We analyzed 15+ LLMs and their pricing and performance. LLM API pricing can be complex and depends on your preferred usage. If you plan to use:
- The chat user interface, see all major LLM subscription plans
- APIs, see LLMs ranked by their performance and type your volume needs in terms of tokens to see the exact pricing:
Model | Input Price | Output Price | Context Length | Max Output Tokens | Arena Score |
|---|---|---|---|---|---|
Google Gemini-2.5-Pro | $2.50 | $15.00 | 1,000k | 65k | 1,455 |
OpenAI o3 | $10.00 | $40.00 | 200k | 100k | 1,444 |
OpenAI gpt-5 | $1.25 | $10.00 | 400k | 128k | 1,442 |
OpenAI gpt-4o | $2.50 | $10.00 | 128k | 16k | 1,441 |
OpenAI gpt-4.5 | $75.00 | $150.00 | 128k | 16k | 1,439 |
xAI Grok-3-Preview | $3.00 | $15.00 | 131k | N/A | 1,409 |
OpenAI o1-2024-12-17 | $15.00 | $60.00 | 200k | 100k | 1,399 |
DeepSeek DeepSeek-R1 | $0.55 | $2.19 | 64k | 8k | 1,394 |
DeepSeek DeepSeek-V3 | $0.27 | $1.10 | 64k | 8k | 1,391 |
OpenAI gpt-5-mini | $0.25 | $2.00 | 400k | 128k | 1,388 |
Hover over model names to see their full names and over headers to see explanations about the columns.
Ranking: The ranking of models is based on the Overall Arena Score in the Chatbot Arena LLM leaderboard.1
When the official Grok 3 API is released and Gemini’s experimental models exit their experimental phase, the missing values will be populated with the official values.
Rate limits control Google API request frequency for free tiers.2
You can check the hallucination rates and reasoning performance of top LLMs in our benchmarks.
Understanding LLM pricing
Tokens: The Fundamental Unit of Pricing
Figure 1: Example of tokenization using the GPT-4o & GPT-4o mini tokenizer for the sentence “Identify New Technologies, Accelerate Your Enterprise.”3
While providers offer a variety of pricing structures, per-token pricing is the most common. Tokenization methods differ across models; examples include:
- Byte-Pair Encoding (BPE): Splits words into frequent subword units, balancing vocabulary size and efficiency.4
- Example: “unbelievable” → [“un”, “believ”, “able”]
- WordPiece: Similar to BPE but optimizes for language model likelihood, used in BERT.5
- Example: “tokenization” → [“token”, “##ization”]. “token” is a standalone word; “##ization” is a suffix.
- SentencePiece: Tokenizes text without relying on spaces, effective for multilingual models like T5.6
- Example: “natural language” → [” natural”, ” lan”, “guage”] or [” natu”, “ral”, ” language”].
Please note that the exact subwords depend on the training data and BPE/WordPiece process. To better understand these tokenization methods, watch the video below:
After grasping tokenization, an average price can be estimated based on the project token length. Table 2 outlines token ranges by content type, such as UI prompts, email snippets, marketing blogs, detailed reports, and research papers, while noting that token counts vary across models. Once a model is chosen, its tokenizer can be used to estimate the average token count for the content.
Table 2: Typical content types, their size ranges, and enterprise considerations (ranges are estimates and may vary).
Context window implications
Awareness of the context window concept is another crucial factor to consider regarding pricing. Here, it is essential to ensure that the total number of tokens from both the input and output does not exceed the context window/length.
If the total exceeds the context window, it may lead to the truncation of the excess output, as shown in Figure 2. Therefore, the output may not be as expected. It is important to note that tokens generated during the reasoning process are also counted within this limitation.
Figure 2: Illustration of context window limitations leading to output truncation in a multi-turn conversation.7
Max output tokens
This is an important parameter in Large Language Models (LLMs) to achieve the desired output and manage costs effectively. While many documentations mention that it can be adjusted using the max_tokens parameter, it is crucial to review the documentation of the specific API being used to identify the correct parameter. It should be adjusted according to the specific needs:
If set too low: It may result in incomplete outputs, causing the model to cut off responses before delivering the full answer.
If set too high: Depending on the temperature (a parameter that controls response creativity), it can lead to unnecessarily verbose outputs, longer response times, and increased cost.
Therefore, it is a parameter that requires careful consideration to optimize resource usage while balancing output quality, cost, and performance.
Table 3: Example input prompts and estimated token counts per content type.
*This assumes that each model produces responses with an equal number of output tokens, although the token count for both input and output may vary depending on each model’s tokenization; the number has been kept constant here for each model.
The LLM API price calculator can be used to determine the total cost per model when generating content types from Table 2 via the API, using the sample prompts provided in Table 3. Additionally, it can be used to calculate costs for custom cases beyond the suggested content types.
LLM API price calculator
You can calculate your total cost by filling out these 3 values below and sorting the results by input cost, output cost, total cost, or alphabetically in increasing or decreasing order:
Note: The default ranking is based on the total cost.
Comparing LLM subscription plans
Non-technical users may prefer to use the UI rather than the API, here is the UI pricing:
Using multiple language models
A tool like OpenRouter allows the same prompt to be sent simultaneously to multiple models. The responses, token consumption, response time, and pricing can then be compared to determine which model is most suitable for the task.
