Token'lar, Bağlam Pencereleri ve Maliyet
Her turda neden tüm geçmişin gönderildiğini ve bunun maliyetini öğrenin.
Token'lar, Bağlam Pencereleri ve Maliyet, CoddyKit'te ücretsiz bir Claude Architect dersidir. Bu, 4 dersinin 4. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Claude Architect öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Claude Architect kursu toplamda 4 dersten oluşur.
Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.
Claude Has No Memory
Here is the most important idea in this lesson: the Claude API keeps NO state between turns.
The model does not remember your last message. Each API call starts fresh. So how do chatbots seem to remember?
You send the full conversation history in every single request. The messages field carries the whole back-and-forth, every time.
What a Request Carries
A Claude API request has a few key fields:
model— which Claude model to usemax_tokens— the cap on the reply lengthsystem— the system promptmessages— the FULL history every turntools/tool_choice— optional tool config
Notice messages grows over time. Turn 1 sends 1 message. Turn 10 sends all 19 prior messages plus the new one.
resp = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=1024,
system="You are a support agent.",
messages=[
{"role": "user", "content": "My order is late."},
{"role": "assistant", "content": "I can help. What is your order ID?"},
{"role": "user", "content": "It's #4821."},
],
)What Is a Token?
Models do not read characters or whole words. They read tokens — small chunks of text.
A rough guide: one token is about 4 characters of English, or roughly 3/4 of a word. "unhappiness" might split into "un", "happiness". Punctuation and spaces count too.
Tokens matter because you pay per token and the context window is measured in tokens, not words.
Input vs Output Tokens
Every request has two token counts that are billed differently:
- Input tokens — everything you send:
system+tools+ the fullmessageshistory. - Output tokens — what the model generates in its reply.
Output tokens usually cost more per token than input tokens. But because the full history is resent each turn, input tokens are what quietly balloon in long conversations.
The Context Window
The context window is the maximum number of tokens a model can handle in one request — input plus output combined.
If your full history plus the requested max_tokens exceeds the window, the request fails. The window is a hard ceiling, not a suggestion.
This is why long-running chats and big tool outputs eventually hit a wall: the resent history keeps growing toward the limit.
Cost Grows With History
Because you resend the whole history each turn, cost does not grow linearly with the conversation — it grows roughly with the square of its length.
Turn 1 bills a few tokens. Turn 20 bills all 19 prior turns again, plus the new one. A 10-message chat re-bills the early messages 10 times over its life.
For an architect, this means: a chatty agent that never trims its history is an expensive agent.
# Rough illustration of resent input growing each turn
history = []
for turn in range(1, 6):
history.append({"role": "user", "content": user_msg(turn)})
resp = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=512,
messages=history, # ENTIRE history resent every turn
)
history.append({"role": "assistant", "content": resp.content})
print("turn", turn, "input_tokens", resp.usage.input_tokens)Measure Before You Optimize
Every response includes a usage object reporting input_tokens and output_tokens. This is your ground truth for cost.
You can also count tokens before sending, so you can predict cost and check you are under the window — without paying for a full generation.
Rule of thumb: instrument token usage in production. Aggregate cost numbers hide which conversations or tool calls are the expensive ones.
count = client.messages.count_tokens(
model="claude-sonnet-4-5",
system="You are a support agent.",
messages=history,
)
print("input tokens before send:", count.input_tokens)
resp = client.messages.create(model="claude-sonnet-4-5", max_tokens=512, messages=history)
print("billed:", resp.usage.input_tokens, resp.usage.output_tokens)Tool Output Bloats Context
In agentic loops, tool results are appended to the history and resent on every following turn. A verbose tool that returns a 5,000-token JSON blob keeps costing you for the rest of the conversation.
