AI Service Input Token Limits: A Comprehensive Comparison (December 2024)

This document provides a detailed comparison of various AI services based on their input token limits, categorizing them into budget-friendly and expensive options. It also identifies services offering the highest input limits through their applications. Understanding these limits is crucial for optimizing AI usage, balancing cost, and ensuring the quality of responses. Token limits directly impact the amount of text an AI model can process in a single request, affecting its ability to handle complex tasks, lengthy documents, and detailed conversations.

Understanding Token Limits

Before diving into specific services, it's important to understand what tokens are. In the context of AI, tokens are the basic units of text that a model processes. These can be words, parts of words, or even individual characters. The token limit refers to the maximum number of these units that an AI model can handle in a single input. Exceeding this limit can lead to truncated inputs, errors, or incomplete responses. Efficiently managing token usage is essential for cost-effective and effective AI applications. Techniques like chunking, summarization, and prioritization can help manage token limits.

Budget-Friendly AI Services

Budget-friendly options provide a cost-effective way to access AI capabilities, often with reasonable token limits suitable for many common tasks. These services are ideal for smaller projects, general-purpose applications, and users who are mindful of costs.

Amazon Bedrock

Amazon Bedrock offers a variety of models with different token limits. Notably, the Claude 2.1 model stands out with a very high input limit of 100,000 tokens. This makes it suitable for large-scale content generation and summarization tasks. The pricing for Claude 2.1 is $0.008 per 1,000 input tokens and $0.024 per 1,000 output tokens. For example, processing 7.5 million input tokens would cost $60. Amazon Bedrock also provides a Provisioned Throughput payment plan for large workloads, ensuring predictable costs. Additionally, the Titan Text Express model, also available through Amazon Bedrock, supports up to 8,000 tokens at a cost of $0.0008 per 1,000 input tokens, making it one of the cheapest options for smaller-scale tasks. For example, processing 3.75 million input tokens would cost $3. Source

Google PaLM 2

Google’s PaLM 2-based models support 8,000 tokens for most applications. Google's pricing is based on a per-character model, which can be more economical for large-scale input tasks. While exact pricing details are not disclosed, it is noted to be competitive for high-volume operations. The clear input costs allow businesses to budget efficiently, and PaLM 2 models are optimized for cost-effectiveness in large-scale tasks. Source

LLaMA Models (Meta)

LLaMA models, such as LLaMA 3.3 Instruct 70B and LLaMA 3.1 Instruct 405B, offer a substantial input token limit of up to 128,000 tokens. These models are open-source, with costs primarily associated with hosting and API providers. Many providers offer competitive pricing for LLaMA models, making them suitable for research and academic use cases. They are capable of handling large documents, such as research papers, technical manuals, and codebases. While they provide high-quality output for structured data and reasoning tasks, their output speeds are generally slower compared to proprietary models. Source

GPT-4 (8k Context Window)

The standard GPT-4 model, with an 8,000 token context window, is a more budget-friendly option compared to its higher-capacity counterparts like GPT-4 Turbo or GPT-4o. It is suitable for short-form content generation, customer support, and chatbot integration. However, it is limited for large-scale summarization or document analysis due to its smaller context window. Source

Cerebras Models

Cerebras models offer an input token limit of 33,000 tokens and are positioned as a budget-friendly option for high-context applications. They are efficient for summarizing medium-length documents and processing multi-turn conversations. While they offer moderate speed and quality compared to larger proprietary models, they provide a good balance between cost and performance for specific use cases. Source

GPT-3.5 Turbo

GPT-3.5 Turbo is a very economical option, especially for basic tasks. It has a context window of 16,000 tokens and is priced at $0.0005 per 1,000 input tokens and $0.0015 per 1,000 output tokens. This makes it a great value for applications that do not require the extensive context of the more advanced models. There is also a GPT-3.5 Turbo Instruct model with a 4,000 token context window, priced at $0.0015 per 1,000 input tokens and $0.002 per 1,000 output tokens.

SpicyChat

SpicyChat offers varying token limits based on subscription tiers. Free and 'Get a Taste' tier users have a token limit of 2,048 tokens. 'True Supporter' tier users can access over 4,096 tokens, and 'All In' tier users can utilize up to 8,192 tokens on certain models. This tiered approach allows users to choose a plan that fits their needs and budget. Source

Other Budget Options

Other services like PromptHub and Voiceflow, which utilize OpenAI models, operate within the token limits of those models. Chatsonic and Perplexity AI, while not specifying exact token limits, also generally follow the token limits of the underlying GPT models they use. YouChat offers unlimited tokens for $6.99 per month, and HuggingChat provides unlimited tokens for $9 per month using the Llama 2 model, both of which are relatively affordable paid options.

