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Token Calculator 2025 - Compare 25+ AI Model Prices
The most accurate token calculator for Large Language Models. Compare real-time pricing for 25 AI models from 4 providers including OpenAI (GPT-4o, GPT-4-turbo), Anthropic (Claude 3.5 Sonnet, Claude 3.5 Haiku), Google (Gemini Pro, Gemini Flash), and xAI (Grok). Get precise token counts and cost estimates per API call, daily usage, and monthly projections.
Supported AI Model Providers
- Anthropic
- OpenAI
- xAI
Key Features
- Real-time token counting with official tokenizers
- Support for system, user, and assistant messages
- Cached input pricing calculations
- Multi-currency support (USD, EUR, GBP, JPY, CNY)
- JSON import/export for conversation data
- Model comparison across all providers
- Daily and monthly cost projections
- Export cost reports as PNG images
Popular Model Pricing
Average input pricing: $2.47 per million tokens
- Claude Haiku 3.5: Input $0.8/M, Output $4/M tokens
- Claude Opus 4.1: Input $15/M, Output $75/M tokens
- Claude Sonnet 3.7 (Legacy): Input $3/M, Output $15/M tokens
- Claude Sonnet 4: Input $3/M, Output $15/M tokens
- Gemini 2.0 Flash: Input $0.1/M, Output $0.4/M tokens
- Gemini 2.0 Flash-Lite: Input $0.075/M, Output $0.3/M tokens
- Gemini 2.5 Flash: Input $0.3/M, Output $2.5/M tokens
- Gemini 2.5 Flash-Lite: Input $0.1/M, Output $0.4/M tokens
- Gemini 2.5 Pro: Input $1.25/M, Output $10/M tokens
- GPT-4.1: Input $2/M, Output $8/M tokens
Token Calculator & API Cost Estimator
Compare real-time pricing for 25 AI models from 4 providers
Quick Price Comparison
| Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|
| Claude Haiku 3.5 | Anthropic | $0.800 | $4.000 | 200,000 |
| Claude Opus 4.1 | Anthropic | $15.000 | $75.000 | 200,000 |
| Claude Sonnet 3.7 (Legacy) | Anthropic | $3.000 | $15.000 | 200,000 |
| Claude Sonnet 4 | Anthropic | $3.000 | $15.000 | 200,000 |
| Gemini 2.0 Flash | $0.100 | $0.400 | 1,000,000 | |
| Gemini 2.0 Flash-Lite | $0.075 | $0.300 | 1,000,000 | |
| Gemini 2.5 Flash | $0.300 | $2.500 | 1,000,000 | |
| Gemini 2.5 Flash-Lite | $0.100 | $0.400 | 1,000,000 | |
| Gemini 2.5 Pro | $1.250 | $10.000 | 200,000 | |
| GPT-4.1 | OpenAI | $2.000 | $8.000 | 128,000 |
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适用场景 / Use Cases
无论是项目启动、模型选型还是成本优化，Token Calculator 都能帮你做出准确的决策
项目成本估算
Project Cost Estimation
在项目启动前，准确估算 AI API 调用成本，避免预算超支。输入预期的用户量、对话频率，快速获得日/月成本预测。
聊天机器人成本规划AI 客服系统预算智能文档处理费用
模型对比选择
Model Comparison & Selection
对比 20+ 主流模型的价格与性能，找到最适合你项目的模型。支持按价格、上下文窗口、缓存支持等维度筛选。
GPT-5 vs Claude Opus 4.1Gemini vs Grok 性价比小模型 vs 大模型场景
账单复核验证
Bill Review & Verification
收到 API 账单后，使用精确的 token 计数验证收费是否准确。我们的计算器使用官方 tokenizer，确保 99.9% 准确率。
OpenAI 账单核对Anthropic 费用确认异常扣费排查
成本优化策略
Cost Optimization Strategy
测试不同的优化策略：Prompt 压缩、缓存利用、小模型替代。实时看到成本下降效果，做出数据驱动的优化决策。
Cached Input 节省 90%Batch API 折扣测算Prompt 工程降本
立即开始计算，优化你的 AI 项目成本
100% 免费使用，无需注册，所有计算在本地完成，数据绝不上传
20+ AI 模型支持
99.9% 准确率
实时价格更新
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Frequently Asked Questions
How accurate is the token count compared to actual API billing?
Our calculator achieves 99.9% accuracy by using the exact same tokenizers as the API providers. For OpenAI models, we use the official tiktoken library. For Anthropic's Claude models, we implement their tokenization algorithm. This means our counts match exactly what you'll be billed for, unlike estimators that use simple character division.
What is cached input pricing and how much can it save?
Cached input pricing is a feature offered by providers like Anthropic and Google where you can reuse the same context (system prompt, examples, documents) across multiple API calls at a large discount. For example, Claude Sonnet 4 supports prompt caching with read starting at $0.30/1M tokens (≤200K tokens). Always refer to each provider's latest pricing.
Which AI model offers the best price-to-performance ratio in 2025?
As of September 2025, Claude 3.5 Haiku offers exceptional value at $0.25/1M input tokens with performance rivaling GPT-4o-mini. For high-volume applications, Gemini 1.5 Flash provides competitive pricing with a massive 1M token context window. GPT-4o-mini remains popular for its balance of cost ($0.15/1M input) and OpenAI ecosystem integration. The 'best' choice depends on your specific needs: latency requirements, context length, and feature support.
How do I calculate costs for a production chatbot serving 10,000 users?
For production scaling: 1) Estimate average conversation length (typically 5-10 exchanges). 2) Calculate tokens per conversation (usually 500-2000 tokens total). 3) Multiply by daily active users and conversation frequency. Example: 10,000 users × 2 conversations/day × 1,000 tokens = 20M tokens/day. With GPT-4o-mini, that's about $3-12/day depending on input/output ratio. Our calculator's 'requests per day' feature helps you model these scenarios precisely.
Can I use this calculator for fine-tuned or custom models?
Yes, our calculator supports fine-tuned model pricing. OpenAI's fine-tuned models may differ from base rates. For GPT-4o fine-tuned, we use $3.75/1M input, $15/1M output, and $1.875/1M cached input as defaults. You can also set custom enterprise prices if needed. Tokenization is unchanged, so counts remain accurate.
How often are the model prices updated and verified?
We verify all prices daily through automated checks against provider APIs and documentation. When providers announce price changes, we typically update within 2-4 hours. Each model shows a 'last verified' timestamp. We also track historical pricing trends, which is valuable for budgeting and forecasting. Major price drops in 2024-2025 have made LLMs 70% cheaper on average.
What's the difference between streaming and batch API pricing?
Most providers charge the same for streaming and non-streaming requests - you pay for total tokens regardless of delivery method. However, OpenAI offers Batch API with 50% discount for non-urgent requests (24-hour turnaround). Some providers like Anthropic offer priority tiers with different pricing. Our calculator shows standard synchronous pricing by default, but you can mentally apply batch discounts where applicable.
How do I optimize token usage to reduce API costs?
Key strategies: 1) Use system message caching for repeated contexts (90% savings). 2) Implement prompt compression techniques - remove unnecessary words while maintaining clarity. 3) Use smaller models where possible - GPT-4o-mini often suffices instead of GPT-4o. 4) Batch similar requests together. 5) Set appropriate max_tokens limits. 6) For RAG systems, optimize chunk sizes (we have a RAG Chunk Optimizer tool). These techniques can reduce costs by 50-70% without sacrificing quality.
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