GPT-4o vs Gemini 1.5 Flash: AI Model Comparison
Explore the key differences between OpenAI and Google's latest language models
GPT-4o
: specialties & advantages
GPT-4o is OpenAI's high-intelligence flagship model, designed for complex, multi-step tasks. It offers advanced capabilities and improved performance over previous models.
Key strengths include:
- Multimodal capabilities (text and vision)
- High intelligence and advanced reasoning abilities
- Superior performance across non-English languages
- Faster text generation (2x faster than GPT-4 Turbo)
- Improved efficiency and lower cost compared to GPT-4 Turbo
- Large context window of 128K tokens
GPT-4o is particularly well-suited for applications requiring sophisticated analysis, creative problem-solving, and handling of complex information across multiple modalities.
Best use cases for
GPT-4o
Here are examples of ways to take advantage of its greatest stengths:
Complex Data Analysis
GPT-4o's advanced reasoning capabilities make it ideal for analyzing complex datasets and providing in-depth insights across various domains.
Multilingual and Multimodal Applications
With superior performance in non-English languages and multimodal inputs, GPT-4o excels in applications requiring diverse language processing and image understanding.
High-Stakes Decision Support
GPT-4o's high intelligence and advanced reasoning make it suitable for supporting critical decision-making processes in fields like finance, healthcare, and strategic planning.
Gemini 1.5 Flash
: specialties & advantages
Gemini 1.5 Flash is Google's advanced language model, designed for fast performance and improved capabilities. It offers significant improvements over previous models at a competitive price point.
Key strengths include:
- Multimodal capabilities (text and vision)
- Massive context window of 1 million tokens
- Advanced reasoning and problem-solving abilities
- Extremely fast output speed
- Support for over 100 languages
- Knowledge cutoff up to November 2023
Gemini 1.5 Flash is particularly well-suited for applications requiring a balance between advanced capabilities, speed, and cost-effectiveness.
Best use cases for
Gemini 1.5 Flash
On the other hand, here's what you can build with this LLM:
Large-Scale Information Processing
Gemini 1.5 Flash's massive context window makes it ideal for analyzing and processing large volumes of data, such as entire codebases or extensive documents.
Real-Time AI Applications
With its extremely fast output speed, Gemini 1.5 Flash excels in real-time applications like live customer support, instant content generation, and rapid data analysis.
Multilingual and Multimodal Tasks
Supporting over 100 languages and having multimodal capabilities, Gemini 1.5 Flash is perfect for diverse applications requiring language understanding and visual processing.
In summary
When comparing GPT-4o and Gemini 1.5 Flash, several key differences emerge:
- Context Window: Gemini 1.5 Flash offers a much larger context window (1 million tokens) compared to GPT-4o (128K tokens), allowing for processing of significantly larger data volumes.
- Performance: GPT-4o shows a slight edge in some benchmark areas, scoring 88.7% on MMLU (5-shot) compared to Gemini 1.5 Flash's 78.9% (5-shot).
- Cost: Gemini 1.5 Flash is significantly more cost-effective, with a blended price of $0.53 per million tokens compared to GPT-4o's $7.50 per million tokens (blended 3:1 input/output ratio).
- Speed: Gemini 1.5 Flash has a faster output speed of 163.6 tokens per second compared to GPT-4o's 86.8 tokens per second.
- Latency: GPT-4o has lower latency with a Time to First Token (TTFT) of 0.45 seconds, while Gemini 1.5 Flash has a TTFT of 1.06 seconds.
- Maximum Output: GPT-4o can generate up to 16.4K tokens per request, while Gemini 1.5 Flash is limited to 8,192 tokens.
- Language Support: Gemini 1.5 Flash explicitly supports over 100 languages, while GPT-4o's language support is described as superior across non-English languages but not quantified.
For most applications requiring a balance between advanced capabilities, speed, and cost-effectiveness, Gemini 1.5 Flash appears to be an excellent choice. However, for tasks demanding the highest level of performance on certain benchmarks or lower latency, GPT-4o may be preferable despite its higher cost.
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Other comparisons
FAQ
What are the main differences in capabilities between GPT-4o and Gemini 1.5 Flash?
The main differences in capabilities between GPT-4o and Gemini 1.5 Flash are:
- Context Window: Gemini 1.5 Flash has a much larger context window (1 million tokens) compared to GPT-4o (128K tokens).
- Performance: GPT-4o shows slightly better performance on some benchmarks like MMLU.
