Compare real-time pricing across all AI providers. Calculate your monthly spend with presets, compare input vs output tokens, and analyze context window limits, vision API pricing, and batch processing discounts.
Quick Start Guide
Follow these 3 simple steps to estimate your AI API costs accurately and find the most cost-effective model for your use case.
Press "Add more models" to explore the catalog. Select models from OpenAI, Anthropic, DeepSeek, and others to compare them side-by-side.
Live catalog syncEnter your expected input tokens (prompts) and output tokens (responses) per request, plus how many requests you expect per month.
View your cost projections and export a PDF report. Building for clients? You can even embed this exact calculator on your own site to capture leads.
Live estimate
Provider (10/12 · hover to remove)
Model (4 available)
Typical API, Heavy RAG, and Max context stress set monthly requests and how hard each call uses the token sliders—stress caps per request and trims calls so totals stay readable. Clears a use-case template on the right. Moving requests clears this row; moving input/output clears the tier.
Sets input, output, requests, and template value weights for the ROI read—touch a token slider and weights fall back to 50% / 50%. With Deep Reasoning, output is ×1.4 before pricing. Clears a volume preset on the left.
Off — no image fees for models that support vision.
Turn On to include image fees.
Applies cached input rates where this catalog lists them (OpenAI, Anthropic, Google, …). Models without a cached rate keep list pricing.
Add markup for client pricing
Model hidden reasoning / extended thinking charged like output tokens when enabled.
Enable for 50% off input & output
Get notified when cost exceeds limit
Estimated monthly
≈ $2,400.00 over 12 months if spend stayed flat (no growth or price changes).
List (catalog)
$2.50 in
$10.00 out
per 1M tokens
This scenario
$2.50 in
$10.00 out
effective $/1M
Share of this month
Bars use your current request and token settings. The right chart contrasts published list pricing with your effective rates after cache, batch, and related toggles.
By category
Input, output, and vision for this workload.
List vs optimized (monthly)
Total monthly at list ratecard vs your scenario.
Month n = n × estimated monthly bill — no seasonality or usage growth.
One roll-up of the four axes below. Open the technical note at the bottom for how these indices are derived.
Composite
Catalog benchmark · 0–100 per row
General knowledge & logic (MMLU-style)
Broad reasoning proxy for comparing model families — not a literal MMLU leaderboard value.
Coding & agents (HumanEval-style)
Coding and tool-use suitability from provider tier and model-id hints, not a fresh code benchmark.
Instruction following
How tightly the model tends to follow complex instructions in our catalog benchmark.
Math & reasoning depth
Numeric and reasoning tilt; boosted for reasoning-first ids in the catalog where applicable.
Same model as above, shown as a radar with a grey industry-average shadow. Axes are normalized in this view, not absolute benchmark percentiles.
Benchmark scan pending — live OpenRouter pricing is synced; scores populate after autonomous research.
Prompt vs catalog window
8,000 input tokens of 128,000 max. Confirm hard output caps in the vendor console.
6.3% of catalog window
TPS speed index
0 /100
≈ 25 TPS display estimate — not measured from your traffic.
US = 100 baseline. Values are a deterministic illustration from model id and provider tier, not ping or routing from your network.
United States
Baseline edge (illustrative)
Canada
Typical North America variance
Australia
Long-haul hint vs US edge
Architecture
Dense
MoE vs dense inferred from catalog / id.
Deployment
Managed API (cloud)
Tools and modalities
Tools / function calling (Strong)
Multimodal text + images (vision-capable in catalog)
JSON mode
Yes (typical API)
Audio (id hint)
No strong id hint
Nothing here is a live latency measurement, SLO, or inventory of your deployment. Use vendor dashboards and your own traces for TTFT, tokens per second under load, and regional routing.
Est. API spend
/ month at these sliders
Strongest scenario
Chatbot Arena
Highest fit index right now
Evaluate if GPT-4o meets your production requirements based on your token volume and active features above. What follows folds those same sliders into pricing and capability signals—value for spend, a concise ROI read, and four mapped scenarios—so you can stress-test this pick without re-entering inputs.
Value for spend
Higher usually means more catalog intelligence per dollar at your effective token prices — for comparisons inside this tool only.
