DeepSeek Coding API for Large Repos and 100k Context

What the window gives you

If you search for a deepseek coding api, you probably want one thing: send a lot of code to a model and get back a refactor, a bug hunt or an explanation. This site sells an OpenAI-compatible API with one open-weight model, model ID uncensored, and a context window of 100,000 tokens. It is not the official DeepSeek API and it is not a router across vendors. It is a hosted model run by an independent provider, tuned to answer without content refusals for lawful adult use.

The window counts the prompt and the completion together. If you send 95,000 tokens of code, only about 5,000 tokens are left for the answer. max_tokens is optional, so the completion can use whatever remains, but you should still plan the split yourself. A typical source file of 200 lines is often somewhere between 1,500 and 3,000 tokens, which means a few dozen files fit in one request. Exact counts depend on the language and the tokenizer, so treat any estimate as rough and read the real numbers from the usage field of each response.

The rest of this article covers how to budget the window, pack files into it, and split work when the project is bigger than 100,000 tokens.

Budget the window before you pack it

Decide the split first. A refactor that returns whole rewritten files needs a large output budget. A question like "where is the session token validated?" needs only a short one. Here are three starting points.

Leave a margin of a few thousand tokens on top. If your estimate is off by 10 percent, a request packed to the exact limit fails or truncates the answer. Packing to 85 percent of your budget is a sensible default.

You can estimate tokens before sending by dividing characters by roughly 3.5 for source code. It is crude, but it is cheap and good enough for a packing loop. After the first real response, compare it with usage.prompt_tokens and adjust the divisor for your own codebase.

Pack files with paths and a clear order

The model has no file system. It only sees text, so you have to give every file a visible boundary and its path. Relative paths matter because the answer will refer to them, and you will want to apply the result back to the right files.

This script walks a folder, skips junk, sorts files so that the ones you name come first, and stops when the budget is used. It then asks for a unified diff.

Copyimport os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseekcodingapi.com/v1",
    api_key=os.environ["API_KEY"],
)

SKIP_DIRS = {".git", "node_modules", "dist", "build", "__pycache__", ".venv"}
EXTS = (".py", ".js", ".ts", ".go", ".rs", ".java", ".md")
BUDGET = 80_000  # tokens of code, estimated at 3.5 chars per token

def collect(root, first=()):
    paths = []
    for base, dirs, files in os.walk(root):
        dirs[:] = [d for d in dirs if d not in SKIP_DIRS]
        for name in files:
            if name.endswith(EXTS):
                paths.append(os.path.relpath(os.path.join(base, name), root))
    paths.sort(key=lambda p: (p not in first, p))
    return paths

def pack(root, first=()):
    parts, used = [], 0
    for rel in collect(root, first):
        text = open(os.path.join(root, rel), encoding="utf-8", errors="ignore").read()
        cost = int(len(text) / 3.5)
        if used + cost > BUDGET:
            continue
        parts.append(f"=== FILE: {rel} ===\n{text}\n=== END: {rel} ===")
        used += cost
    return "\n\n".join(parts), used

code, used = pack("./myproject", first=("src/auth.py", "src/session.py"))
resp = client.chat.completions.create(
    model="uncensored",
    temperature=0.2,
    messages=[
        {"role": "system", "content": "You are a careful senior engineer. Answer only from the files provided. Quote file paths exactly."},
        {"role": "user", "content": code + "\n\nTask: rename SessionStore to TokenStore everywhere. Return a unified diff only."},
    ],
)
print(resp.choices[0].message.content)
print(resp.usage)

Two details are worth copying. First, first=(...) puts the files you care about at the start, so a skipped file at the end costs less than a skipped file at the start. Second, files that do not fit are skipped one by one rather than stopping the loop, so a single huge generated file does not block everything after it.

Write the prompt so the model can find things

Put the instruction where it will be read

With long inputs, state the task at the end, right after the code, and repeat a short version in the system message. Details buried in the middle of 80,000 tokens are the ones most likely to be missed. If one function matters more than the others, name it in the task text.

Ask for a format you can apply

For refactors, ask for a unified diff or for "changed files only, each in its own fenced block with the path above it". Asking for full rewrites of every file wastes output tokens and invites silent edits to code you did not mention. For explanations, ask for a short map first (files, entry points, data flow) and then details.

Keep temperature low for code

A value around 0.2 gives more repeatable edits. temperature, top_p, stop and seed are passed through, so setting a seed helps when you want to compare two prompt variants fairly.

When the repo is bigger than the window

A repo with 400,000 tokens of code will not fit, and cramming a random slice into the prompt is the wrong fix. Use two passes instead.

