Cheapest LLM APIs for coding

Rank cost after quality

A useful coding shortlist has two stages. First, run each model against the same repository tasks and acceptance tests. Then compare the prices of the models that pass. Reversing that order can make a cheap model expensive through retries, review time, and broken changes.

Cost-first shortlist

The monthly column uses 10M input and 2M output tokens. Qwen3-Coder-Next uses its Singapore international ≤32K request tier.

Build a 20-task coding gate

Choose tasks from work you already understand. A compact evaluation can cover:

• five bug fixes with an existing failing test;
• five small feature changes with written acceptance criteria;
• four refactors that must keep all tests green;
• three code-review tasks where the model must identify a real defect;
• three repository questions that require finding the correct files.

Record first-pass success, total model calls, input and output tokens, elapsed time, and human correction time. Price becomes meaningful only after those numbers exist.

DeepSeek's integration surface

DeepSeek documents an OpenAI-format base URL, so an existing OpenAI SDK integration can target its API by changing the base URL and model identifier. Keep the API key in an environment variable.

import os
from openai import OpenAI

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

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Fix the failing test."}]
)

Watch the context tier

Repository agents can send large prompts. Qwen3-Coder-Next's published Singapore price rises from $0.30/$1.50 at ≤32K input tokens per request to $0.50/$2.50 for 32K–128K and $0.80/$4.00 for 128K–256K. Budget from the tier your requests actually reach.

The practical decision

Test DeepSeek V4 Flash first when minimizing cost is the goal. Include Qwen3-Coder-Next when you want a coding-specific candidate, and keep a stronger fallback if your evaluation shows the inexpensive model fails complex tasks. The winning setup may be a router, not one model.