{"id":1245,"date":"2026-06-22T17:35:50","date_gmt":"2026-06-22T17:35:50","guid":{"rendered":"https:\/\/convly.ai\/ai-api-cost-calculator\/"},"modified":"2026-08-03T05:00:14","modified_gmt":"2026-08-03T05:00:14","slug":"ai-api-cost-calculator","status":"publish","type":"page","link":"https:\/\/convly.ai\/pt\/ai-api-cost-calculator\/","title":{"rendered":"Calculadora de Custos de API de IA \u2014 Compare os Pre\u00e7os dos LLMs por Uso Mensal"},"content":{"rendered":"<p>How much will the OpenAI, Anthropic, Google or DeepSeek API actually cost you each month? Enter your usage and compare every model side by side \u2014 pricing is pulled live from our <a href=\"\/models\/\">AI models database<\/a>.<\/p>\n<div class=\"acc\" id=\"acc\">\n  <div class=\"acc-controls\">\n    <label>Input tokens \/ month (millions)<input type=\"number\" id=\"acc-in\" value=\"50\" min=\"0\" step=\"1\"><\/label>\n    <label>Output tokens \/ month (millions)<input type=\"number\" id=\"acc-out\" value=\"10\" min=\"0\" step=\"1\"><\/label>\n    <div class=\"acc-hint\">Tip: a typical chatbot turn is ~1K in \/ ~0.5K out. 50M in \/ 10M out \u2248 ~50,000 such turns a month.<\/div>\n  <\/div>\n  <table class=\"acc-table\">\n    <thead><tr><th>Model<\/th><th>Developer<\/th><th>$\/1M in<\/th><th>$\/1M out<\/th><th>Est. monthly cost<\/th><\/tr><\/thead>\n    <tbody id=\"acc-body\"><\/tbody>\n  <\/table>\n  <p class=\"acc-note\">Estimate = (input&nbsp;M \u00d7 input price) + (output&nbsp;M \u00d7 output price). Real bills vary with caching, batch discounts, and long-context surcharges. Open-weight models you self-host aren't listed here (their cost is your hardware\/electricity, not per-token).<\/p>\n<\/div>\n<script>(function(){\n  var DATA=[{\"name\":\"Claude Fable 5\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-fable-5\\\/\",\"dev\":\"Anthropic\",\"in\":10,\"out\":50},{\"name\":\"Claude Haiku 4.5\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-haiku-4-5\\\/\",\"dev\":\"Anthropic\",\"in\":1,\"out\":5},{\"name\":\"Claude Opus 4.8\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-opus-4-8\\\/\",\"dev\":\"Anthropic\",\"in\":5,\"out\":25},{\"name\":\"Claude Opus 5\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-opus-5\\\/\",\"dev\":\"Anthropic\",\"in\":5,\"out\":25},{\"name\":\"Claude Sonnet 4.6\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-sonnet-4-6\\\/\",\"dev\":\"Anthropic\",\"in\":3,\"out\":15},{\"name\":\"Claude Sonnet 5\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/claude-sonnet-5\\\/\",\"dev\":\"Anthropic\",\"in\":2,\"out\":10},{\"name\":\"DeepSeek R1\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/deepseek-r1\\\/\",\"dev\":\"DeepSeek\",\"in\":0.5,\"out\":2.15},{\"name\":\"DeepSeek R1 Distill Llama 70B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/deepseek-r1-distill-llama-70b\\\/\",\"dev\":\"DeepSeek\",\"in\":0.8,\"out\":0.8},{\"name\":\"DeepSeek V4-Flash\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/deepseek-v4-flash\\\/\",\"dev\":\"DeepSeek\",\"in\":0.14,\"out\":0.28},{\"name\":\"DeepSeek V4-Pro\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/deepseek-v4-pro\\\/\",\"dev\":\"DeepSeek\",\"in\":0.435,\"out\":0.87},{\"name\":\"Gemini 2.5 Pro\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemini-2-5-pro\\\/\",\"dev\":\"Google (Google DeepMind)\",\"in\":1.25,\"out\":10},{\"name\":\"Gemini 3.1 Pro\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemini-3-1-pro\\\/\",\"dev\":\"Google\",\"in\":2,\"out\":12},{\"name\":\"Gemini 3.5 Flash\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemini-3-5-flash\\\/\",\"dev\":\"Google\",\"in\":1.5,\"out\":9},{\"name\":\"Gemini 3.6 Flash\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemini-3-6-flash\\\/\",\"dev\":\"Google\",\"in\":1.5,\"out\":7.5},{\"name\":\"Gemma 3 