Figure 3: Interface showcasing a prompt sent to multiple Large Language Models (LLMs), including R1, Mistral Small 3, GPT-4o-mini, and Claude 3.5 Sonnet.8
Benefits and Challenges
- Increased Adaptability and Efficiency: Orchestration enhances responsiveness, allowing for real-time assessment of model efficiency, leading to the identification of a cost-effective model and potential savings.
- Prompt Sensitivity and Optimization: Identical prompts can elicit vastly different outputs across models, necessitating prompt engineering tailored to each model to achieve desired results, adding to development and maintenance complexity.
Pricing mechanics & hidden costs
Reasoning tokens vs. output tokens
A growing number of providers have introduced reasoning models that spend additional compute to perform chain-of-thought reasoning internally. These models may use a separate “reasoning token” class (distinct from standard output tokens), which typically costs significantly more.
For example, models like GPT-o1 or Claude 3.5 Sonnet Thinking generate internal reasoning traces even when you do not explicitly request them. These internal tokens count toward your bill and can substantially increase cost, especially in long analytical tasks such as legal review, data analysis, or multi-step reasoning.
This makes it essential to:
- Choose a reasoning model only when accuracy substantially outweighs cost.
- Disable chain-of-thought or set a shorter max output token count when possible.
- Test the same task on non-reasoning models to see if performance is comparable at a fraction of the price.
Since reasoning models can generate 10–30× more thinking tokens per request, understanding this distinction is critical for cost planning.
Architecture-driven pricing differences
LLM architectures directly influence model efficiency and, therefore, API pricing. For example:
- Mixture-of-Experts (MoE) models activate only a subset of parameters per request, reducing compute cost and allowing providers to offer lower per-token rates.
- Speculative decoding pairs a smaller draft model with a larger one, improving throughput and lowering cost for deterministic tasks.
- Quantized variants (e.g., 4-bit or 8-bit) can perform inference at lower precision, enabling lower pricing for locally deployed or cloud-hosted versions.
Understanding these architectural choices helps users predict not only pricing differences but also latency, quality, and how a model scales under production workloads.
Operational costs beyond API fees
While per-token pricing is the primary cost driver, many production deployments incur additional costs beyond API usage:
- Embeddings and vector databases: Storing and retrieving vectors (e.g., Pinecone, Weaviate, ChromaDB) adds cost per query and per GB of storage.
- Reranking and post-processing models: Many applications use smaller models for summarization, filtering, or classification before sending a final request to a bigger model.
- Caching layers: Providers like OpenAI now offer prompt-level caching, but local caching infrastructure may require additional compute.
- Logging, monitoring, and auditing: Enterprises often incur costs for token-level monitoring, latency tracking, and security audits.
These hidden costs often account for 20–40% of total LLM operational expenses and should be considered when evaluating pricing structures.
Enterprise-specific pricing considerations
Many LLM vendors charge additional fees for enterprise-grade security and compliance features, such as:
- Single-tenant deployments
- Dedicated GPU clusters
- Enhanced SLAs (e.g., uptime, latency guarantees)
- Data residency and regional controls
- SOC2, HIPAA, or GDPR compliance modes
These offerings can increase costs significantly but are essential for regulated industries such as healthcare, finance, legal services, and public institutions.
Future trends in LLM pricing
As the LLM ecosystem matures, pricing structures are expected to evolve significantly. One of the most notable shifts is the rapid commoditization of general-purpose models. Core language capabilities, such as summarization, basic Q&A, and standard content generation, are becoming increasingly inexpensive as competition grows, model efficiency improves, and open-source alternatives proliferate.
This trend mirrors earlier phases of cloud adoption, where baseline compute became highly affordable. Users can expect lower per-token prices and more generous context windows for lightweight or mid-tier models as providers compete for market share.
While general models are likely to become cheaper, advanced reasoning and specialized multimodal models will remain premium offerings. These models require substantially more computation to produce deep reasoning chains, long-term planning, code analysis, and complex multimodal understanding.
As a result, providers may maintain higher pricing for models capable of sophisticated analysis or agentic behavior. This creates a dual-market structure: inexpensive general-purpose models suitable for routine workflows and higher-priced reasoning models reserved for mission-critical or accuracy-dependent use cases.
Another significant shift is the move toward per-action pricing, which offers a more intuitive cost structure for many users. Instead of charging solely by tokens, providers may increasingly offer fixed-rate pricing for tasks such as document summarization, contract review, structured data extraction, or classification workflows.
This approach aligns with the way non-technical users already think about tasks, making budgeting more predictable and simplifying procurement decisions. As LLMs evolve into more capable “AI workers,” task-based pricing will become an attractive and practical model.
Finally, the industry may see a rise in SLA-based pricing tiers, particularly for enterprises that rely on AI for regulated, high-stakes, or time-sensitive applications. Similar to cloud infrastructure pricing, LLM providers could introduce tiered service levels (ranging from standard to business to mission-critical tiers), each with different guarantees for uptime, latency, data residency, and support response times.
These models would allow enterprises to align cost with reliability needs, paying more when guaranteed performance is essential and saving when flexibility is acceptable.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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