Our one-line read
ROI Verdict: GPT-4o — At your effective token prices this scenario sits in a mid-market band. On the same catalog benchmark 0–100 axes as the Model DNA chart, GPT-4o reads as balanced general-purpose performance without a single dominant pillar. Stress-test against complex agents, multimodal apps, and enterprise integrations on openai if that mirrors your product.
Figures mirror the calculator above. Treat as orientation: confirm with your own benchmarks, regions, and contract discounts before you commit budget.
Each card shows a fit score (0–100) for a typical workload shape. Scan the bars, then read the lane that sounds like your product.
39
fit
GPT-4o in chatbot arena matchups
Tuned for low-latency product UX versus o-series reasoning models. For chatbot arenas, pricing on output tokens matters most when replies are long — GPT-4o is usable across tiers if you cap completion length.
8
fit
GPT-4o in coding & agent workflows
GPT-4o handles coding workloads with a low coding index (0/100 on the same heuristic axis as the DNA radar) — Complex agents, multimodal apps, and enterprise integrations on OpenAI
12
fit
GPT-4o on long documents & RAG
Context window 128,000 tokens frames how much GPT-4o can hold per call — pair chunking with complex agents, multimodal apps, and enterprise integrations on openai.
8
fit
GPT-4o on structured extraction
Heuristic math/logic blend suggests GPT-4o for light-to-moderate extraction — always validate on your schema.
Fit indices mix catalog intelligence with your effective prices; incompatible Vision or non-native Deep reasoning toggles zero or heavily discount lanes, matching the compare value engine. The efficiency ring blends the same template weights — orientation only, not a vendor benchmark.
Workload: Custom Configuration
26
Overall Intelligence Score
Scores below 70 indicate elevated delivery risk for this workload profile — proceed with a controlled pilot or evaluate alternatives with a stronger fit before commitment.
Monthly spend mix — use the split to prioritize where you optimize first.
Pros
Cons
Compare with alternatives
Improve model–workload alignment
With your current settings, GPT-4o may underdeliver on this workload. Shortlist models with better capability match—then confirm with list pricing, batch discounts, and side‑by‑side API cost analysis.
Choosing a better‑aligned LLM API reduces failed generations, rework, and runaway inference spend on high‑volume traffic.
Best for: High-volume simple chat, drafting, and cost experiments
Best for: Hard problems where correctness beats speed
Best for: Massive context RAG, long video/text analysis, and research dumps
Best for: High-volume assistants, extraction, and cost-gated features
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Explore comparisons
Same calculator layout, tailored copy and FAQ for each pair.
Building AI apps is exciting. But AI API costs can get out of control fast. You need a reliable AI API cost estimator. Our tool helps you predict your monthly LLM expenses. You can compare OpenAI pricing with Anthropic cost. You can see if DeepSeek is cheaper than Google Gemini. We make AI pricing simple.
AI models charge by the token. A token is just a piece of a word. It is hard to guess how many tokens your app will use. If your app goes viral, your API bill will spike. You need to plan ahead. An LLM pricing calculator lets you test different scenarios. You can see what happens if you get 10,000 users. You can see the cost of long conversations. This stops bill shock. It helps you set the right price for your own software.
Every AI provider splits costs into two parts. These are input tokens and output tokens. Input tokens are what you send to the AI. This includes your prompt. It includes any documents you upload. It includes the system instructions. Output tokens are what the AI sends back. This is the generated text. Output tokens are always more expensive. They take more compute power to generate. Our AI API cost estimator calculates both automatically.
OpenAI is the most popular AI provider. Their pricing changes often. GPT-4o is their flagship model. It is fast and smart. But it is not the cheapest. GPT-4o-mini is much cheaper. It costs a fraction of the price. It is great for simple tasks. Then there are the reasoning models. The o1 and o3-mini models think before they speak. They use hidden reasoning tokens. This makes them more expensive per request. You must factor this into your OpenAI pricing estimates.
Anthropic makes the Claude models. Claude 3.5 Sonnet is a favorite among developers. It is amazing at coding. The Anthropic cost structure is very competitive. Claude 3.5 Haiku is their fastest model. It is very cheap. It is perfect for reading large documents. Claude 3.5 Opus is their largest model. It is very expensive. You should only use Opus for very hard problems. Our calculator lets you compare Claude vs GPT-4o side by side.