The fix is to trim verbose tool output to the relevant fields before appending it. Do not store a whole API dump in context when three fields are all the model needs.
raw = lookup_order(order_id) # huge JSON
# Trim to what the model actually needs
tool_result = {
"order_id": raw["id"],
"status": raw["status"],
"eta": raw["estimated_delivery"],
}
history.append({
"role": "user",
"content": [{"type": "tool_result", "tool_use_id": tu_id,
"content": json.dumps(tool_result)}],
})Summarize to Stay in Budget
For long conversations, you can replace old turns with a compact progressive summary to keep the history small and under the window.
But beware: summarization makes numbers, percentages, and dates vague. The model rewrites "refund of $482.10 on 2026-03-14" into "a refund last spring."
The architect's fix: pull transactional facts into a separate verbatim "case facts" block kept outside the summary, so exact values never get blurred.
Lost in the Middle
A bigger context window is not a free pass. Models attend most strongly to the start and the end of the input, and least to the middle. This is the "lost-in-the-middle" effect.
So burying a critical instruction or fact in the middle of a giant history risks it being ignored — even though you paid full price to send it.
Keep key instructions and the current task near the edges; trim the bulky middle.
Batch API for Non-Blocking Jobs
One cost lever: the Message Batches API. It is about 50% cheaper than standard requests, with up to a 24-hour processing window.
The trade-offs: there is no latency SLA, and multi-turn tool calling is NOT supported. Use custom_id to correlate requests; re-submit only the failures.
Use Batch for overnight reports and large audits. Never use it for blocking, time-sensitive, or pre-merge checks — a user is waiting on those.
batch = client.messages.batches.create(requests=[
{"custom_id": "doc-001", "params": {
"model": "claude-sonnet-4-5", "max_tokens": 1024,
"messages": [{"role": "user", "content": classify(doc_1)}]}},
{"custom_id": "doc-002", "params": {
"model": "claude-sonnet-4-5", "max_tokens": 1024,
"messages": [{"role": "user", "content": classify(doc_2)}]}},
]) # ~50% cheaper, up to 24h, no latency SLAQuick Check
A production support chatbot's per-conversation cost is climbing fast as sessions get longer, even though each user reply is short. What is the primary cause, and the right architect-grade fix?
Recap: Tokens, Context & Cost
Key takeaways:
- The API keeps no state — you resend the full
messageshistory every turn. - Billing is per token, split into input (system + tools + history) and output.
- The context window caps input + output; resent history grows toward it and cost grows roughly with the square of conversation length.
- Measure with
usageandcount_tokens; trim verbose tool output to relevant fields. - Summarize to stay in budget, but keep exact numbers/dates in a verbatim case-facts block; watch the lost-in-the-middle effect.
- Batch API = ~50% cheaper for non-blocking jobs only — never for time-sensitive checks.
Yapay zeka eğitmeniyle Python öğren — ücretsiz
Tarayıcında gerçek kod yaz ve çalıştır, 7/24 yapay zeka eğitmeninden anında yardım al; web'de ya da uygulamada kaldığın yerden devam et.
- Kurslar
- 26
- Dersler
- 104
Sıkça Sorulan Sorular
“Token'lar, Bağlam Pencereleri ve Maliyet” dersi ücretsiz mi?
Evet — “Token'lar, Bağlam Pencereleri ve Maliyet” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Claude Architect kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Claude Architect kursu toplamda 4 dersten oluşur.
“Token'lar, Bağlam Pencereleri ve Maliyet” dersinde ne öğreneceğim?
Her turda neden tüm geçmişin gönderildiğini ve bunun maliyetini öğrenin. Claude Architect ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.
Claude Architect öğrenmeye başlamak için deneyim gerekli mi?
Önceden deneyim gerekmez. CoddyKit'te Claude Architect, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 4. dersidir.
“Token'lar, Bağlam Pencereleri ve Maliyet” dersi ne kadar sürer?
Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.
Bu Claude Architect dersinde kod yazıp çalıştırabilir miyim?
Evet. Her Claude Architect dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.
Bu kursun tüm dersleri
- Claude Model Ailesi
- API İsteğinin Anatomisi
- Durdurma Nedenleri Açıklaması
- Token'lar, Bağlam Pencereleri ve Maliyet