12B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemma-3-12b\\\/\",\"dev\":\"Google\",\"in\":0.05,\"out\":0.15},{\"name\":\"Gemma 3 27B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemma-3-27b\\\/\",\"dev\":\"Google\",\"in\":0.08,\"out\":0.16},{\"name\":\"Gemma 3 4B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gemma-3-4b\\\/\",\"dev\":\"Google\",\"in\":0.05,\"out\":0.1},{\"name\":\"GLM 5.2\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/glm-5-2\\\/\",\"dev\":\"Zhipu AI\",\"in\":1.4,\"out\":4.4},{\"name\":\"GPT-5.5\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gpt-5-5\\\/\",\"dev\":\"OpenAI\",\"in\":5,\"out\":30},{\"name\":\"GPT-5.6 Sol\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/gpt-5-6-sol\\\/\",\"dev\":\"OpenAI\",\"in\":5,\"out\":30},{\"name\":\"Grok 4\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/grok-4\\\/\",\"dev\":\"xAI\",\"in\":3,\"out\":15},{\"name\":\"Kimi K2.7 Code\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/kimi-k2-7-code\\\/\",\"dev\":\"Moonshot AI\",\"in\":0.6,\"out\":2.5},{\"name\":\"Kimi K3\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/kimi-k3\\\/\",\"dev\":\"Moonshot AI\",\"in\":3,\"out\":15},{\"name\":\"Llama 3.1 8B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/llama-3-1-8b\\\/\",\"dev\":\"Meta\",\"in\":0.02,\"out\":0.03},{\"name\":\"Llama 3.3 70B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/llama-3-3-70b\\\/\",\"dev\":\"Meta\",\"in\":0.1,\"out\":0.32},{\"name\":\"Llama 4 Maverick\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/llama-4-maverick\\\/\",\"dev\":\"Meta\",\"in\":0.15,\"out\":0.6},{\"name\":\"Llama 4 Scout\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/llama-4-scout\\\/\",\"dev\":\"Meta\",\"in\":0.1,\"out\":0.3},{\"name\":\"Mistral 7B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/mistral-7b\\\/\",\"dev\":\"Mistral AI\",\"in\":0.02,\"out\":0.03},{\"name\":\"Mistral Large 3\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/mistral-large-3\\\/\",\"dev\":\"Mistral AI\",\"in\":2,\"out\":6},{\"name\":\"Mistral NeMo 12B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/mistral-nemo-12b\\\/\",\"dev\":\"Mistral AI\",\"in\":0.02,\"out\":0.04},{\"name\":\"Phi-4\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/phi-4\\\/\",\"dev\":\"Microsoft\",\"in\":0.07,\"out\":0.14},{\"name\":\"Qwen3 14B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/qwen3-14b\\\/\",\"dev\":\"Alibaba\",\"in\":0.12,\"out\":0.24},{\"name\":\"Qwen3 235B-A22B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/qwen3-235b-a22b\\\/\",\"dev\":\"Alibaba\",\"in\":0.45,\"out\":1.8},{\"name\":\"Qwen3 30B-A3B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/qwen3-30b-a3b\\\/\",\"dev\":\"Alibaba\",\"in\":0.12,\"out\":0.5},{\"name\":\"Qwen3 32B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/qwen3-32b\\\/\",\"dev\":\"Alibaba\",\"in\":0.08,\"out\":0.28},{\"name\":\"Qwen3 8B\",\"url\":\"https:\\\/\\\/convly.ai\\\/pt\\\/model\\\/qwen3-8b\\\/\",\"dev\":\"Alibaba\",\"in\":0.04,\"out\":0.14}];\n  var $=function(id){return document.getElementById(id);};\n  function render(){\n    var im=parseFloat($('acc-in').value)||0, om=parseFloat($('acc-out').value)||0;\n    var list=DATA.map(function(m){return {m:m,cost:im*m.in+om*m.out};}).sort(function(a,b){return a.cost-b.cost;});\n    var body=$('acc-body'); body.innerHTML='';\n    list.forEach(function(x,i){\n      var tr=document.createElement('tr'); if(i===0)tr.className='acc-best';\n      tr.innerHTML='<td><a href=\"'+x.m.url+'\">'+x.m.name+'<\/a>'+(i===0?' <span class=\"acc-tag\">cheapest<\/span>':'')+'<\/td>'+\n        '<td>'+(x.m.dev||'\u2014')+'<\/td><td>$'+x.m.in.toFixed(2)+'<\/td><td>$'+x.m.out.toFixed(2)+'<\/td>'+\n        '<td><b>$'+x.cost.toLocaleString(undefined,{maximumFractionDigits:2})+'<\/b>\/mo<\/td>';\n      body.appendChild(tr);\n    });\n  }\n  ['acc-in','acc-out'].forEach(function(id){$(id).addEventListener('input',render);});\n  render();\n})();<\/script>\n\n<p>The cheapest model isn&#8217;t always the best fit \u2014 check each model&#8217;s page for context window, benchmarks and capabilities before you switch. And remember: open-weight models you self-host have no per-token cost at all.