Google Gemini has a massive context window. You can send it millions of tokens. You can upload whole books. You can upload hour-long videos. But filling that context window costs money. Gemini 1.5 Pro is their best model. Gemini 1.5 Flash is their fast and cheap model. Google also charges different rates for prompts under 128k tokens versus over 128k tokens. Our AI API cost estimator handles this complex math for you.
DeepSeek shocked the AI world. DeepSeek V3 is incredibly smart. DeepSeek R1 is an amazing reasoning model. And their API costs are tiny. They are much cheaper than OpenAI. Many developers are switching to DeepSeek to save money. You can also use open source models like Llama 3. You can run them on providers like Together AI or Groq. These providers charge very little per million tokens. If cost is your main worry, look at these options.
Many models can look at images. This is called vision. But images are not free. AI providers turn images into tokens. A high-resolution image might cost 1,000 tokens. A low-resolution image might cost 85 tokens. If your app processes thousands of images, the cost adds up fast. Our calculator includes a vision cost estimator. Just tell it how many images you process. It will add the vision cost to your total API bill.
Do you need answers right now? If not, you can save 50%. OpenAI and Anthropic offer Batch APIs. You send them a big file of requests. They process it within 24 hours. In exchange, they cut the price in half. This is perfect for data extraction. It is great for summarizing old articles. It is ideal for translating large databases. Always use batch pricing for offline tasks. Our calculator has a toggle for batch discounts.
A context window is the AI's short-term memory. It is how much text it can read at once. Early models had small windows. Now, models can read millions of tokens. But there is a trap. Every time you ask a question, you pay for the whole context window again. If you have a long chat, the input gets bigger every turn. The cost grows exponentially. You must manage your context window. Do not send the whole chat history if you do not need it.
Prompt caching is a game changer. Anthropic and OpenAI now offer it. If you send the same big document twice, you get a discount. The AI remembers the document. It does not have to read it from scratch. This drops input costs by 50% to 80%. It also makes the AI answer much faster. If you build chatbots over large PDFs, prompt caching is mandatory. Our AI API cost estimator factors in cached token discounts automatically.
Do you build AI apps for clients? You need to charge them for API usage. You cannot eat the cost yourself. Many agencies add a markup. If the API costs $100, they charge the client $150. This covers server costs and provides profit. Our calculator has an Agency Mode. You can type in your markup percentage. It will show you your cost, the client's price, and your total profit. You can even export a PDF report to show your client.
Do not just pick the smartest model. Pick the right model for the job. Use GPT-4o or Claude 3.5 Sonnet for hard coding tasks. Use GPT-4o-mini or Claude 3.5 Haiku for simple text sorting. Use DeepSeek V3 if you want smart answers on a tight budget. Run tests. See if the cheap model can do the job. If it can, use it. You will save thousands of dollars a year.
Estimating is just the start. You must track your real usage. Set hard limits in your OpenAI or Anthropic dashboard. If you do not set limits, a bug in your code could cost you a fortune. Use tools like Helicone or Langfuse. They track every single request. They show you which users cost the most money. They help you find bad prompts that waste tokens. Always monitor your live API costs.
AI is getting cheaper. Every few months, a new model drops the price. What costs $10 today might cost $1 next year. But we are also using AI for harder tasks. We are building AI agents that run in loops. An agent might make 50 API calls to solve one problem. So while the cost per token goes down, your total token usage will go up. You will always need an AI API cost estimator to stay on budget.
Do you sell AI services? Your clients probably ask about costs. You can embed this exact calculator on your own website. It is fully white-label. You can change the colors to match your brand. You can use it to capture leads. When a client calculates their cost, they enter their email to get the report. You get a new qualified B2B lead. It is the best way to sell AI development services.
Many beginners misunderstand tokens. A token is not a word. A token is a chunk of characters. In English, one token is about four characters. So, 100 tokens is about 75 words. But this changes for other languages. Spanish or French might use more tokens per word. Languages like Japanese or Chinese use even more. Coding languages also use tokens differently. Spaces, brackets, and symbols all count. If you write code, your token count will be higher than plain text. You must remember this when using an AI API cost estimator. If your app serves non-English users, your API costs will be higher. You need to budget for this difference.