Expensive AI Services

Expensive AI services offer higher performance, larger context windows, and advanced features, but come at a premium price. These services are typically used for enterprise-level applications, complex tasks, and situations where high accuracy and extensive context are critical.

OpenAI GPT-4 (32k Context Window)

GPT-4 with a 32,000 token context window is a significant step up from the 8,000 token version. It is suitable for advanced tasks requiring long context windows, such as dataset analysis, math tasks, and advanced reasoning. It also offers multimodal capabilities for text and image inputs. However, it comes at a higher cost, with pricing at $0.06 per 1,000 input tokens and $0.12 per 1,000 output tokens. For example, processing 7.5 million input tokens would cost $450. Source

OpenAI GPT-4 Turbo

GPT-4 Turbo supports 128,000 tokens, one of the highest among OpenAI's offerings. It is optimized for large-scale, high-complexity tasks and offers faster response times compared to the standard GPT-4. While specific pricing details for GPT-4 Turbo are not explicitly listed, it is expected to be higher than the standard GPT-4 due to its extended context window. Source

GPT-4o (OpenAI, Nov '24)

GPT-4o, released in November 2024, offers an input token limit of 128,000 tokens. It is priced at $2.50 per 1 million input tokens and $10.00 per 1 million output tokens, resulting in a blended cost of $4.38 per 1 million tokens (assuming a 3:1 input-to-output ratio). GPT-4o is designed for enterprise-level use cases like legal document analysis, code generation, and summarizing large datasets. It handles long conversations and multi-document queries without losing context. It also boasts a high output speed of 112.7 tokens per second and low latency for real-time applications. Source

Gemini 1.5 Pro (Google)

Gemini 1.5 Pro stands out with an impressive input token limit of 1 million tokens. This model is designed for processing extremely large datasets, such as 1,500-page PDFs, 30,000 lines of code, or 96 Cheesecake Factory menus. It is ideal for enterprises requiring extensive context retention and analysis. While the pricing is premium and not disclosed, it is significantly higher than OpenAI models. Gemini 1.5 Pro excels in maintaining context over long conversations or document chains and offers superior tokenization efficiency, enabling complex multi-step reasoning. Source

Drift

Drift offers unlimited tokens, but this comes at a significant cost of $2,500 per month, billed annually. This makes it one of the more expensive options, suitable for organizations with very high-volume needs and the budget to support it. Source

Other Expensive Options

LivePerson and Ada, focused on customer service automation, do not specify token limits but are priced on an enterprise level, indicating higher costs. These services are tailored for large organizations with complex needs and are likely to be on the higher end of the cost spectrum.

Highest Input Limits

The highest input limits are primarily offered by the most advanced models, which are designed to handle very large amounts of text in a single request. These models are ideal for tasks that require extensive context and detailed analysis.

Gemini 1.5 Pro

Gemini 1.5 Pro leads with a 1 million token limit, making it suitable for processing entire books, large codebases, or extensive datasets. It retains full context across long conversations or multi-document workflows. Source

GPT-4 Turbo and GPT-4o

Both GPT-4 Turbo and GPT-4o offer a 128,000 token limit. GPT-4 Turbo is optimized for large-scale, high-complexity tasks, while GPT-4o provides a balance of high input limits with superior output speed and quality. Source

LLaMA Models

LLaMA models, particularly the larger versions like LLaMA 3.3 Instruct 70B and LLaMA 3.1 Instruct 405B, also offer a 128,000 token limit. Their open-source nature provides flexibility for custom use cases. Source

Claude 2.1

Claude 2.1, available through Amazon Bedrock, supports up to 100,000 tokens, making it a cost-effective high-capacity model. It is exceptional for summarizing lengthy documents and handling large datasets. Source

Summary Table

The following table summarizes the key information about the discussed AI models, including their input token limits, pricing, and best use cases:

Key Insights and Considerations

When selecting an AI service, consider the following:

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Conclusion

Choosing the right AI service depends heavily on your specific needs and budget. For the highest input limits, Gemini 1.5 Pro is the clear leader, followed by GPT-4 Turbo, GPT-4o, and LLaMA models. For budget-conscious users, models like GPT-3.5 Turbo, LLaMA, and Claude 2.1 offer a good balance of cost and performance. Understanding the trade-offs between cost, token limits, and performance is crucial for making informed decisions and optimizing your AI applications.