<\/p>\n<p><!--geo-block--><\/p>\n<h2>How much does an LLM API cost, and how do you calculate it?<\/h2>\n<p>AI API pricing is charged by the token, billed separately for input (your prompt plus any context you send) and output (the model&#8217;s reply), and quoted per million tokens. To estimate a bill you multiply your input tokens by the input price, add your output tokens multiplied by the output price, then scale by how many requests you run per month. In 2026 input pricing runs roughly $0.10\u2013$5 per million tokens and output roughly $0.30\u2013$30 per million, with output almost always costing more than input \u2014 typically 2\u20135\u00d7 (a median of about 4\u00d7).<\/p>\n<ul>\n<li><strong>The formula:<\/strong> monthly cost \u2248 (requests\/month \u00d7 avg input tokens \u00d7 input price \u00f7 1,000,000) + (requests\/month \u00d7 avg output tokens \u00d7 output price \u00f7 1,000,000).<\/li>\n<li><strong>Token-to-word rule:<\/strong> about 1.3 tokens per English word, so 1,000 tokens \u2248 750 words and 1 million tokens \u2248 750,000 words.<\/li>\n<li><strong>Price spread:<\/strong> budget\/&#8221;lite&#8221; models sit near $0.10\u2013$0.15 per million input tokens; frontier flagship models reach roughly $5 input and $25\u2013$30 output.<\/li>\n<li><strong>Open-weight models self-hosted have no per-token fee<\/strong> \u2014 you pay for the GPU, power and hosting instead.<\/li>\n<\/ul>\n<h2>Frequently asked questions<\/h2>\n<h3>How do I calculate the monthly cost of an AI API?<\/h3>\n<p>Take your average input tokens and output tokens per request, multiply each by that model&#8217;s per-million price (input price and output price are separate), add them together for a per-request cost, then multiply by your monthly request volume. For example, 100,000 requests a month at 1,000 input and 500 output tokens each is 100M input and 50M output tokens; at $1\/M input and $4\/M output that is $100 + $200 = $300 a month.<\/p>\n<h3>Why do output tokens cost more than input tokens?<\/h3>\n<p>Input tokens are processed in a single parallel pass, whereas each output token requires its own full forward pass through the model, so it is far more compute-intensive to generate text than to read it. That is why output is usually priced 2\u20135\u00d7 higher than input, with a typical ratio around 4\u00d7. When you estimate a bill, weight output tokens accordingly rather than assuming input and output cost the same.<\/p>\n<h3>What does &#8220;cost per 1M tokens&#8221; actually mean?<\/h3>\n<p>Providers quote prices per one million tokens rather than per request because token counts vary so much between calls. A token is roughly \u00be of an English word, so one million tokens is about 750,000 words \u2014 the length of a long book. To get your real cost, divide the quoted per-million price by 1,000,000 and multiply by the exact number of tokens you send and receive.<\/p>\n<h3>What is the cheapest LLM API in 2026?<\/h3>\n<p>The lowest-cost options are the &#8220;flash&#8221;, &#8220;lite&#8221; and open-model APIs, which land around $0.10\u2013$0.15 per million input tokens and roughly $0.30\u2013$0.60 per million output \u2014 often 30\u201350\u00d7 cheaper than frontier flagship models at ~$5 input \/ $25\u2013$30 output. The best value is usually the cheapest model that still clears your quality bar, so match the model to the task rather than defaulting to the most expensive one. Because prices shift most quarters, use the live figures in the calculator above before committing to volume.