- Speed: Gemini 1.5 Flash has a faster output speed (163.6 tokens/s) compared to GPT-4o (86.8 tokens/s).
- Latency: GPT-4o has lower latency with a TTFT of 0.45s, while Gemini 1.5 Flash has a TTFT of 1.06s.
- Maximum Output: GPT-4o can generate up to 16.4K tokens per request, while Gemini 1.5 Flash is limited to 8,192 tokens.
- Cost: Gemini 1.5 Flash is significantly more cost-effective than GPT-4o.
Which model is more cost-effective for general-purpose tasks?
For general-purpose tasks, Gemini 1.5 Flash is significantly more cost-effective:
- Gemini 1.5 Flash has a blended price of $0.53 per million tokens ($0.35 for input, $1.05 for output).
- GPT-4o costs $7.50 per million tokens (blended 3:1 input/output ratio).
- This makes Gemini 1.5 Flash approximately 14 times cheaper than GPT-4o for most use cases.
- The cost difference is substantial, making Gemini 1.5 Flash a more economical choice for applications with high volume token processing or budget constraints.
However, the cost difference should be weighed against specific performance requirements and use case needs, as GPT-4o may offer advantages in certain scenarios despite its higher price.
How do the models compare in terms of performance benchmarks?
GPT-4o and Gemini 1.5 Flash perform differently on various benchmarks:
- MMLU (Massive Multitask Language Understanding): GPT-4o scores 88.7% (5-shot) compared to Gemini 1.5 Flash's 78.9% (5-shot).
- MMMU (Massive Multitask Multimodal Understanding): GPT-4o scores 59.4%, while Gemini 1.5 Flash scores 56.1% (0-shot).
- HumanEval: GPT-4o achieves 87.2% (0-shot), outperforming Gemini 1.5 Flash's 74.3% (0-shot).
- HellaSwag: Gemini 1.5 Flash scores 86.5% (10-shot), while this benchmark is not available for GPT-4o.
These benchmarks suggest that GPT-4o generally performs better on language understanding and reasoning tasks. However, the performance difference is not overwhelming, and Gemini 1.5 Flash remains highly competitive, especially considering its significantly lower cost.
What are the key factors to consider when choosing between GPT-4o and Gemini 1.5 Flash for a project?
When choosing between GPT-4o and Gemini 1.5 Flash for a project, consider the following factors:
- Context Length: If your project requires processing very large documents or extensive conversation histories, Gemini 1.5 Flash's larger context window (1M tokens) may be advantageous.
- Speed Requirements: For applications needing extremely fast output, Gemini 1.5 Flash's higher token generation speed may be preferable.
- Latency Sensitivity: If your application requires very low latency for the first response, GPT-4o's lower TTFT might be more suitable.
- Budget: Gemini 1.5 Flash is significantly more cost-effective, making it ideal for projects with budget constraints or high-volume usage.
- Performance Requirements: Consider GPT-4o's slight edge on certain benchmarks if your application aligns closely with these tasks and requires maximum performance.
- Maximum Output Length: If your application needs to generate longer responses in a single request, GPT-4o's higher maximum output (16.4K tokens) might be beneficial.
- Language Diversity: For applications requiring support for a wide range of languages, Gemini 1.5 Flash's explicit support for over 100 languages may be an advantage.
- API Integration: Consider the ease of integration with your existing infrastructure and the specific API features offered by OpenAI (for GPT-4o) or Google (for Gemini 1.5 Flash).
Evaluate these factors based on your project's specific requirements, balancing the need for advanced capabilities with cost-effectiveness, speed, and scalability.
How many AI models can I build on my app?
You can build as many models as you want!
Licode places no limits on the number of models you can create, allowing you the freedom to design, experiment, and refine as many data models or AI-powered applications as your project requires.
Which LLMs can we use with Licode?
Licode currently supports integration with seven leading large language models (LLMs), giving you flexibility based on your needs:
- OpenAI: GPT 3.5 Turbo, GPT 4o Mini, GPT 4o
- Google: Gemini 1.5 Pro, Gemini 1.5 Flash
- Anthropic: Claude 3 Sonnet, Claude 3 Haiku
These LLMs cover a broad range of capabilities, from natural language understanding and generation to more advanced conversational AI. Depending on the complexity of your project, you can choose the right LLM to power your AI app. This wide selection ensures that Licode can support everything from basic text generation to advanced, domain-specific tasks such as image and code generation.
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