I Tested 10 AI Coding Models So You Don't Blow Your Budget

So here's what happened: i Tested 10 AI Coding Models So You Don't Blow Your Budget

Look, I run a one-person dev shop. Every API call I make is coming out of money a client paid me to ship their project. So when someone says "just use GPT-4o for everything," I hear "just hemorrhage margin for no good reason." That's why I spent two weekends running ten different AI coding models through the exact same set of tasks I get billed for. I'm sharing everything — the scores, the surprises, and the per-task dollar damage.

Why I bothered doing this in the first place

Here's the thing nobody tells you when you're freelancing: the model that's "best" on Twitter isn't necessarily the one that makes you money. A $3.00/M output model might write slightly prettier code than a $0.25/M model, but if both pass code review, the cheaper one wins every single time. My billable hour is the same regardless of which LLM spat out the function. The model cost is pure overhead.

So I sat down with my last six months of client work and pulled out the patterns. Five task types that eat up most of my week:

1. Throwaway utility functions (flatten a list, parse a date, transform a JSON shape)
2. Squashing async/await bugs in Node backends
3. Algorithm implementation (Dijkstra, BFS, rate limiters, the usual interview-flavored stuff)
4. Security and performance review on Go services
5. Full feature scaffolds — Express endpoints with pagination, filtering, the boring CRUD plumbing

Every model got the same five prompts. I scored each one out of 10 based on whether the code actually ran, whether I'd be embarrassed to commit it, and whether it handled the edge cases a senior reviewer would flag.

The lineup (and what each one cost me)

Here's every model I tested, the output price per million tokens, and what category they fall into:

#	Model	Provider	Output $/M	Category
1	DeepSeek V4 Flash	DeepSeek	$0.25	General (strong code)
2	DeepSeek Coder	DeepSeek	$0.25	Code-specialized
3	Qwen3-Coder-30B	Qwen	$0.35	Code-specialized
4	DeepSeek V4 Pro	DeepSeek	$0.78	Premium general
5	DeepSeek-R1	DeepSeek	$2.50	Reasoning (code thinking)
6	Kimi K2.5	Moonshot	$3.00	Premium general
7	GLM-5	Zhipu	$1.92	Premium general
8	Qwen3-32B	Qwen	$0.28	General purpose
9	Hunyuan-Turbo	Tencent	$0.57	General purpose
10	Ga-Standard	GA Routing	$0.20	Smart routing

The price column is where I focus first. You can always re-rank by quality later, but you can never un-spend money.

The scores that actually matter

I built a "value" column that divides quality by dollars. That's the number I care about when I'm picking a default for the week:

Rank	Model	Score	Price	Value (Score/$)
1	Qwen3-Coder-30B	8.8	$0.35	25.1
2	DeepSeek V4 Flash	8.7	$0.25	34.8
3	DeepSeek Coder	8.6	$0.25	34.4
4	DeepSeek V4 Pro	9.1	$0.78	11.7
5	DeepSeek-R1	9.4	$2.50	3.8
6	Kimi K2.5	9.0	$3.00	3.0
7	Qwen3-32B	8.3	$0.28	29.6
8	GLM-5	8.0	$1.92	4.2
9	Hunyuan-Turbo	7.5	$0.57	13.2
10	Ga-Standard	8.5*	$0.20	42.5*

A quick note on Ga-Standard — it's a smart router, so its score bounces around depending on which backend it picks. On a good day it's the cheapest thing in the lineup. On a weird day it routes to something pricey and the "value" math gets muddy. Treat that asterisk as a "your mileage may vary" sticker.

Task 1: Flatten a nested list in Python

Pretty basic, right? "Write a Python function to flatten a nested list recursively." You'd think every model nails this, and they mostly do. The differences are in the extras.

DeepSeek V4 Flash handed me a clean recursive solution with proper type hints. Nothing fancy, nothing wrong. Score: 9.0.

Qwen3-Coder-30B did the same thing but tossed in an iterative alternative plus a few edge case notes. That's the kind of thing that saves me ten minutes of writing the second version myself when the client asks "can we also support generators?" Score: 9.0.

DeepSeek Coder was correct but bloated — way more comments than the function needed. I'd have to clean it up before committing. Score: 8.5.

Kimi K2.5 produced the most readable version with a real docstring. Honestly gorgeous code, but at $3.00/M I'm not paying a premium for readability I can add in 30 seconds. Score: 9.0.

DeepSeek-R1 at $2.50/M included a Big-O analysis and two different approaches (recursive and iterative). For an interview prep gig I had last month, that would have been a lifesaver. For a "flatten this list for the migration script" task, it's overkill. Score: 9.5.

My pick for billable work: DeepSeek V4 Flash. It did the job clean, and the cost-per-call is roughly one-tenth of the reasoning model.

Task 2: Async/await race condition in JavaScript

The buggy code I fed every model:

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

Classic. Every model identified it correctly (which is the bare minimum for a code model in 2026). What separated the good from the great was what came after the fix.

DeepSeek V4 Flash gave me a clean explanation plus three fix options — async/await, Promise chaining, and a wrapper. Score: 9.0.

Qwen3-Coder-30B shipped the fix with proper error handling baked in. That alone saves me a second pass. Score: 9.0.

DeepSeek Coder got it right but barely explained anything. Score: 8.5.

Qwen3-32B also nailed it but felt a touch verbose. Score: 8.5.

Tie between DeepSeek V4 Flash and Qwen3-Coder-30B. For pure bug-fix work, either one is fine. I'd default to the cheaper of the two for routine client tickets.

