 33B were trained on 1.4 trillion tokens, LLaMA 7B was trained on one trillion tokens, and Llama 3 models were pre-trained on approximately 15 trillion tokens. Training data includes publicly available online information, licensed data, and data from Meta's products and services. |
| Multimodal Input Capabilities | Natively multimodal, meaning it can understand, operate across, and combine different types of information, including text, code, audio, images, and video. | Meta AI develops multimodal AI systems that process text, images, audio, and video. Examples include FLAVA and ImageBind. ImageBind can process information across six data modalities: text, image, video, thermal imagery, depth information, and audio. Seamless M4T is designed for translation and transcription across nearly 100 languages using both text and voice inputs. Llama 4 is natively multimodal. |
| Context Window Length | Gemini 1.5 Pro has a standard context window of 128,000 tokens. A limited preview of Gemini 1.5 Pro was available with a context window of up to 1 million tokens. Gemini 2.5 Flash-Lite has a 1 million token context window. Gemini 2.5 Pro ships with a 1 million token context window (2 million coming soon). | Llama 4 Scout has a 10 million token context window. Llama 4 Maverick offers a 1 million token context window. Llama 3.1 has a context length of 128,000 tokens. |
| Fine-tuning and Customization Options | Prompt engineering is a starting point for customization. Google AI Studio allows for prompt tuning using input-output pairs. Vertex AI offers full fine-tuning capabilities, allowing you to adjust model weights using large, high-quality datasets. This is a paid service. | Fine-tuning is available as a limited preview. You can customize hyperparameters like epochs and batch size. Fine-tuning improves performance for specific use cases by training on specialized datasets. It helps adapt general-purpose models to industry-specific or task-specific needs. Fine-tuning can optimize a smaller model to match the performance of a larger model. |
| API Availability and Pricing | Gemini Pro is accessible through the Gemini API in Google AI Studio or Google Cloud Vertex AI. Pricing tiers are planned, starting with a standard context window and scaling up to 1 million tokens. | Meta AI API balances open innovation with sustainable business models. Meta offers pathways for developers to explore AI capabilities without immediate financial commitment, such as a Developer Sandbox. Meta's open-source approach with Llama 3 offers competitive rates, especially for developers and smaller organizations. Approximate token cost for Meta (Llama 3) is $0.10-$0.50 per 1M tokens. |
| Integration with Existing Platforms and Services | Integrates with Google products like Bard, Search, Ads, Chrome, and Duet AI. It also integrates with Google Cloud Vertex AI. Gemini AI integrates across Google's ecosystem, from search to mobile devices. It enhances applications like Gmail, Google Docs, and Google Maps. It also integrates with development tools like VS Code and JetBrains. | Meta AI integrates with platforms like WhatsApp, Messenger, Instagram, and the web. It is available on Cloudflare Workers AI platform. Meta AI models can be fine-tuned in Azure AI Foundry. BentoML supports a wide range of data types, such as text, images, audios and documents. You can easily integrate it with your existing ML workflow without a custom pipeline for handling multimodal inputs. |
| Real-time Processing Speed and Latency | Optimized to run efficiently on various platforms, from data centers to mobile devices. Gemini 2.0 Flash is designed for speed and low latency. Gemini 2.5 Flash-Lite excels at high-volume, latency-sensitive tasks. | Llama 4 Scout is optimized for inference speed. Meta AI uses a mixture-of-experts (MoE) architecture to intelligently use only a fraction of parameters during active inference, delivering faster responses. Llama 3 70B has a lower latency compared to average, taking 0.42s to receive the first token (TTFT). |
| Hallucination Rate and Factuality | Gemini Apps can generate plausible-sounding responses that may contain factual errors. Gemini includes a 'Double-check' feature that uses Google Search to verify information in its responses. Google recommends validating all output from Gemini for Google Cloud products. | The text generated by AI may not be relevant, accurate, or appropriate. Models lack the ability to verify the accuracy or reliability of the outputs they produce. Carefully review responses for accuracy. |
| Bias and Fairness Mitigation Strategies | Google implements strategies to minimize bias and enhance diversity and fairness in its responses. These strategies include pre-processing, in-training, intra-processing, and post-processing techniques. Bias mitigation initiatives are prioritized, with a focus on ethical and responsible AI practices. | Meta focuses on building fair AI systems through diverse datasets. They use tools like Fairness Flow, which evaluates fairness during development. Meta uses data augmentation to ensure diverse training datasets. They conduct regular algorithmic audits to assess performance across different demographic groups. Meta has systems and policies to detect and remove harmful content, such as hate speech. |
| Security and Privacy Measures | Gemini Code Assist conforms to Google's privacy commitment with generative AI technologies. Google doesn't use your data to train models without permission. Gemini brings enterprise-grade security and applies existing organizational controls and data handling practices. User activity is set to auto-delete after 18 months by default, but this can be adjusted. | Meta AI prioritizes privacy and ethical considerations. They implement data encryption, security measures, and access controls. Meta AI gives users control over their data and its usage. They provide opt-out options for data collection and processing. Meta AI uses encryption to protect user data both in transit and at rest. Role-based access control ensures that only authorized personnel can access user data. Secure authentication protocols, like multi-factor authentication, protect user accounts. |
| Community Support and Documentation | Google Cloud provides extensive and user-friendly API documentation. Support channels include help centers and community forums. | Meta shares datasets with the research community. They offer tools to help measure fairness and mitigate potential bias in NLP models. Meta has a Llama Defenders Program, making tools available to help organizations detect threats such as scams and phishing attempts. |
| Price | Pricing tiers are planned, starting at a standard context window and scaling up to 1 million tokens. | Not available |
| Ratings | Gemini Ultra surpasses state-of-the-art performance on a range of benchmarks, including text, coding, and multimodal tasks. Gemini 2.0 Pro achieves high accuracy rates on reasoning tasks compared to GPT-4.0. Gemini 2.5 Pro is state-of-the-art across a range of benchmarks requiring advanced reasoning. | Not available |