@kayseqalbitaajir: 👏

Kayse Qalbi Taajir

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Saturday 10 October 2026 15:05:37 GMT

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qureeshta magalacad❣️🤍💋💕 :

🥰🥰🥰

2026-10-10 19:46:03

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Habi :

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2026-10-10 15:48:05

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Boqorada qoraxey1 :

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2026-10-10 15:47:37

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samawade boy :

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2026-10-10 15:36:38

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Boqorada garxajis G36🇮🇷 :

😂😂😂😂😂😂😂😂

2026-10-10 15:34:08

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ayaan Toosane :

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2026-10-10 15:16:49

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umu sumaya :

❤️❤️❤️

2026-10-10 15:14:30

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𝐒𝐮𝐧𝐝𝐮𝐬 𝐜𝐚𝐝𝐞𝐲 :

💞💞💞

2026-10-10 15:12:39

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ar banandagy :

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2026-10-10 15:10:24

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najaad :

[Heartwarming][Heartwarming][Heartwarming]

2026-10-10 20:09:14

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Team da dầu mụn đâu rùi !! #kemchongnang #kiemdau #hoptaccung #sahemul #dadau

Team da dầu mụn đâu rùi !! #kemchongnang #kiemdau #hoptaccung #sahemul #dadau

A lot of people use ChatGPT, Claude, Gemini or Kimi every day, but still do not fully understand what a token actually is, why input tokens and output tokens are billed separately, or why one prompt suddenly feels cheap while another one burns through your quota fast 💸⚙️ That is exactly why I made this post. In simple terms, tokens are the small chunks of text an AI model reads and writes. Your prompt uses input tokens. The model’s reply uses output tokens. If you paste a massive document, add lots of instructions, upload long text, ask for chain reasoning, or request a very detailed answer, your token usage climbs quickly 📈 If the model has to think more, search more, or generate a longer response, that can increase usage even more. This is where so many people get caught out 😅 They see “1M context window” or “200K context window” and assume that means unlimited prompting. It does not. Context window is the total space the model can handle across the conversation, including your prompt, prior chat history, system instructions, tool outputs, and the reply. Then you also have output caps, which limit how much the model can return in one answer 🧠📚 I also wanted this post to clear up one of the biggest practical questions: how do you estimate token usage before you even hit send? The honest answer is you will never predict it perfectly, but you can get close. Short plain-English prompts usually stay light. Large PDFs, pasted code, huge tables, long back-and-forth chats, and “give me a very detailed response” style prompts usually cost far more. If you want to stay efficient, tighten your prompt, reduce unnecessary context, and ask for the exact format you need 🎯 That is also why comparing model token limits matters. Different platforms give you different context sizes, output caps, pricing, and behaviour. OpenAI, Claude, Gemini and Kimi all have different trade-offs when it comes to long context, pricing efficiency, reasoning depth, and how much work they do before answering 🔍💡 So if you are building workflows, automations, AI agents, or even just trying to control costs, understanding tokens is not optional anymore. It is one of the core skills. If you are serious about AI, stop thinking only in prompts and start thinking in token budgets, context management, and output control 🛠️ That shift alone will make you better at using every major model. Save this post so you can come back to it later 📌 Send it to someone who keeps asking why their AI bill is so high 💸 Share it with anyone learning prompt engineering, automation, or local AI setups. And if you want more plain-English UK tech content on AI models, tools, pricing, workflows, cloud, cybersecurity and PC tech, follow TechSerks 🇬🇧🚀 Which platform confuses you the most when it comes to tokens… OpenAI, Claude, Gemini or Kimi? Drop it in the comments 👇 If you want, I can do a follow-up post next on: “How API pricing actually works” 💰 “How context windows really work” 🧠 or “How to write better prompts with fewer tokens” ✍️🔥 Comment “TOKENS” if you want part 2 👇🚀

A lot of people use ChatGPT, Claude, Gemini or Kimi every day, but still do not fully understand what a token actually is, why input tokens and output tokens are billed separately, or why one prompt suddenly feels cheap while another one burns through your quota fast 💸⚙️ That is exactly why I made this post. In simple terms, tokens are the small chunks of text an AI model reads and writes. Your prompt uses input tokens. The model’s reply uses output tokens. If you paste a massive document, add lots of instructions, upload long text, ask for chain reasoning, or request a very detailed answer, your token usage climbs quickly 📈 If the model has to think more, search more, or generate a longer response, that can increase usage even more. This is where so many people get caught out 😅 They see “1M context window” or “200K context window” and assume that means unlimited prompting. It does not. Context window is the total space the model can handle across the conversation, including your prompt, prior chat history, system instructions, tool outputs, and the reply. Then you also have output caps, which limit how much the model can return in one answer 🧠📚 I also wanted this post to clear up one of the biggest practical questions: how do you estimate token usage before you even hit send? The honest answer is you will never predict it perfectly, but you can get close. Short plain-English prompts usually stay light. Large PDFs, pasted code, huge tables, long back-and-forth chats, and “give me a very detailed response” style prompts usually cost far more. If you want to stay efficient, tighten your prompt, reduce unnecessary context, and ask for the exact format you need 🎯 That is also why comparing model token limits matters. Different platforms give you different context sizes, output caps, pricing, and behaviour. OpenAI, Claude, Gemini and Kimi all have different trade-offs when it comes to long context, pricing efficiency, reasoning depth, and how much work they do before answering 🔍💡 So if you are building workflows, automations, AI agents, or even just trying to control costs, understanding tokens is not optional anymore. It is one of the core skills. If you are serious about AI, stop thinking only in prompts and start thinking in token budgets, context management, and output control 🛠️ That shift alone will make you better at using every major model. Save this post so you can come back to it later 📌 Send it to someone who keeps asking why their AI bill is so high 💸 Share it with anyone learning prompt engineering, automation, or local AI setups. And if you want more plain-English UK tech content on AI models, tools, pricing, workflows, cloud, cybersecurity and PC tech, follow TechSerks 🇬🇧🚀 Which platform confuses you the most when it comes to tokens… OpenAI, Claude, Gemini or Kimi? Drop it in the comments 👇 If you want, I can do a follow-up post next on: “How API pricing actually works” 💰 “How context windows really work” 🧠 or “How to write better prompts with fewer tokens” ✍️🔥 Comment “TOKENS” if you want part 2 👇🚀

حصل في فرنسا سبحان الله

حصل في فرنسا سبحان الله

Ninh Bình ơi #travel #ninhbinh #dulichninhbinh #ninhbinhtrip #dulichvietnam

Ninh Bình ơi #travel #ninhbinh #dulichninhbinh #ninhbinhtrip #dulichvietnam

Unfreeze account 🥹🥀😩# Unfreeze #grow account # poryoupage #fyp 💔🥺 پر محبوب نے جب محبتیں منہ پر ماری تو رونا اگیا @🥺🥺😭

Unfreeze account 🥹🥀😩# Unfreeze #grow account # poryoupage #fyp 💔🥺 پر محبوب نے جب محبتیں منہ پر ماری تو رونا اگیا @🥺🥺😭

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