I Wish I Knew AI Coding Models Sooner — Here's the Full Breakdown

I Wish I Knew AI Coding Models Sooner — Here's the Full Breakdown

Last quarter my burn rate went sideways. Not because we hired anyone new, not because we shipped a big feature — because I'd been running every PR review and every refactor through whatever premium model my IDE defaulted to. When I finally pulled the invoice, I wanted to throw my laptop into the bay. That's the day I started taking coding model pricing seriously, and that's the data I'm going to walk you through.

If you're building a startup and you're treating "AI writes my code" as one undifferentiated line item, you're leaving a huge amount of ROI on the table. I spent three weeks running the same five coding tasks across ten different models, all through a single unified endpoint so I could swap vendors without rewriting a single line of integration code. What follows is the shortlist, the prices, the scores, and — most importantly for a CTO — what I actually deployed.

The first thing I learned the hard way: those leaderboard screenshots you see on Twitter are not your production reality. They average across languages, across task types, across reasoning depth. What I needed was a score that weighted my real workload — Python services, TypeScript APIs, a growing Go sidecar, and the occasional JavaScript fix from a junior dev — against the dollar amount I'd actually be billed.

So I built my own rubric. Five tasks, four languages, scored 1–10 on correctness, code quality, documentation, and edge-case handling. Not glamorous, but it maps to what a real engineering org actually ships. If you're optimizing for ROI rather than vibes, this is the shape of work you're paying for.

Here's the raw list. I'm keeping every dollar figure exactly as the providers publish them, because half the confusion in this space comes from people quoting rounded numbers that turn out to be 2x wrong.

That Ga-Standard line is interesting — it's a routing layer that picks a backend per request. More on that in a minute, because it's central to my "no vendor lock-in" architecture.

I picked these because they map to things my team ships every week:

Each task gets a 1–10 score. I multiply by 10 because humans like big numbers, then I divide by the per-million-token output price to get a "value ratio." That ratio is the only number that matters when you're optimizing ROI at scale.

Here's what my spreadsheet looked like after the dust settled:

The asterisk on Ga-Standard is doing a lot of work there — that score is an average across whatever backend the router picks, so it floats task to task. But the price floor is real: $0.20/M is genuinely cheap.

Read those numbers the way a CFO would. Kimi K2.5 scores 9.0 and DeepSeek-R1 scores 9.4. They sound great. But DeepSeek V4 Flash scores 8.7 at less than a tenth of the price. When you're shipping at scale, "good enough at 8x cheaper" wins almost every time. That's the production-ready lesson.

"Write a Python function to flatten a nested list recursively."

Winner: DeepSeek-R1, but only because I asked for analysis. For pure code, DeepSeek V4 Flash gave me exactly what I needed at one-tenth the cost.

This is the test that separates "code generator" from "code reviewer." The buggy pattern:

let data = null;
fetch('/api/data').then(r => r.json()).then(d => data = d);
console.log(data); // Always logs null — race condition!

Every model I tested caught the issue. The question was how they fixed it and how much they explained.

Tie between DeepSeek V4 Flash and Qwen3-Coder-30B. Both gave me production-ready fixes I'd actually merge. That's important — at scale, "correct" without "mergeable" costs more than it saves.

This is where reasoning models earn their keep. Type safety, priority queue implementation, edge cases for disconnected graphs — that's a lot of cognitive surface area.

For algorithm-heavy work, DeepSeek-R1 at $2.50/M is genuinely worth the spend. This is the case where the value ratio goes out the window and you just want the highest-scoring model because the cost of a bug here is much higher than the token bill.

Here's the part that actually changed how my engineering org spends money. I do not hardcode a single model anywhere. Instead, every code-gen request goes through a unified router so I can mix-and-match based on the task. The router I use lives at https://global-apis.com/v1

, and it exposes every one of these models through the same OpenAI-compatible schema. That single fact has saved me from vendor lock-in, full stop.

Here's the kind of wrapper I drop into our internal dev tools:

python
import os
import requests
from typing import Literal

BASE_URL = "https://global-apis.com/v1"
API_KEY = os.environ["GLOBAL_APIS_KEY"]

TaskType = Literal["function", "bugfix", "algorithm", "review", "feature"]

MODEL_FOR_TASK = {
    "function": "deepseek-v4-flash",         # $0.25/M, fast and clean
    "bugfix":   "deepseek-v4-flash",         # 9.0 on JS race conditions
    "algorithm": "deepseek-r1",              # $2.50/M but worth it for hard problems
    "review":   "qwen3-coder-30b",           # $0.35/M, best code-specialized quality
    "feature":  "qwen3-coder-30b",           # dedicated code model wins on full features
}

def code_complete(prompt: str, task: TaskType, max_tokens: int = 1024) -> str:
    """Route a code-gen request to the cheapest model that's still production-ready."""
    model = MODEL