AI Model Pricing Comparison: Claude, GPT, Gemini

Frontier Tier Comparison

Frontier models deliver the highest reasoning quality and handle the most complex tasks, but their premium pricing makes them expensive for high-volume agent workloads. These models are best reserved for tasks that genuinely require deep reasoning, nuanced judgment, or creative generation.

Claude Opus 4 from Anthropic leads the frontier tier at $15 per million input tokens and $75 per million output tokens. Opus excels at complex multi-step reasoning, code generation, and tasks requiring careful analysis of large contexts. Its extended thinking capability, where the model reasons through problems before responding, produces higher quality outputs on difficult tasks but generates additional reasoning tokens that increase the per-request cost. Cached input tokens cost $1.50 per million, offering a 90 percent discount for repeated context.

GPT-5.5 from OpenAI prices at $5 per million input tokens and $30 per million output tokens. GPT-5.5 competes with Opus on complex reasoning tasks and offers strong performance on mathematical and scientific problems. Its reasoning mode generates visible chain-of-thought tokens that improve accuracy on complex problems at the cost of additional output token consumption.

Gemini 2.5 Pro from Google sits at $1.25 per million input tokens for standard contexts, increasing to $2.50 for contexts exceeding 200,000 tokens. Output tokens cost $10 per million. Gemini Pro's standout feature is its massive two-million-token context window, which allows it to process entire codebases or document collections in a single call. For tasks that require processing large amounts of input, Gemini Pro often delivers the lowest cost per processed token among frontier models.

For a concrete comparison, consider a complex analysis task consuming 10,000 input tokens and generating 2,000 output tokens. On Claude Opus 4, this task costs $0.30. On GPT-5.5, it costs $0.11. On Gemini 2.5 Pro, it costs $0.03. The quality difference between these models on most tasks is smaller than the price difference suggests, making model selection a critical optimization lever.

Mid-Tier Comparison

Mid-tier models represent the sweet spot for most agent workloads. They deliver quality that is indistinguishable from frontier models on 80 to 90 percent of tasks while costing one-third to one-fifth as much. For agent builders, the mid tier should be the default choice, with frontier models reserved for specific high-complexity tasks.

Claude Sonnet 4 prices at $3 per million input tokens and $15 per million output tokens. Sonnet handles coding tasks, analysis, conversational AI, and content generation with quality that rivals Opus on most workloads. Its cached input tokens cost $0.30 per million, making it exceptionally cost-effective for agents with stable system prompts. Sonnet is the most popular model for production agent deployments due to its balance of capability, speed, and price.

GPT-5.2 from OpenAI costs $1.75 per million input tokens and $14 per million output tokens. It delivers strong multi-modal capabilities, handling text, images, and structured data in a unified interface. For agents that need to process screenshots, diagrams, or photographs alongside text, GPT-5.2 provides competitive quality at a lower input price than Sonnet.

GPT-4o remains widely deployed at approximately $2.50 per million input tokens and $10 per million output tokens. Its mature ecosystem, extensive fine-tuning options, and broad community support make it a reliable choice for teams invested in the OpenAI platform. Many production agents still run on GPT-4o because the migration cost to newer models does not justify the incremental improvement for their use case.

Gemini 2.5 Flash offers the most competitive mid-tier pricing at $0.15 per million input tokens and $0.60 per million output tokens in standard mode. Thinking mode, where the model reasons through problems explicitly, costs $0.70 per million input tokens. Flash delivers surprisingly strong performance on coding, summarization, and analytical tasks, making it a compelling option for cost-sensitive agent deployments that need better quality than budget models provide.

Budget Tier Comparison

Budget models handle simple, high-volume tasks at minimal cost. They excel at classification, extraction, routing, and template-based generation. For agent architectures that use model routing, budget models typically handle 50 to 70 percent of all requests, making their low cost the foundation of efficient agent economics.

Claude Haiku 4.5 costs $1 per million input tokens and $5 per million output tokens. Despite its budget positioning, Haiku handles a surprising range of tasks competently, including basic coding, straightforward analysis, and conversational responses. Its speed, often delivering responses in under a second, makes it ideal for interactive agent experiences where latency matters as much as cost.

GPT-4o Mini prices at $0.15 per million input tokens and $0.60 per million output tokens. It provides the essentials of the GPT-4o architecture at a fraction of the cost, handling classification, extraction, and simple generation tasks effectively. Its multimodal capabilities extend to the budget tier, allowing agents to process images at low cost.

Gemini Flash-Lite represents the floor of commercial API pricing at $0.10 per million input tokens and $0.40 per million output tokens. At these prices, even massive volumes cost almost nothing. An agent processing one million simple requests per day, each consuming 500 input tokens and 100 output tokens, would pay approximately $50 per day for input and $40 per day for output, totaling $2,700 per month for a million daily interactions.

Cached and Batch Pricing

Every major provider offers significant discounts through prompt caching and batch processing. These discount mechanisms can cut effective costs by 50 to 90 percent and should be fundamental considerations in any agent cost optimization strategy.

Anthropic's prompt caching provides a 90 percent discount on cached input tokens. System prompts, tool definitions, and any stable context that repeats across calls can be cached, with the cache persisting for five minutes after the last use. For agents handling steady traffic, most input tokens qualify for cached pricing. Opus cached inputs drop from $15 to $1.50 per million, Sonnet from $3 to $0.30, and Haiku from $1 to $0.10.

OpenAI offers similar caching with a 50 percent discount on cached input tokens. While less aggressive than Anthropic's discount, the caching mechanism works automatically for messages at the beginning of the prompt, requiring no explicit cache management from the developer.

Batch processing APIs provide 50 percent discounts on both input and output tokens across providers. Anthropic's Message Batches API and OpenAI's Batch API both accept large volumes of requests for asynchronous processing within a 24-hour window. For agents handling non-time-sensitive tasks like content generation, data processing, and bulk analysis, batch mode halves costs with no quality tradeoff.

DeepSeek deserves mention for its uniquely aggressive caching, offering 90 to 98 percent discounts on cached tokens. DeepSeek V3 at $0.27 per million input tokens with cache hits at $0.01 per million makes it the cheapest option for cache-heavy workloads, though availability and rate limits differ from the major providers.

Choosing the Right Model for Each Task

The optimal approach is not choosing a single model but building a routing layer that sends each task to the cheapest model capable of handling it well. This model routing strategy combines the quality of frontier models with the economics of budget models.

A typical routing architecture uses a budget model like Haiku or Flash-Lite to classify incoming requests by complexity, then routes simple requests to the budget tier, moderate requests to the mid tier, and complex requests to the frontier tier. The routing call itself costs fractions of a cent and saves dollars per complex interaction by preventing over-provisioning.

The quality evaluation step is essential for effective routing. Before deploying a routing architecture, benchmark each model tier against your specific task types using a representative test set. Identify the quality threshold below which users or downstream processes notice degradation, then set your routing boundaries just above that threshold. Many teams discover that their quality threshold is lower than expected, allowing more traffic to flow to cheaper models.

Fallback chains provide resilience and cost optimization simultaneously. If a budget model's response fails quality checks, the system automatically re-routes to a mid-tier model. If that also fails, it escalates to frontier. This approach ensures quality while minimizing the average cost per interaction, since only the requests that truly need expensive models receive them.