Best Local LLM for Python Coding: Local or Hosted API?

Start with your constraints, not a model name

The question best local llm for python coding has no fixed answer, because models change quickly and your machine is the real limit. Before comparing names, write down four things:

• GPU memory. This decides which model sizes you can run at all.
• Context needs. Editing one function is different from reasoning over a whole package.
• Privacy rules. Some code cannot leave your machine, which is a legitimate reason to stay local.
• Refusal tolerance. Security tooling, scrapers, reverse engineering and fuzzers can trigger refusals in heavily filtered models.

If strict offline operation is a hard requirement, you need a model you run yourself. If it is not, a hosted API removes the hardware question. This site belongs to the second group: it is a hosted, OpenAI-compatible API, not a local runtime, and it is worth testing next to your local setup.

How much GPU memory you need

A rough rule for weights: parameters times bytes per parameter. At 16-bit precision that is 2 bytes per parameter; at 8-bit, 1 byte; at 4-bit, about 0.5 bytes.

Model size	16-bit	8-bit	4-bit
7B	14 GB	7 GB	3.5 GB
14B	28 GB	14 GB	7 GB
32B	64 GB	32 GB	16 GB

These figures are weights only. You also need memory for the context cache, which grows with the prompt, and for the runtime itself. Leave a few gigabytes of margin, and more if you plan to paste in large files. Heavier quantization saves memory but can cost some accuracy, which matters for code where a single wrong character breaks the program.

How to compare coding models fairly

Public leaderboards measure someone else's tasks. A better method takes an hour and uses your own code.

1. Collect 15 to 20 real tasks: a bug with a failing test, a function to write from a docstring, a refactor, a request to add type hints.
2. Run each model with the same prompt, the same temperature (0.2 is a sensible start for code) and the same seed where supported.
3. Score by running the tests, not by reading the answer. Does it import real modules? Does it pass?
4. Note refusals separately. A model that is 5% better on code but declines your port scanner task is worse for you.
5. Record time per task and, for hosted models, cost per task.

Keep the harness in a file so you can rerun it when a new model appears. That reuse is worth more than any single result.

Testing a hosted option on the same tasks

Because the API is OpenAI-compatible, your harness needs only a different base URL. This script asks for a function, extracts the first Python block and runs it against a test. The model ID is uncensored.

Copyimport re
import subprocess
import tempfile
from openai import OpenAI

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

TASK = "Write a Python function parse_size(text) that converts '10KB' or '2 MB' to bytes."
TEST = "assert parse_size('10KB') == 10240\nassert parse_size('2 MB') == 2 * 1024 * 1024\nprint('ok')"

r = client.chat.completions.create(
    model="uncensored",
    temperature=0.2,
    seed=7,
    messages=[
        {"role": "system", "content": "You write correct Python 3. Reply with one fenced code block and no commentary."},
        {"role": "user", "content": TASK},
    ],
)
text = r.choices[0].message.content
m = re.search(r"```(?:python)?\n(.*?)```", text, re.S)
code = m.group(1) if m else text

with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f:
    f.write(code + "\n" + TEST)
out = subprocess.run(["python3", f.name], capture_output=True, text=True, timeout=20)
print(out.stdout.strip() or out.stderr.strip())

Run the same task list against your local model and compare pass rates. The code runs generated output, so use a throwaway directory or a container for anything beyond toy examples.

Where a hosted model helps a Python developer

A hosted endpoint is not better in every case, but a few jobs suit it well:

• Large context. The window is 100,000 tokens, prompt plus completion. That fits a sizeable module, its tests and a stack trace in one request.
• Tool calling. You can define tools such as run_tests or read_file and let the model call them in a loop, with results sent back as role: "tool" messages.
• JSON mode. With response_format: {"type":"json_object"} you can get a structured code review, such as a list of issues with line numbers.
• Fewer refusals on security work. The model is tuned to answer without content refusals for lawful adult use, which includes security testing of systems you own or are authorized to test.

The trade-off is plain: your code leaves your machine, and you need a network connection. Prompts are not used for training, but if your policy forbids sending code to any third party, stay local.

Letting the model run your tests with tool calling

Tool calling turns a one-shot answer into a loop: the model asks for the tests to be run, sees the failure and fixes the code. Define one tool and handle the call yourself.

Copyimport json
import subprocess
from openai import OpenAI

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

tools = [{
    "type": "function",
    "function": {
        "name": "run_tests",
        "description": "Run pytest in the project and return the output",
        "parameters": {"type": "object", "properties": {}, "required": []},
    },
}]

messages = [
    {"role": "system", "content": "You fix Python bugs. Call run_tests to check your work."},
    {"role": "user", "content": "The tests in this project fail. Run them and tell me the first error."},
]

for _ in range(5):
    r = client.chat.completions.create(model="uncensored", messages=messages, tools=tools, temperature=0.2)
    msg = r.choices[0].message
    messages.append(msg)
    if not msg.tool_calls:
        print(msg.content)
        break
    for call in msg.tool_calls:
        out = subprocess.run(["pytest", "-x", "-q"], capture_output=True, text=True)
        messages.append({"role": "tool", "tool_call_id": call.id, "content": (out.stdout + out.stderr)[-4000:]})

The loop is capped at five rounds so a confused model cannot run forever. Keep the tool output short, because everything you return is billed as input on the next round. Cutting it to the last 4,000 characters keeps each turn cheap.

What it costs for coding work

Coding prompts are input-heavy: you paste code and get a shorter answer. Take a refactor request with 6,000 input tokens and 1,500 output tokens at $0.15 and $0.50 per 1M:

• Input: 6,000 / 1,000,000 x $0.15 = $0.0009
• Output: 1,500 / 1,000,000 x $0.50 = $0.00075
• Total: $0.00165 per request

A thousand such requests cost $1.65. The $0.50 trial credit covers about 303 of them, enough to run a 20-task benchmark several times over. A local GPU may be cheaper per request at heavy volume, but it has an upfront price and does not shrink to zero when you are not using it. The pricing page has the full table.

Practical settings for code generation

• Temperature 0.1 to 0.3. Lower values reduce invented APIs and random style shifts.
• Pin a seed when you compare prompts, so differences come from the prompt and not from sampling.
• State the Python version and libraries in the system message: "Python 3.11, standard library only" removes a lot of guesswork.
• Ask for tests with the code. A model that writes its own tests often catches its own off-by-one errors when you run them.
• Send the failing traceback, not a description of it.

To try it, claim the trial on the API key page, then follow the quickstart. For chat-style use, see the uncensored LLM chat guide. The service is for lawful adult use, and it does not generate sexual content involving minors.

Common pitfalls with coding models

• Invented imports and function names. Run the code. A model can describe a library method that does not exist, so the test is the only judge.
• Outdated syntax. If you need modern features such as structural pattern matching, name the Python version in the prompt.
• Too much context. A larger window does not mean you should paste everything. Send the files that matter, plus the error.
• Trusting generated security code. Review anything touching authentication, crypto or input parsing by hand.
• Running output blindly. Generated code can delete files or make network calls. Use a sandbox.

More guides

• How Much Does an LLM Cost? Per-Token Math and Examples
• Uncensored LLM Chat: Integration Guide & Use Cases