<\/p>\n<h3>Do open-source or open-weight models have a per-token API cost?<\/h3>\n<p>No \u2014 if you self-host an open-weight model (such as Llama, Qwen, DeepSeek or Mistral variants) there is no per-token API charge at all; you pay only for the GPU or server, electricity and maintenance. That flips the economics: self-hosting is a fixed hourly cost regardless of usage, so it only beats per-token API pricing at high, steady volume. At low or bursty volume a hosted API is almost always cheaper, since you pay for exactly the tokens you use.<\/p>\n<h3>How much VRAM do I need to self-host a model instead of paying per token?<\/h3>\n<p>At 4-bit quantisation a model needs roughly 0.5\u20130.6 GB of VRAM per billion parameters, plus extra for the KV cache that grows with context length. So an 8B model needs about 5\u20136 GB and fits a consumer GPU, a 70B model needs about 40\u201348 GB (a data-centre card or two 24 GB cards), and adding 15\u201320% headroom for KV cache and overhead is a safe rule of thumb. Longer context windows and larger batch sizes raise the KV-cache requirement, so size for your real prompt lengths, not just the weights.<\/p>\n<h3>How can I reduce my AI API bill?<\/h3>\n<p>The biggest levers are sending fewer tokens and choosing a cheaper model tier for easy tasks. Prompt caching can cut the cost of repeated system prompts or context by up to ~90%, and batch\/asynchronous endpoints commonly give around a 50% discount for non-urgent work. Trimming long context, capping max output length, and routing simple requests to a lite model while reserving flagship models for hard ones typically cut a bill by a large multiple.<\/p>\n<p><!--convly-tools--><br \/>\n<style>.ctools-wrap{margin:36px 0 10px;border-top:1px solid #e6e8ef;padding-top:22px}.ctools-h{font-weight:700;font-size:14px;color:#1a1a2e;margin:0 0 14px;text-transform:uppercase;letter-spacing:.05em}.ctools-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(210px,1fr));gap:12px}.ctool{display:flex;flex-direction:column;gap:6px;padding:15px 16px;border:1px solid #e6e8ef;border-radius:12px;background:#f8f9fb;text-decoration:none!important;transition:transform .15s,box-shadow .15s,border-color .15s}.ctool:hover{border-color:#6d28d9;background:#fff;box-shadow:0 8px 20px -10px rgba(109,40,217,.35);transform:translateY(-2px)}.ctool-i{display:inline-flex;align-items:center;justify-content:center;width:38px;height:38px;border-radius:10px;background:#f1ecfb;color:#6d28d9;margin-bottom:2px}.ctool:hover .ctool-i{background:#6d28d9;color:#fff}.ctool-t{font-weight:700;font-size:14.5px;color:#1a3ba3}.ctool-d{font-size:12.5px;color:#5a6472;line-height:1.4}<\/style><div class=\"ctools-wrap\"><p class=\"ctools-h\">More free tools from Convly<\/p><div class=\"ctools-grid\"><a class=\"ctool\" href=\"\/models\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><ellipse cx=\"12\" cy=\"5.5\" rx=\"7.5\" ry=\"3\"\/><path d=\"M4.5 5.5v6c0 1.66 3.36 3 7.5 3s7.5-1.34 7.5-3v-6\"\/><path d=\"M4.5 11.5v6c0 1.66 3.36 3 7.5 3s7.5-1.34 7.5-3v-6\"\/><\/svg><\/span><span class=\"ctool-t\">AI Models Database<\/span><span class=\"ctool-d\">30+ LLMs \u2014 specs, pricing &amp; context, side by side.<\/span><\/a><a class=\"ctool\" href=\"\/llm-leaderboard\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"9.5\" y=\"4\" width=\"5\" height=\"16\" rx=\"1\"\/><rect x=\"3\" y=\"10\" width=\"5\" height=\"10\" rx=\"1\"\/><rect x=\"16\" y=\"13\" width=\"5\" height=\"7\" rx=\"1\"\/><\/svg><\/span><span class=\"ctool-t\">LLM Leaderboard 2026<\/span><span class=\"ctool-d\">Rank every model by intelligence, price &amp; speed.<\/span><\/a><a class=\"ctool\" href=\"\/ai-api-cost-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"5\" y=\"3\" width=\"14\" height=\"18\" rx=\"2\"\/><path d=\"M8.5 7h7\"\/><path d=\"M8.5 11.5h.01M12 11.5h.01M15.5 11.5h.01M8.5 15h.01M12 15h.01M15.5 15h.01M8.5 18h.01M12 18h.01M15.5 18h.01\"\/><\/svg><\/span><span class=\"ctool-t\">AI API Cost Calculator<\/span><span class=\"ctool-d\">Compare what each model costs you per month.