Task 3: Dijkstra in TypeScript

This is where the cheap models start sweating. "Implement Dijkstra's shortest path in TypeScript" sounds simple until you actually need the priority queue and the type safety to hold up at scale.

DeepSeek-R1 nailed it — perfect type safety, a real priority queue, and the explanation was solid. Score: 9.5.

Qwen3-Coder-30B came in just behind with a working implementation. Score: 9.0.

DeepSeek V4 Flash produced correct code but with looser types. Acceptable for a prototype, would need cleanup for production. Score: 8.5.

DeepSeek Coder got the logic right but the types were a mess. Score: 8.0.

GLM-5 was technically correct but felt like it had been written by someone who'd never used TypeScript seriously. Score: 7.5.

Here's the math I ran in my head: if I'm charging the client $150/hour and Dijkstra takes me 45 minutes to write from scratch, the model's job is to knock that down to 15 minutes. The $2.50/M DeepSeek-R1 call that gets me 90% of the way there costs me maybe $0.08 in tokens. The $0.25/M model that gets me 70% of the way there still costs me 30 minutes of cleanup. The expensive model actually wins on net billable hours for hard algorithm work.

Task 4: Go code review

"Review this Go code for security issues and performance" is a weird one because there's no single right answer. I'm scoring on whether the model catches real issues and whether its suggestions are actually good.

DeepSeek V4 Pro caught the most issues — SQL injection, missing context timeouts, an N+1 query, and a goroutine leak. The suggestions were actionable. Score: 9.5.

DeepSeek-R1 caught fewer issues but explained each one in pedagogical detail. Great for teaching, slightly slower to skim. Score: 9.0.

Qwen3-Coder-30B was solid — caught the security stuff, missed the goroutine leak. Score: 8.5.

Kimi K2.5 was thorough but kept suggesting changes I'd argue with. Score: 8.0.

For Go review specifically, I'd reach for DeepSeek V4 Pro. At $0.78/M, the extra quality is worth it for the peace of mind on production code.

Task 5: Express.js REST endpoint with pagination

"Build a REST API endpoint with Express.js that paginates and filters users." This is the bread and butter of my freelance week.

Qwen3-Coder-30B gave me a complete, well-structured endpoint with input validation, error handling, and clean separation of concerns. I'd commit it with maybe two tweaks. Score: 9.5.

DeepSeek V4 Flash was 90% of the way there, slightly less polish on the error responses. Score: 9.0.

DeepSeek Coder was functional but skipped the validation. Score: 8.0.

Hunyuan-Turbo produced working code but with questionable variable naming. Score: 7.0.

GLM-5 had a bug in the filter logic. Caught it, docked a point. Score: 7.5.

For this exact task, Qwen3-Coder-30B at $0.35/M is my new default. It's the kind of code that takes me 30 minutes to write and the model gets me 80% there in five seconds.

What I actually do with all this

I built a small Python helper that routes different tasks to different models. Here's roughly what it looks like:

import os
import requests

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

def generate_code(prompt: str, model: str = "deepseek-v4-flash") -> str:
    """Send a coding prompt and return the model's response."""
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json",
        },
        json={
            "model": model,
            "messages": [
                {"role": "system", "content": "You are a senior software engineer. Write clean, production-ready code."},
                {"role": "user", "content": prompt},
            ],
            "temperature": 0.2,
        },
        timeout=60,
    )
    response.raise_for_status()
    return response.json()["choices"][0]["message"]["content"]

quick_function = generate_code(
    "Write a Python function to flatten a nested list recursively"
)

# For harder algorithm work, I switch to the reasoning model
dijkstra_code = generate_code(
    "Implement Dijkstra's shortest path in TypeScript with proper types",
    model="deepseek-r1",
)

Same endpoint, two different model choices depending on what I'm working on. I keep the cheap one on hot keys and only reach for the expensive reasoning model when the task actually justifies it.

The real ROI calculation

Let me put actual numbers on this. Suppose I run 200 coding requests in a typical month (that's about 10 per workday, which is honestly on the low end for me during a heavy sprint).

If I used Kimi K2.5 ($3.00/M) for everything and averaged 500 output tokens per call, that's 200 × 500 = 100,000 tokens = $0.30 in model spend. Not bad.

If I used DeepSeek-R1 ($2.50/M) for everything on the same volume: $0.25.

If I used DeepSeek V4 Flash ($0.25/M) for everything: $0.025.

That last one is twelve times cheaper than Kimi. And honestly? For 80% of my tasks, the quality difference is barely visible. I save real money by routing smart instead of just picking the "best" one.

But here's the nuance: for that other 20% — the hard algorithms, the security-sensitive Go services, the gnarly refactors — the cheap models cost me billable hours. I spend 20 extra minutes cleaning up what a $2.50/M model would have nailed on the first try. At $150/hour, 20 minutes is $50 of my time. The expensive model pays for itself many times over on those specific tasks.

The winning move is mixing. Default to the value king, escalate to the specialist when the task demands it.

My final default stack

After two weekends of testing, here's what I actually use day-to-day:

• Default for most things: DeepSeek V4 Flash at $0.25/M. Clean, cheap, good enough 90% of the time.
• For full feature scaffolds: Qwen3-Coder-30B at $0.35/M. That extra polish is worth three cents.
• For hard algorithm work: DeepSeek-R1 at $2.50/M. Yes it's pricey, but it saves me billable hours.
• For Go code review: DeepSeek V4 Pro at $0.78/M. Caught things the others missed.
• When I want the router to decide: Ga-