Every AI chatbot has a system prompt. This is the hidden instruction set. It tells the AI how to behave. It might say, "You are a helpful assistant. Do not use bad words." This system prompt is sent with every single user message. If your system prompt is 500 tokens long, you pay for those 500 tokens every time a user says "Hello". If you have 10,000 users saying "Hello", you pay for 5,000,000 tokens just for the system prompt. This is a massive hidden cost. You must keep your system prompts short and efficient. Do not write a novel. Write clear, tight rules. This will lower your OpenAI pricing and Anthropic cost.
RAG is very popular. It lets AI read your private data. When a user asks a question, your app searches your database. It finds the right document. It sends that document to the AI. Then the AI answers the question. RAG is great for accuracy. But it is terrible for API costs. You are sending huge chunks of text to the AI every time. If you send 5 pages of text for every question, your input token usage will explode. Our LLM pricing calculator helps you model RAG costs. Just set your average input tokens to a high number, like 5,000. You will quickly see why you need to optimize your search results. Only send the AI the exact paragraphs it needs.
You can teach an AI new tricks in two ways. You can use a long prompt. Or you can fine-tune the model. A long prompt costs money every time you use it. Fine-tuning costs money upfront. You pay to train the model on your data. But after training, the model is yours. You do not need a long prompt anymore. The input costs go down. However, fine-tuned models often have a higher cost per token than base models. You must do the math. Our AI API cost estimator can help. Compare the cost of a long prompt on a cheap model versus a short prompt on a fine-tuned model. Usually, fine-tuning only saves money if you have massive volume.
AI agents are the future. An agent does not just answer a question. It takes action. It searches the web. It writes code. It runs the code. If the code fails, it tries again. This is called a loop. Loops are very dangerous for your budget. An agent might make 20 API calls to finish one task. Each call has input and output tokens. The context window grows with every step. A single task could cost $1.00 instead of $0.01. You must put hard stops on your agents. Tell them to stop after 5 tries. Use an LLM token calculator to estimate the worst-case scenario. Never let an agent run forever.
The AI war is heating up. OpenAI was the king. Now DeepSeek is challenging them. DeepSeek models are incredibly cheap. They cost a fraction of GPT-4o. Many developers are running A/B tests. They send the same prompt to OpenAI and DeepSeek. If DeepSeek gives a good answer, they use it. This saves massive amounts of money. But DeepSeek is hosted in China. Some enterprise clients do not allow this. They require data to stay in the US or Europe. In that case, you must pay the higher OpenAI pricing. Always check your client's data privacy rules before picking the cheapest API.
If you build an AI SaaS, how do you charge your users? You have three choices. First, a flat monthly fee. This is risky. Power users will drain your profits. Second, a credit system. Users buy 1,000 credits for $10. Each AI action costs 1 credit. This is safe and profitable. Third, bring your own key (BYOK). Users paste their own OpenAI API key into your app. They pay OpenAI directly. You just charge for your software. This is the safest method. Use our AI pricing calculator to figure out your exact costs. Then build your pricing model around those numbers. Never guess your margins.
We talked about RAG earlier. RAG requires search. To search text with AI, you need embeddings. An embedding model turns text into numbers. This lets computers find similar text fast. Embedding models are very cheap. OpenAI's text-embedding-3-small costs almost nothing. But if you embed millions of documents, the cost adds up. You only pay this cost once per document. After it is embedded, it lives in your vector database. Do not forget to add embedding costs to your total AI budget. Our calculator focuses on generative models, but embeddings are a hidden piece of the puzzle.
Look at any AI pricing page. Output tokens always cost more than input tokens. Why? It is about how GPUs work. Reading input tokens is fast. The GPU can process them all at once in parallel. Generating output tokens is slow. The GPU must generate them one by one. It predicts the first word. Then it predicts the second word based on the first. This takes much more time and energy. This is why you should ask the AI to be concise. If you do not need a long answer, tell the AI to keep it short. "Answer in one sentence." This simple prompt trick will slash your output token costs.
Everything you need to know about AI API pricing
Model DNA radar chart for selected models
Axes: Price · Logic · Coding · Context · Speed · Multimodal · Openness. Openness = rough “how open/hostable” hint from provider family, not a license statement.