LLM API pricing can be complex and depends on your preferred usage. We analyzed 15+ LLMs and their pricing and performance:
- See major LLM subscription plans
- See LLMs ranked by performance, then enter your volume needs in tokens to see the exact pricing.
Hover over model names to view their benchmark results, real-world latency, and pricing, to assess each model’s efficiency and cost-effectiveness.
Ranking: Models are ranked by their average position across all benchmarks.
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.”1
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.2
- Example: “unbelievable” → [“un”, “believ”, “able”]
- WordPiece: Similar to BPE but optimizes for language model likelihood, used in BERT.3
- 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 T5.4
- 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, including UI prompts, email snippets, marketing blogs, detailed reports, and research papers, and notes 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.5
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:
Microsoft Copilot
Free plan
Key features: Basic Microsoft app integration, works across devices, and includes 15 boosts per day.
Limitations:
- Limited AI credits (Designer only)
- Preferred model access only during non-peak hours
- Restricted Copilot Voice usage
Pro plan ($20/month)
Key features: Preferred model access, 100 boosts/day, full Microsoft 365 integration, early feature access, and complete app support.
Limitation: This plan is intended for individual use only.
Google Gemini
Basic free plan
Key features: Access to Gemini 2.0 Flash, basic writing and image tools, Google app integration, and voice conversations.
Limitations:
- Only provides access to a basic model
- Features are limited compared to paid tiers
Advanced ($20/month)
Key features: Access to Gemini 2.0 Pro (experimental), deep research tools, 1500-page document analysis, 2TB storage, custom Gems, and enhanced coding support.
Mistral AI
Free plan
Key features: Web browsing, basic file analysis, image generation, and fast “flash” responses.
Limitations:
- Training data may be used
- Capabilities are limited compared to paid plans
Pro plan ($15/month)
Key features: Unlimited web browsing, extended analysis capacity, opt-out data sharing, and dedicated support.
Limitation: This plan is meant for individual use only.
Team plan ($20/user/month annual or $25/user/month monthly)
Key features: Central billing, API credits, data excluded from training, and advanced features.
Limitation: Requires at least two team members.
Enterprise plan (Custom pricing)
Key features: Secure on-premise deployment, enhanced support, granular admin control, and detailed analytics.
OpenAI
Free plan
Key features: Access to GPT-4o mini, standard voice mode, limited uploads, and basic image generation.
Limitations:
- Usage is capped.
- Only basic models are available.
Plus plan ($20/month)
Key features: Extended usage limits, advanced voice modes, access to beta features, and limited GPT-4 availability.
Limitation: Designed for individual use and must comply with usage policies.
Pro plan ($200/month)
Key features: Unlimited access to o1/o1-mini/GPT-4o, higher video and screensharing limits, o1 Pro mode, extended Sora access, and Operator preview (U.S. only).
Limitation: Usage must remain reasonable and follow policy requirements.
Team plan ($25/user/month annual or $30/user/month monthly)
Key features: Higher message limits, advanced voice modes, admin management console, and training-excluded team data.
Limitation: Requires at least two team members.
Enterprise plan (Custom pricing)
Key features: High-speed model access, expanded context windows, enterprise-grade data controls, domain verification, analytics, and enhanced support.
Claude.ai
Free plan
Key features: Web and mobile access, basic analysis, access to the latest model, and document uploading.
Limitations:
- Usage is limited
- Features are basic compared to paid plans
Pro plan ($18/month annual or $20/month monthly)
Key features: Access to Claude 3.5 Sonnet and Opus, project organization, increased usage limits, and early feature access.
Limitation: Intended only for individual users.
Team plan ($25/user/month annual or $30/user/month monthly)
Key features: Central billing, collaboration functionality, expanded usage, and admin controls.
Limitation: Requires a minimum of five team members.
Enterprise plan (Custom pricing)
Key features: Expanded context windows, SSO, domain capture, role-based access, SCIM support, audit logs, and data integrations.
Using multiple language models
A tool like OpenRouter allows the same prompt to be sent to multiple models simultaneously. 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.6
Benefits and Challenges
- Increased Adaptability and Efficiency: Orchestration enhances responsiveness, enabling real-time assessment of model efficiency and identifying 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 incurs significantly higher costs.
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 the 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, it is critical to understand this distinction 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
Commoditization of general models
General-purpose language models are becoming less expensive as competition increases and open-source options expand. Capabilities such as summarization, fundamental question answering, and standard content generation require less specialized computation, which encourages providers to lower per-token rates.
- Growing availability of efficient open-source models.
- Lower prices for lightweight and mid-tier models.
- More generous context windows as a differentiator.
This stage resembles the early cloud market, where basic compute capacity became affordable as providers scaled.
Premium pricing for reasoning and multimodal models
In contrast to general models, advanced reasoning and multimodal systems will continue to command a premium. These models are designed for more intensive analytical tasks, such as long-form reasoning, planning, code analysis, and the interpretation of mixed data types.
- Higher compute requirements for complex reasoning.
- Demand for accuracy-sensitive workflows.
- Clear divide between commodity language tasks and high-precision tasks.
This creates a two-tier market: inexpensive general models for routine work and premium models for tasks that depend on stronger reasoning performance.
Growth of per-action pricing
Pricing strategies may shift from per-token billing to per-action structures. This approach assigns a fixed cost to tasks such as contract review, summarization, classification, or data extraction. Users who prefer predictable costs may find this structure easier to manage.
- Fixed pricing for common tasks.
- Budgeting becomes more straightforward for non-technical teams.
- Aligns with the way users already think about defined tasks.
As LLMs handle more specialized tasks, this model becomes a practical alternative for both vendors and customers.
Expansion of SLA-based pricing tiers
Enterprises with strict reliability or regulatory requirements may adopt service levels similar to those used in cloud infrastructure. These tiers could differentiate on uptime guarantees, latency expectations, data residency options, and support response times.
- Standard, business, and mission-critical tiers.
- Pricing aligned with performance expectations.
- Clear structure for organizations with varied operational needs.
This allows enterprises to align spending with required reliability rather than paying a single flat rate regardless of workload sensitivity.
Timeline of expected shift
2025 to 2026
- Increased adoption of per-action pricing, especially in productivity and enterprise tools
- Early separation of commodity language models and premium reasoning models
2026 and beyond
- Broader rollout of SLA-based pricing tiers
- More precise market segmentation between general, task-based, and advanced reasoning offerings
FAQ
Reference Links
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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