AI Token Price: How Much Do OpenAI, Claude, Gemini, and Other AI APIs Cost?

By: WEEX|2026/04/30 12:15:33
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AI token price means the cost of using an AI model API, measured by the number of input and output tokens processed by the model. A token is a small unit of text, often a word fragment, punctuation mark, number, or short word. In practice, AI platforms charge separately for the prompt you send to the model and the answer the model generates.

AI Token Price: How Much Do OpenAI, Claude, Gemini, and Other AI APIs Cost?

That split is the key to understanding AI API pricing. A model that looks cheap on input tokens can become expensive if your application generates long answers, uses reasoning tokens, calls tools, searches the web, or keeps large conversation history in context.

As of April 30, 2026, OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral, and Perplexity all publish token-based pricing, but they do not package costs in exactly the same way. Some platforms price cached input separately. Some charge extra for search. Some include thinking tokens in output. Some offer batch discounts. The right comparison is not just "which model is cheapest?" It is "which model is cheapest for the workload I actually run?"

AI Token Price Comparison by Platform

The table below summarizes selected public API prices checked from official pricing or documentation pages on April 30, 2026. Prices are listed per 1 million tokens in USD unless noted.

PlatformExample model or tierInput priceOutput priceCost note
OpenAIGPT-5.5$5.00$30.00Premium model for coding and professional work; cached input listed at $0.50
OpenAIGPT-5.4 mini$0.75$4.50Lower-cost OpenAI option for coding, computer use, and subagents
AnthropicClaude Opus 4.7$5.00$25.00Opus-class pricing; cache reads listed at $0.50 per MTok
AnthropicClaude Sonnet 4.6$3.00$15.00Balanced Claude option for coding and agentic tasks
AnthropicClaude Haiku 4.5$1.00$5.00Lower-cost Claude tier
Google GeminiGemini 3.1 Pro, prompts <= 200K$3.60$21.60Output price includes thinking tokens
Google GeminiGemini 3 Flash$0.50$3.00Speed-focused model; batch/flex options can be cheaper
Google GeminiGemini 2.5 Flash$0.30$2.50Cost-efficient general model
DeepSeekDeepSeek-V4-Flash$0.14 cache miss / $0.0028 cache hit$0.28Very low listed rate with 1M context
DeepSeekDeepSeek-V4-Pro$0.435 cache miss / $0.003625 cache hit$0.87Official page showed discounted rates on April 30, 2026
MistralMistral Small 4$0.15$0.60Hybrid instruct, reasoning, and coding model
MistralMistral Medium 3.5$1.50$7.50Frontier-class multimodal model optimized for agentic and coding use cases
PerplexitySonar Pro$3.00$15.00Search request fees are charged separately
PerplexitySonar Deep Research$2.00$8.00Adds citation, search-query, and reasoning-token pricing

The quick read: DeepSeek and Mistral publish some of the lowest listed token prices, Gemini Flash-style models are strong for high-volume workloads, and OpenAI or Claude premium models cost more because they target harder reasoning, coding, and agentic work. But price alone does not prove value. A cheaper model that needs three retries can cost more than a premium model that completes the task once.

What Input and Output Tokens Mean

Input tokens are everything you send to the model: the user prompt, system message, conversation history, examples, retrieved documents, tool schemas, and sometimes file or image representations. Output tokens are what the model generates back.

CLAUDE.webp

Output tokens often matter more because they are usually more expensive. OpenAI's GPT-5.5, for example, lists output at $30 per 1 million tokens versus $5 for input. Claude Sonnet 4.6 lists output at $15 versus $3 for input. Gemini 3.1 Pro lists output at $21.60 versus $3.60 for prompts up to 200K tokens.

That means a chatbot that gives long answers, an AI writing tool that drafts full articles, or an agent that explains every step can burn budget quickly. If you want lower AI token price in real production, controlling output length is often more important than shaving a few hundred tokens from the prompt.

How to Estimate Real AI API Cost

The basic formula is simple:

Total cost = input tokens x input rate + output tokens x output rate + tools/search/storage fees

For example, suppose a support chatbot uses Claude Sonnet 4.6 and one request has 2,000 input tokens and 600 output tokens. At $3 per 1M input tokens and $15 per 1M output tokens, the request cost is:

ItemTokensRateCost
Input2,000$3 / 1M$0.006
Output600$15 / 1M$0.009
Total2,600Mixed$0.015

That looks tiny per request, but it scales. One million similar requests would cost about $15,000 before any extra tool, search, storage, logging, retry, or orchestration costs.

This is why teams should test with real traffic samples. A pricing page tells you the rate. Your product design determines the token volume.

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Which AI Platform Is Cheapest?

There is no universal cheapest platform because "cheap" depends on the workload.

For high-volume classification, extraction, tagging, and short summarization, lower-cost models such as DeepSeek-V4-Flash, Mistral Small 4, Gemini Flash, or Haiku-style tiers may be enough. These workloads often have predictable prompts and short outputs, so cost matters more than maximum reasoning depth.

For coding agents, complex research, long-context analysis, and professional workflow automation, the best value may come from a stronger model even if its token price is higher. OpenAI GPT-5.5, Claude Opus/Sonnet, Gemini Pro, and Mistral Medium-style models are priced for harder work. If a premium model reduces retries, hallucinations, review time, or failed tool calls, it can be cheaper at the workflow level.

For search-heavy applications, Perplexity Sonar pricing needs a separate lens. Token price is only part of the bill. Sonar and Sonar Pro also include request fees by search context size, while Sonar Deep Research can add citation tokens, search-query costs, and reasoning tokens.