<\/span><\/a><a class=\"ctool\" href=\"\/llm-vram-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"6\" y=\"6\" width=\"12\" height=\"12\" rx=\"2\"\/><rect x=\"9.5\" y=\"9.5\" width=\"5\" height=\"5\" rx=\"1\"\/><path d=\"M9 3v3M15 3v3M9 18v3M15 18v3M3 9h3M3 15h3M18 9h3M18 15h3\"\/><\/svg><\/span><span class=\"ctool-t\">LLM VRAM Calculator<\/span><span class=\"ctool-d\">Can your GPU run that model locally? Find out.<\/span><\/a><a class=\"ctool\" href=\"\/self-hosting-vs-api-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><path d=\"M12 4v16\"\/><path d=\"M5 7h14\"\/><path d=\"M5 7l-2.5 6a3 3 0 0 0 5 0L5 7z\"\/><path d=\"M19 7l-2.5 6a3 3 0 0 0 5 0L19 7z\"\/><path d=\"M8.5 20h7\"\/><\/svg><\/span><span class=\"ctool-t\">Self-Hosting vs API<\/span><span class=\"ctool-d\">Buy a GPU or pay per token? See the break-even.<\/span><\/a><a class=\"ctool\" href=\"\/compare-ai-models-and-gpus-2026\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"3.5\" y=\"3.5\" width=\"12\" height=\"12\" rx=\"2.5\"\/><path d=\"M8.5 20.5h9.5a2.5 2.5 0 0 0 2.5-2.5V8.5\"\/><\/svg><\/span><span class=\"ctool-t\">Compare Models &amp; GPUs<\/span><span class=\"ctool-d\">Every model paired with the GPU that runs it.<\/span><\/a><a class=\"ctool\" href=\"\/ai-benchmarks\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><path d=\"M4 17a8 8 0 1 1 16 0\"\/><path d=\"M12 17l4.2-4.6\"\/><circle cx=\"12\" cy=\"17\" r=\"1.4\"\/><path d=\"M4 17h2M18 17h2M12 7V5\"\/><\/svg><\/span><span class=\"ctool-t\">AI Benchmarks Explained<\/span><span class=\"ctool-d\">What each benchmark measures, and who leads.<\/span><\/a><a class=\"ctool\" href=\"\/image-to-prompt\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"3.5\" y=\"4.5\" width=\"17\" height=\"15\" rx=\"2.5\"\/><circle cx=\"9\" cy=\"10\" r=\"1.6\"\/><path d=\"M3.5 16.5l4.7-4.2a1.8 1.8 0 0 1 2.4 0l5.9 5.2\"\/><path d=\"M14.5 14l1.9-1.7a1.8 1.8 0 0 1 2.4 0l1.7 1.5\"\/><\/svg><\/span><span class=\"ctool-t\">Image-to-Prompt<\/span><span class=\"ctool-d\">Turn any image into an editable AI prompt.<\/span><\/a><\/div><\/div><\/p>\n<div class=\"convly-chart-block\" data-chart=\"api-cost\">\n<div style=\"background:#ffffff;border:1px solid #e5e7eb;border-radius:12px;padding:22px 26px;margin:32px auto;max-width:820px;box-shadow:0 1px 3px rgba(15,23,42,.06);\">\n<p style=\"margin:0 0 14px;font-weight:700;font-size:15.5px;color:#0f172a;\">Blended API cost per 1M tokens \u2014 what AI models really cost<\/p>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Mistral 7B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:4.0%;background:#16a34a;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.02<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Llama 3.1 8B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:4.0%;background:#16a34a;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.02<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Mistral NeMo 12B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:5.8%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.03<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Gemma 3 4B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:18.6%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.06<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Qwen3 8B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:19.3%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.07<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Gemma 3 12B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:21.3%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.07<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Phi-4<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:23.5%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.09<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Gemma 