1. Summarise per module. Send each package or folder on its own and ask for: public functions with one-line purposes, what it imports, what it exposes, and any side effects. Keep each summary under 400 words.
2. Ask the real question against the summaries. The combined summaries are small, so the model can pick which modules matter. Then send the two or three relevant modules in full for the actual edit.

The first pass is independent per module, so run it in parallel. A full key allows 8 concurrent requests, so a pool of 6 or 8 workers is the right size. A trial key allows 2.

Copyfrom concurrent.futures import ThreadPoolExecutor
from openai import OpenAI

client = OpenAI(base_url="https://api.deepseekcodingapi.com/v1", api_key="YOUR_KEY")

def summarise(item):
    name, code = item
    r = client.chat.completions.create(
        model="uncensored",
        temperature=0.1,
        max_tokens=700,
        messages=[
            {"role": "system", "content": "Summarise code modules for other engineers. Be factual and short."},
            {"role": "user", "content": f"Module: {name}\n\n{code}\n\nList public functions with one line each, imports, side effects."},
        ],
    )
    return name, r.choices[0].message.content

modules = {"auth": "...code...", "billing": "...code...", "api": "...code..."}
with ThreadPoolExecutor(max_workers=6) as pool:
    summaries = dict(pool.map(summarise, modules.items()))

A streamed request holds its concurrency slot until the stream ends, so do not mix a long streamed session and a full pool of workers on the same key without leaving a slot or two free.

What it costs

Billing is prepaid and pay as you go: $0.15 per 1M input tokens and $0.50 per 1M output tokens. Failed or refused requests are not charged. Some real numbers for code work:

The last row adds up to exactly $0.016. Send 100 requests like that in a day and you spend $1.60. The summarise-then-ask method on a 400,000-token repo is eight module summaries of about 50,000 tokens each (400,000 input tokens, $0.06) plus 8 summaries of roughly 600 tokens (4,800 output tokens, $0.0024), then one 30,000-token edit request. The whole thing lands under $0.08. See the pricing page for top-up amounts and bonuses.

Pitfalls and fixes

• The model invents a function that is not in your files. Add "If something is not in the provided files, say so" to the system message, and ask it to quote the line it relies on.
• Answer cut off mid-diff. Your prompt left too little room. Lower the code budget, or ask for one file at a time across several calls.
• Request rejected for size. The maximum body is 8 MB. Text rarely gets there, but a minified bundle can. Exclude build output.
• HTTP 429 with code concurrency. You have more requests in flight than your key allows. Lower the pool size. A 429 with Retry-After: 5 means temporary overload, so wait that long and retry.
• Edits that compile but change behaviour. Long-context models can lose track of details. Run your tests on every result and review the diff as you would a colleague's patch.

For endpoint details and parameters, read the API documentation. If you want a broader overview of what this API is for, see the existing developer guide on this site. To start, claim a key on the API key page; new users can take $0.50 of trial credit with one click, valid for 7 days, no card needed.

More guides

• Uncensored Coding Assistant for Security Research
• Uncensored DeepSeek Coding API: The Complete Guide

Common Questions from Developers

Many developers ask about the model architecture. Our uncensored model is an open-weight model run on our own servers. It is not GPT, Claude, or DeepSeek. It is tuned specifically for coding without refusals. The context window is 100k tokens. You can stream responses for real-time feedback. Function calling is fully supported for tool use.

Is there a free trial? Yes. Every new account gets $0.50 of trial credit valid for 7 days. You can test the API immediately. If you make a mistake, credit is not refunded but never expires. Double charges are fixed via the Support page. Our limits are 300 requests per minute and 8 concurrent requests per key. This ensures fair usage for all developers.

Getting Started: API Key Setup

Getting started is fast. Go to the "Get API key" page. You can sign up with Google or use an email and password. Your API key is shown immediately. You can start making requests right away. The base URL is https://api.deepseekcodingapi.com/v1. This URL works with the official OpenAI SDKs and any OpenAI-compatible client.

You only need one active key per account. A new key replaces the old one. This simplifies your configuration. You do not need to manage multiple keys for different environments. Our API key setup is designed for speed. You can focus on coding instead of configuring complex payment gateways. The process takes less than a minute. You are ready to generate code instantly.

Final Verdict: Is It Right for You?

This API is ideal for developers who want freedom. If you are tired of content filters blocking your code, this is the solution. The pricing is transparent and fair. You pay only for what you use. The crypto payment option appeals to those who value privacy. The prepaid model ensures no surprise bills.

The 100k context window and streaming support make it a powerful tool. You get an uncensored coding LLM that delivers results. Our API is an independent service. We are not affiliated with any major vendor. We provide a reliable alternative for your coding needs. You can trust us to deliver consistent performance. Start your trial today and experience the freedom.