What Most People Miss About AI Token Price

The first mistake is comparing only the input-token number. Output is usually more expensive, and many modern models also bill thinking or reasoning tokens as part of the output side.

The second mistake is ignoring cached input. OpenAI, Anthropic, Google, DeepSeek, and xAI all describe cached or cache-related pricing in different ways. If your app repeatedly sends the same long system prompt, policy text, product catalog, or documentation block, caching can materially reduce cost. If every request is unique, caching helps less.

The third mistake is forgetting that tools are not free. Web search, code execution, file search, retrieval, storage, image generation, voice, and long-context processing can all change the effective price. xAI's official docs, for example, separate token costs from server-side tool invocation costs. Perplexity separates token pricing from search request fees. Google charges separately for some grounding and search usage.

The fourth mistake is assuming every token is equal across providers. Tokenizers differ. Anthropic notes that Claude Opus 4.7 uses a new tokenizer that may use up to 35% more tokens for the same fixed text. That matters when comparing providers by price per million tokens.

For readers tracking how AI model costs affect broader technology and market narratives, WEEX has also published coverage of OpenAI GPT-5.5 for agentic tasks. That is a separate topic from API billing, but it helps explain why model capability, token cost, and market attention often move together when a major AI platform changes pricing or releases a stronger model.

That market link is especially relevant when AI news spills into listed equities, AI infrastructure names, and digital assets with AI narratives. In those cases, unit price is not enough. Readers also need to understand valuation basics such as crypto market cap before treating an AI headline as a reason to chase any token or market proxy.

Practical Budgeting Tips

Start with a small benchmark set. Run the same real prompts across two or three candidate models, then measure input tokens, output tokens, latency, accuracy, and retry rate.

Cap output length. Long answers are expensive, and users often prefer concise responses anyway. Use maximum output limits, structured formats, or short answer modes where possible.

Separate easy and hard tasks. Do not send every request to the most expensive model. Route simple classification, rewriting, and extraction jobs to cheaper models, then reserve premium models for complex reasoning, coding, or high-stakes review.

Use caching where the same context repeats. Long system prompts, policy documents, style guides, and product reference material are good candidates.

Watch tool usage. Search, file retrieval, and code execution may be necessary, but they should be measured as part of total cost, not treated as invisible model behavior.

Risk Warning: AI API Pricing Can Change Fast

The biggest risk in AI token price comparisons is stale data. Providers change model names, discount structures, batch pricing, cache rules, context-window tiers, and tool charges. A comparison that was accurate in April 2026 may be wrong after a model launch or pricing update.

There is also operational risk. A prompt loop, retry bug, runaway agent, overly long context window, or tool-calling error can turn a cheap prototype into an expensive production incident. Set hard spend limits, monitor usage by feature, log token counts, and review invoices during the first weeks after deployment. The same discipline applies to trading around AI pricing news: a practical framework for risk management in trading is more useful than reacting to every model launch as a signal.

Security risk belongs in the same conversation. AI API keys, billing dashboards, cloud consoles, and trading accounts all become high-value targets once automation is connected to real money or real infrastructure. If your team is tightening access controls, WEEX's guide to Two-Factor Authentication (2FA) is a useful plain-language refresher on why second-factor protection matters. Teams should also refresh basic anti-phishing habits, especially when API-key resets, fake billing alerts, and support impersonation messages increase after major AI product news. WEEX's guide on how to spot phishing and safeguard your WEEX account is relevant beyond exchange accounts because the attack pattern is similar across developer tools and financial platforms.

Finally, avoid choosing a model only because it has the lowest listed token price. The real risk is paying less per token but more per successful task because the model needs more retries, produces weaker answers, or requires more human review.

Bottom Line

The best way to compare AI token price is to calculate the cost of a real task, not just the sticker price per million tokens. OpenAI and Claude premium models are expensive but may be worth it for complex work. Gemini, DeepSeek, and Mistral offer strong lower-cost options for high-volume workflows. Perplexity is useful when built-in search is central, but its request and search costs must be counted separately.

Before choosing a platform, test your own prompts, measure input and output tokens, include tool fees, and compare the cost per successful result. That is the only AI token price that actually matters in production.

FAQ

What is AI token price?

AI token price is the amount an AI platform charges to process text tokens through a model API. Most platforms charge separately for input tokens, which are the prompts and context you send, and output tokens, which are the model's response.

Which AI API has the lowest token price?

Based on official prices checked on April 30, 2026, DeepSeek-V4-Flash and some Mistral models list very low per-million-token rates. But the cheapest model for your product depends on accuracy, retries, output length, caching, tool use, and latency.

Why are output tokens more expensive than input tokens?

Output tokens require the model to generate new text, often with reasoning or planning. Many providers price output several times higher than input, so long responses can dominate the bill.

Are thinking tokens billed?

Often, yes. Google Gemini's pricing page states that output price includes thinking tokens for several models. Other providers may count reasoning or internal planning differently, so check the official docs for the model you use.

How many words are in 1 million tokens?

There is no exact universal conversion because tokenizers differ by provider and language. A rough English estimate is that 1 token is about 3-4 characters, or around three-quarters of a word. Always use the provider's tokenizer or usage metadata for billing estimates.

How can I reduce AI API costs?

Use shorter prompts, cap output length, cache repeated context, route easy jobs to cheaper models, batch non-urgent work where supported, and monitor tool calls. Most savings come from product design, not from chasing the lowest rate alone.

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