AI Model Comparison
Compare 10+ LLM specs side by side - context, pricing, capabilities
Gemini 2.0 Flash
$0.5/M total
Gemini 2.0 Flash
1M tokens
GPT-4o
5/5 features
| Specification | GPT-4oOpenAI | GPT-4o miniOpenAI | Claude 3.5 SonnetAnthropic | Gemini 2.0 FlashGoogle |
|---|---|---|---|---|
| Vendor | OpenAI | OpenAI | Anthropic | |
| Release Date | 2024-05 | 2024-07 | 2024-06 | 2024-12 |
| Context Window | 128K | 128K | 200K | 1M |
| Max Output | 16K | 16K | 8K | 8K |
| Input $/M tokens | $2.50 | $0.15 | $3.00 | $0.10 |
| Output $/M tokens | $10.00 | $0.60 | $15.00 | $0.40 |
| Vision | ||||
| Function Calling | ||||
| JSON Mode | ||||
| Streaming | ||||
| Fine-tuning | ||||
| Best For | General-purpose tasks, multimodal applications, production chatbots | High-volume chatbots, classification, simple Q&A, cost-sensitive apps | Coding, long-form writing, analysis, nuanced reasoning | Large document processing, video analysis, cost-sensitive high-volume apps |
Context Window Comparison
Data based on official API documentation as of July 2026. Specifications may change. Visit official documentation for the most current information.
How to Choose the Right LLM in 2026
With 10+ frontier models available, choosing the right LLM for your project depends on five key factors: cost, context window, capabilities, performance, and vendor ecosystem. Use the comparison tool above to evaluate models across these dimensions simultaneously.
For Cost-Sensitive Apps
Choose GPT-4o mini ($0.15/M) or Gemini 2.0 Flash ($0.10/M) for high-volume tasks. DeepSeek V3 ($0.27/M) offers the best value for Chinese language workloads.
For Long Documents
Gemini 1.5 Pro (2M context) processes entire codebases or books. Claude 3.5 Sonnet (200K) is better for nuanced analysis of medium-length documents.
For Coding & Development
Claude 3.5 Sonnet leads in coding benchmarks. GPT-4o is a strong all-rounder. o1 excels at complex algorithmic challenges.
For Multimodal (Vision)
GPT-4o and Gemini 2.0 Flash offer the best price-to-vision ratio. Claude 3.5 Sonnet excels at complex image analysis and OCR.
Model Tier Guide
Most capable models (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, DeepSeek V3). Best quality, highest cost. Use for production-critical tasks.
Optimized for speed and cost (GPT-4o mini, Claude 3.5 Haiku, Gemini 2.0 Flash, Llama 3.1 70B). Best for high-volume, latency-sensitive applications.
Deep thinking models (o1, o1 mini). Best for complex math, science, and multi-step problems. Higher latency and cost, but superior accuracy on hard tasks.
FAQ
Which LLM has the largest context window in 2026?
Gemini 1.5 Pro has the largest context window at 2 million tokens, followed by Gemini 2.0 Flash at 1 million tokens. For comparison, GPT-4o and Claude 3.5 Sonnet both support 128K-200K tokens. A larger context window allows processing entire books, codebases, or hours of video in a single request.
Which AI models support vision capabilities?
As of July 2026, GPT-4o, GPT-4o mini, o1, Claude 3.5 Sonnet, Claude 3.5 Haiku, Gemini 2.0 Flash, and Gemini 1.5 Pro all support vision (image understanding). DeepSeek V3 and Llama 3.1 70B are text-only. If you need vision at low cost, GPT-4o mini and Gemini 2.0 Flash are the most affordable options.
Which LLM is best for coding in 2026?
Claude 3.5 Sonnet is widely considered the best model for coding, with strong performance on SWE-bench and real-world development tasks. GPT-4o is a close second. For budget-conscious developers, DeepSeek V3 offers excellent coding performance at 89% lower cost than GPT-4o. For complex algorithmic problems, o1 provides superior multi-step reasoning.
Can I fine-tune these AI models?
Fine-tuning is available for GPT-4o, GPT-4o mini, and Llama 3.1 70B. OpenAI offers managed fine-tuning through their API. Llama 3.1 70B can be fine-tuned locally or via providers like Together AI and Replicate. Anthropic and Google models currently do not support fine-tuning. For custom use cases, Llama 3.1 70B is the best open-source option.
What is the difference between function calling and JSON mode?
Function calling allows the model to invoke external tools/APIs based on user requests - the model decides which function to call and with what parameters. JSON mode forces the model to output valid JSON. Function calling is more flexible and is needed for agentic workflows (tool use, API orchestration). JSON mode is simpler and sufficient for structured data extraction. GPT-4o, Gemini, DeepSeek, and Llama support both.