I burned through DeepSeek's 5M free tokens in 14 days — here's the exact math

#deepseek #llm #opensource #ai

I burned through DeepSeek's 5M free tokens in 14 days — here's the exact math

DeepSeek gives every new account 5,000,000 free API tokens on signup. No promo code. No credit card. Credits auto-apply the moment your phone is verified.

I signed up on March 27, 2026 and exhausted the balance on April 10 — 14 days. Average burn: ~357,000 tokens per day. That's about 446 chat-style API calls per day, or 6,250 calls total at typical 500-input / 300-output ratios.

What follows is the day-by-day breakdown, the three mistakes that wasted ~600K tokens (12% of the entire grant), and the four habits that would have stretched the same balance to a full month.

The accounting setup

I logged every API call's prompt_tokens and completion_tokens into a single SQLite table:

import sqlite3, json
from openai import OpenAI

db = sqlite3.connect("deepseek_usage.db")
db.execute("""
  CREATE TABLE IF NOT EXISTS calls (
    ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    model TEXT, prompt_tokens INT, completion_tokens INT,
    purpose TEXT
  )
""")

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key=os.environ["DEEPSEEK_API_KEY"]
)

def call(prompt, purpose, model="deepseek-chat", **kw):
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        **kw
    )
    u = r.usage
    db.execute(
        "INSERT INTO calls (model,prompt_tokens,completion_tokens,purpose) VALUES (?,?,?,?)",
        (model, u.prompt_tokens, u.completion_tokens, purpose)
    )
    db.commit()
    return r.choices[0].message.content

That single wrapper let me run SELECT purpose, SUM(prompt_tokens+completion_tokens) FROM calls GROUP BY purpose and see exactly where my budget went.

Day-by-day burn

Day	Activity	Tokens used	Cumulative	% of 5M
1-2	First wrapper, "hello world" calls	18,400	18,400	0.4%
3	RAG prototype, sloppy chunking	712,000	730,400	14.6%
4-5	RAG fix + re-runs	480,000	1,210,400	24.2%
6	Switched to V4 from R1	215,000	1,425,400	28.5%
7-9	Real prototype usage	1,640,000	3,065,400	61.3%
10	Discovered max_tokens unset	410,000	3,475,400	69.5%
11-13	Tightened prompts, capped output	1,180,000	4,655,400	93.1%
14	Insufficient balance error	345,000	5,000,000	100%

The three mistakes that cost ~600K tokens (12% of the grant)

1. Defaulting to DeepSeek R1 instead of V4 for non-reasoning tasks (~280K tokens wasted)

I started with model="deepseek-reasoner" because R1 is the "fancy" one. R1 generates internal thinking tokens for its chain-of-thought reasoning. Those tokens count against your balance but never appear in the output.

A simple "summarize this paragraph" task that takes ~400 tokens on V4 took ~1,200 tokens on R1. For a math problem, R1 burned ~4,000 tokens vs V4's ~600.

I lost about 280K tokens running summarization, classification, and small extraction tasks on R1 before I realized the cost.

Fix: Default to model="deepseek-chat" (V4). Switch to R1 only when you genuinely need step-by-step reasoning — math proofs, complex logic, multi-step analysis.

2. No `max_tokens` cap on chat calls (~250K tokens wasted)

By default, DeepSeek will happily generate 1,000+ token responses when you only need 200. I had a prototype that asked the model to "classify this support ticket into one of 5 categories." The expected output was a single word. V4 was giving me 5-paragraph explanations of why it picked the category.

# Before — V4 averaged 380 output tokens per classification
client.chat.completions.create(model="deepseek-chat", messages=[...])

# After — V4 averaged 8 output tokens per classification
client.chat.completions.create(model="deepseek-chat", messages=[...], max_tokens=20)

That single parameter cut my classification cost by 47x.

3. Sending full document context on every RAG call (~70K tokens wasted)

Early RAG prototype: I was re-sending a 2,400-token reference document on every call, even when the user's question was a follow-up that didn't need the full context.

# Before — 2,400 input tokens every call
messages = [
    {"role": "system", "content": full_document_text},
    {"role": "user", "content": user_question}
]

# After — ~400 input tokens average
relevant_chunks = vector_search(user_question, top_k=3)
messages = [
    {"role": "system", "content": "\n\n".join(relevant_chunks)},
    {"role": "user", "content": user_question}
]

Top-k retrieval with a vector store dropped my average input cost on RAG calls by 6x. The quality of answers actually improved — less context noise.

The four habits that would have stretched 5M tokens to a full month

If I were starting over with a fresh 5M balance, here's what I would do from day one.

Habit 1: System prompt under 200 tokens, always

Every API call includes your system prompt. If your system prompt is 500 tokens and you make 5,000 calls, that's 2.5M tokens just for system prompts — half your free balance.

I started with a 480-token system prompt. After trimming, it was 140 tokens with no measurable quality drop.

Heuristic: if your system prompt is more than 3 sentences, you can usually cut 50% of it. Test by removing one sentence at a time and checking output quality.

Habit 2: `temperature=0` for deterministic tasks

For classification, extraction, structured output — anything where the "right" answer is well-defined — set temperature=0. Outputs become consistent, you can cache results by input hash, and you stop wasting tokens on creative variation you didn't want.

Habit 3: Batch related questions into one call

Instead of 5 separate API calls for 5 related questions about the same document:

# Before — 5 calls, 5 system-prompt overheads
for q in questions:
    answer(document, q)

# After — 1 call, 1 system-prompt overhead
answer_batch(document, questions)
# Prompt: "Answer each of these 5 questions about the document below..."

That single change saved ~20-30% on total input tokens in my prototype.

Habit 4: Track usage daily, not at month-end

I set up a 10-line cron job to print my daily total at 23:00:

total = db.execute(
    "SELECT SUM(prompt_tokens+completion_tokens) FROM calls WHERE date(ts)=date('now')"
).fetchone()[0]
print(f"Today: {total:,} tokens ({total/5_000_000*100:.1f}% of grant)")

Most developers find out they're over budget the day credits run out. A daily printout catches the curve early — I would have seen day 3's 712K burn the same evening and corrected before day 4 doubled it.

What about after the credits run out?

DeepSeek's paid tier is unusually cheap: $0.27 input / $1.10 output per million V4 tokens. To put that in perspective, the same workload that burned my 5M free credits in 14 days would cost about $0.81 in paid tokens for the same period.

For a deeper breakdown of the math, expiry policies across providers, and how DeepSeek's free tier compares to OpenAI's $5 starter credit and Google AI Studio's 1,500 daily requests, I keep referring back to TokenMix's DeepSeek free credits guide — it tracks all 300+ provider free tiers in one place.

TL;DR

Lesson	Token cost / saving
Default to V4, not R1, for non-reasoning	Save ~3-10x per call
Always set `max_tokens` cap	Save 40-70% on short outputs
Cap system prompt at 200 tokens	Save 50-80% on multi-call overhead
Use top-k retrieval, not full context	Save 4-8x on RAG inputs
Track usage daily, not weekly	Catch overruns before they compound

5M tokens is genuinely a lot if you treat the budget like real money. It's also surprisingly easy to burn through if you treat it like "free." The math here is simple — and it's exactly the same math that applies once you're paying for tokens.

If you want the full DeepSeek free credit breakdown — including a cross-provider comparison table (DeepSeek vs OpenAI vs Google AI Studio vs Groq vs OpenRouter), pricing tier explainer, and all 7 optimization strategies — TokenMix has the canonical reference here.