3 27B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:25.3%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.10<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Qwen3 32B<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:29.0%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.13<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Llama 4 Scout<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:31.1%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$0.15<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Claude Sonnet 4.6<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:83.0%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$6.00<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Claude Opus 5<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:90.2%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$10.00<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Claude Opus 4.8<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:90.2%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$10.00<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">GPT-5.6 Sol<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:91.9%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$11.25<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">GPT-5.5<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:91.9%;background:#3b5bdb;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$11.25<\/span><\/div>\n<\/div>\n<div style=\"display:flex;align-items:center;gap:10px;margin:7px 0;\">\n<div style=\"flex:0 0 185px;text-align:right;font-size:13px;color:#334155;\">Claude Fable 5<\/div>\n<div style=\"flex:1;white-space:nowrap;\">\n<div style=\"width:100.0%;background:#ea580c;height:14px;border-radius:3px;display:inline-block;vertical-align:middle;max-width:82%;\"><\/div>\n<p> <span style=\"font-size:12.5px;font-weight:700;color:#0f172a;\">$20.00<\/span><\/div>\n<\/div>\n<p style=\"margin:14px 0 0;font-size:12.5px;color:#64748b;text-align:center;\">Blended cost = (3 \u00d7 input + output) \u00f7 4 \u00b7 10 cheapest + 6 priciest of 36 tracked, log scale \u00b7 full spread 909\u00d7 \u00b7 Green = best value, orange = highest. Updated Aug 03, 2026.<\/p>\n<\/div>\n<details style=\"margin:-18px auto 28px;max-width:820px;\">\n<summary style=\"cursor:pointer;font-size:13.5px;color:#6d28d9;font-weight:600;\">&#128203; Embed this chart on your site (free, with attribution)<\/summary>\n<p><textarea readonly style=\"width:100%;height:90px;font-size:12px;margin-top:8px;\">&lt;a href=&quot;https:\/\/convly.ai\/ai-api-cost-calculator\/&quot;&gt;&lt;img src=&quot;https:\/\/convly.ai\/wp-content\/uploads\/charts\/api-cost-blended.png&quot; alt=&quot;Chart: blended API cost per 1M tokens across AI models \u2014 909x spread&quot; style=&quot;max-width:100%&quot;&gt;&lt;\/a&gt;&lt;br&gt;Chart by &lt;a href=&quot;https:\/\/convly.ai\/ai-api-cost-calculator\/&quot;&gt;Convly.ai&lt;\/a&gt;<\/textarea><\/details>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>How much will the OpenAI, Anthropic, Google or DeepSeek API actually cost you each month? Enter your usage and compare [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-1245","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1245","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/page"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=1245"}],"version-history":[{"count":5,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1245\/revisions"}],"predecessor-version":[{"id":2092,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1245\/revisions\/2092"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=1245"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}