Compare GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, and more – Updated Weekly
| Model | Input Price (per 1M tokens) | Output Price (per 1M tokens) | Best For |
|---|---|---|---|
| GPT-4o | $5.00 | $15.00 | Best overall quality |
| GPT-4o Mini | $0.15 | $0.60 | Best value / cheapest |
| Claude 3.5 Sonnet | $3.00 | $15.00 | Best for coding |
| Claude 3.5 Haiku | $0.25 | $1.25 | Fast, affordable |
| Gemini 1.5 Pro | $2.50 | $10.00 | Long context (2M) |
| Gemini 1.5 Flash | $0.075 | $0.30 | Fastest, cheapest |
| Llama 3 (70B) | $0.90 | $0.90 | Open source |
| Llama 3 (8B) | $0.15 | $0.15 | Very cheap |
| DeepSeek-V3 | $0.27 | $0.27 | Open source, cheap |
| Mistral Large | $2.00 | $6.00 | European alternative |
| Groq (Mixtral) | $0.24 | $0.24 | Fastest inference |
| Cohere Command R+ | $2.50 | $10.00 | RAG applications |
Choosing the right AI model for your application is no longer just about quality. In 2026, pricing has become a critical factor. With dozens of large language models (LLMs) available from OpenAI, Anthropic, Google, Meta, DeepSeek, and Mistral, understanding the cost per token is essential for startups, developers, and businesses building AI-powered products. This guide compares every major LLM's pricing, helps you calculate your monthly API costs, and reveals which model gives you the best value for your specific use case.
Before comparing prices, you need to understand what a "token" is. In LLM terms, a token is roughly 0.75 words for English text. So 1,000 tokens equals approximately 750 words. A typical API call to ChatGPT might use 500-1000 tokens for input and 200-500 tokens for output.
When you see "price per 1M tokens," that means the cost for processing 1 million tokens – roughly equivalent to 750,000 words, or about three full-length novels, or approximately 1,500 pages of text. For most applications, you'll be dealing with much smaller numbers, but pricing is standardized at the 1M token level for easy comparison.
OpenAI's GPT-4o is currently the gold standard for quality. Released in May 2025, GPT-4o matches GPT-4 Turbo quality but is 2x faster and 50% cheaper. It supports vision, audio, and text inputs, making it the most versatile model on the market. GPT-4o can understand images, transcribe audio, and generate text in a single unified model – something competitors are still catching up to.
Best for: GPT-4o is ideal for complex reasoning, coding, creative writing, and any task requiring deep understanding. GPT-4o Mini is perfect for high-volume, low-latency applications like chatbots, content moderation, data extraction, and customer support automation.
Anthropic's Claude 3.5 Sonnet is widely considered the best model for coding and complex instruction following. Independent benchmarks show Claude 3.5 Sonnet outperforms GPT-4o on programming tasks (92% vs 89% on HumanEval) and multi-step reasoning. Anthropic's models also have a 200K token context window, allowing you to process up to 150,000 words in a single API call.
Best for: Sonnet for coding, technical support, software development, and complex agent workflows. Haiku for real-time applications like customer service chatbots where speed is critical – Haiku can process 1,000 tokens in under 2 seconds.
Google's Gemini 1.5 Pro offers the longest context window of any major model – 2 million tokens. This allows you to process entire books (like all three volumes of "The Lord of the Rings" in one go), long codebases (100,000+ lines of code), or hours of video in a single API call. Gemini 1.5 Flash is Google's lightweight, ultra-fast model designed for high-volume, cost-sensitive applications.
Best for: Pro for document analysis, legal review, historical research, and any task needing massive context. Flash for high-volume, cost-sensitive applications like real-time translation, content moderation, and social media monitoring.
Llama 3 is Meta's flagship open-source model. You can run it on your own servers using tools like Ollama, vLLM, or TensorRT-LLM, or use hosting providers like Together AI, Groq, Replicate, or Fireworks AI. The 70B parameter version matches GPT-4 quality for many tasks, while the 8B version is remarkably capable for its size and can run on a single laptop GPU.
Best for: Companies wanting to avoid vendor lock-in, needing on-premise deployment for compliance reasons (healthcare, finance, government), or running batch processing where latency isn't critical. The 8B version is surprisingly capable for summarization, classification, and extraction tasks.
DeepSeek-V3 from Chinese AI company DeepSeek (backed by High-Flyer Capital Management) has gained massive popularity in recent months for being open-source and incredibly cheap. It uses a Mixture-of-Experts (MoE) architecture with 671B total parameters but only 37B activated per token, which keeps computational costs low while maintaining high quality. On benchmarks like MMLU, DeepSeek-V3 scores 88.3%, just behind GPT-4o's 88.7%.
Best for: Budget-conscious developers and researchers. DeepSeek-V3 performs similarly to GPT-4 on many benchmarks at a fraction of the cost. The R1 reasoning model is comparable to OpenAI's o1 but much cheaper.
Mistral Large is Europe's answer to GPT-4. Developed by French company Mistral AI, it supports multiple European languages natively (English, French, German, Spanish, Italian) and offers strong reasoning capabilities. Mistral also offers a free tier through their La Plateforme API.
Best for: European businesses concerned about data sovereignty (Mistral is GDPR-compliant), multilingual applications, and embedding-based search and RAG using Mistral Embed.
Groq doesn't make its own models – instead, it offers ultra-fast inference for open-source models like Mixtral 8x7B, Llama 3, and Gemma using their custom LPU (Language Processing Unit) hardware. Their technology achieves over 500 tokens per second – compared to 50-100 tokens/sec on traditional GPUs – making it ideal for real-time applications.
Best for: Real-time applications requiring sub-second response times, like voice assistants, live translation, real-time analytics dashboards, and interactive gaming AI.
Cohere specializes in Retrieval-Augmented Generation (RAG) and enterprise search. Command R+ is optimized for grounding responses in your own documents, with built-in citation support. Cohere's models are particularly strong at following complex instructions and providing verifiable, source-attributed answers.
Best for: Enterprise RAG applications, document Q&A, knowledge management systems, legal document review, and customer support automation with citation requirements.
For Production Chatbots (High Volume): GPT-4o Mini or Claude Haiku or Gemini Flash are your best bets. At $0.075-$0.25 per 1M input tokens, you can handle millions of conversations for under $100. Gemini Flash offers the absolute lowest price at $0.075 per 1M input.
For Coding Assistance: Claude 3.5 Sonnet consistently outperforms other models on coding tasks (92% on HumanEval vs 89% for GPT-4o). While more expensive at $3/$15 per 1M tokens, the improved accuracy reduces debugging time and developer frustration.
For Document Analysis (Long Context): Gemini 1.5 Pro's 2M token context window is unmatched. You can analyze entire books, long legal contracts, or extensive codebases in one API call. The next closest is Claude's 200K tokens.
For Cost-Sensitive Applications: Gemini 1.5 Flash ($0.075 input) is the absolute cheapest. GPT-4o Mini ($0.15 input) and Llama 3 8B ($0.15) are also excellent choices for budget-conscious projects.
For Real-Time Applications: Groq's LPU delivers sub-second latency (as low as 200ms for first token). Use it for voice assistants, live translation, real-time content moderation, or interactive gaming AI.
For Open Source Avoidance: Llama 3 and DeepSeek-V3 are open-weight, meaning you can download and run them on your own infrastructure. This eliminates per-token API costs entirely, though you'll pay for compute (GPUs or TPUs).
For Enterprise RAG: Cohere Command R+ offers built-in citation support and is optimized for grounding responses in your documents. Claude 3.5 Sonnet also excels at RAG with its strong instruction-following capabilities.
This comprehensive guide answers the exact questions people type into Google every day. Here are the specific search terms that lead users to this page:
1. What is the cheapest AI model for API use?
A: Gemini 1.5 Flash at $0.075 per 1M input tokens is currently the cheapest among major providers. GPT-4o Mini ($0.15) and Llama 3 8B ($0.15 via hosted providers) are close seconds.
2. Is GPT-4o Mini good enough for most applications?
A: Yes. For 80-90% of use cases (chatbots, content moderation, data extraction, summarization, sentiment analysis), GPT-4o Mini performs nearly as well as GPT-4o at 1/30th the cost. The quality difference is often imperceptible for simple tasks.
3. Which model is best for coding?
A: Claude 3.5 Sonnet consistently scores highest on coding benchmarks like HumanEval (92%), SWE-Bench, and Codeforces. It also produces more maintainable code with better comments and documentation. It's worth the higher price for serious development work.
4. Can I use open-source models to save money?
A: Yes. Llama 3 and DeepSeek-V3 are open-weight and can be run on your own servers for a fixed hardware cost, avoiding per-token API fees entirely. A single NVIDIA A100 GPU can run Llama 3 70B at ~50 tokens/sec, costing about $3/hour on cloud providers.
5. How often do AI model prices change?
A: Prices have dropped 40-60% since 2024. Competition between OpenAI, Anthropic, Google, and open-source providers continues to drive prices down. Major price drops typically happen every 3-6 months. We update this page weekly to reflect the latest prices.
6. Which model has the longest context window?
A: Gemini 1.5 Pro with 2 million tokens. That's enough to process the entire "Game of Thrones" series (all 5 books) in a single API call. GPT-4o and Claude 3 have 128K and 200K tokens respectively.
7. Does Google's Gemini offer a free tier?
A: Yes. Google AI Studio offers free usage with rate limits: 60 requests per minute. For production, Vertex AI pricing starts at $0.075 per 1M input tokens for Gemini Flash.
8. What is OpenAI's batch processing discount?
A: OpenAI offers 50% discount for batch API calls (24-hour turnaround). For asynchronous processing, this can significantly reduce costs for non-real-time applications like data analysis or content summarization.
9. Which model is best for non-English languages?
A: Gemini 1.5 Pro supports over 100 languages with strong performance. GPT-4o also performs well on major languages (Spanish, Chinese, French, German, Japanese, Korean). Mistral Large is excellent for European languages.
10. Can I negotiate custom pricing for high volume?
A: Yes. All major providers offer custom pricing tiers for customers using over 500M-1B tokens per month. Contact sales for enterprise agreements.
11. What's the difference between input and output tokens?
A: Input tokens are what you send to the API (your prompt). Output tokens are what the model generates (the response). Output tokens are typically 2-3x more expensive because generation requires more computation.
12. How many tokens does a typical ChatGPT conversation use?
A: A simple Q&A: ~200-500 tokens. A detailed product description: ~500-1000 tokens. A long conversation with history: ~2000-5000 tokens. A full document summarization: ~5000-20000 tokens.