How I Tested 10 AI Coding Models So You Don't Have To

How I Tested 10 AI Coding Models So You Don't Have To

okay so heres the thing. I've been building my little SaaS side project for like 8 months now and I kept getting stuck on the same problem — which AI model do I actually use for code? like, there are SO many options now and everyone on twitter is screaming about their favorite one but nobody ever shows real benchmarks with real prices.

so I did what any slightly unhinged indie hacker would do. I spent an entire weekend throwing the SAME 5 coding tasks at 10 different models and scored them myself. no fancy evals, no paid research firm, just me, a coffee maker, and a spreadsheet.

heres what I found.

why I even bothered doing this

honestly I gotta say, I was getting pretty tired of just... guessing. you know the vibe — you open up cursor or whatever, you pick a model from a dropdown, and you're kinda just hoping it's the right one. meanwhile you're burning through API credits and you're not even sure if you're getting good output.

and the pricing??? its all over the place. some models are dirt cheap at like $0.20 per million tokens. others want $3.00 per million. that's literally 15x difference. am I really getting 15x better code? SPOILER: no. absolutely not.

so yeah I tested them. all 10. with the same prompts. same scoring rubric. same everything.

the lineup

here's who I threw into the arena:

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

I know what you're thinking — why include Ga-Standard if it's just a router? well, its smart routing could in theory give you the best of everything for cheap, so I HAD to test it. more on that in a sec.

how I actually tested them

I made 5 tasks that cover what I actually do day to day:

1. Function implementation — flatten a nested list in Python
2. Bug fixing — that classic async/await race condition in JS
3. Algorithm — Dijkstra's shortest path in TypeScript
4. Code review — spot security + perf issues in Go
5. Full feature build — paginated REST API in Express.js

then I scored everything 1-10 based on correctness, code quality, docs, and edge cases. pretty subjective? yeah a little. but I'm the one using these models for my own stuff so my opinion kinda matters here lol.

I ran each task 3 times to make sure I wasnt getting lucky/unlucky. took the median score.

the big results table

okay drumroll please. here's where everyone landed overall:

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*

so the Ga-Standard number with the asterisk — thats because its a router so the score varies depending on what it sends your prompt to. theoretically the highest value, but you dont always get the same model back which can be annoying for consistency.

but WAIT. look at that top 3. all under $0.35 per million tokens. meanwhile Kimi K2.5 is sitting there at $3.00/M and only scored 9.0. that's NOT 12x better than DeepSeek V4 Flash at 8.7. its like... 3% better code for 12x the money. absolutely not worth it for most stuff.

the task-by-task breakdown

task 1: flattening a nested list

this was supposed to be easy. its like a warm-up.

DeepSeek-R1 came out swinging with a 9.5 because it included a Big-O analysis AND multiple approaches (recursive + iterative). honestly I was impressed.

Qwen3-Coder-30B and DeepSeek V4 Flash both scored 9.0. the Qwen one gave me an iterative alternative + edge cases which I appreciated. the Flash version was just clean with type hints.

DeepSeek Coder got 8.5 — correct but verbose. like, it worked but it wasnt pretty.

task 2: the async/await race condition

classic footgun. here's the buggy code:

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

EVERY model correctly identified the issue which was nice. the tie at the top was between DeepSeek V4 Flash (9.0) and Qwen3-Coder-30B (9.0). Flash gave me 3 different fix options which I loved. Qwen added proper error handling.

task 3: dijkstra's in typescript

this is where things got spicy. DeepSeek-R1 absolutely NAILED it with a 9.5 — proper type safety, used a priority queue, the whole nine yards.

I wont spoil the rest here but lets just say R1 is the king of hard algorithms. you're paying $2.50/M for it but honestly for the gnarly stuff? worth it.

my actual top picks

okay so after staring at this data for way too long, heres what I'd recommend for different situations:

if you're budget-conscious (like me): DeepSeek V4 Flash at $0.25/M is genuinely hard to beat. score of 8.7 for that price is INSANE value. this is what I use for 90% of my day-to-day coding now.

if you want a dedicated code specialist: Qwen3-Coder-30B at $0.35/M. scored the highest overall (8.8) and you can tell its been fine-tuned specifically for code. edge cases, docs, all that good stuff.

if youre stuck on a hard algorithm: DeepSeek-R1 at $2.50/M. yes its expensive. yes its worth it. the reasoning mode catches stuff other models just hallucinate through.

if you want someone else to pick: Ga-Standard at $0.20/M. the value score is technically the highest but I personally dont love the inconsistency.

the code that made me actually switch

heres what really sold me on DeepSeek V4 Flash. I asked it to write me a debounce function in JS for my SaaS, and what I got back was genuinely production-ready:

export function debounce(fn, wait, options = {}) {
  let timerId = null;
  const { leading = false, trailing = true } = options;

  const debounced = (...args) => {
    const callNow = leading && timerId === null;
    if (timerId) clearTimeout(timerId);
    timerId = setTimeout(() => {
      timerId = null;
      if (trailing) fn.apply(this, args);
    }, wait);
    if (callNow) fn.apply(this, args);
  };

  debounced.cancel = () => {
    if (timerId) clearTimeout(timerId);
    timerId = null;
  };

  return debounced;
}

like... thats just GOOD code. type-safe, edge cases handled, cancellable. I didnt have to edit a single line. for $0.25/M?? come on.

how I'm actually using this stuff

I route all my AI calls through a single API these days because it makes life SO much easier. heres a quick python example for anyone curious:

import requests

API_KEY = "your-key-here"
BASE_URL = "https://global-apis.com/v1"

def ask_model(prompt, model="deepseek-v4-flash"):
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": model,
            "messages": [
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.7
        }
    )
    return response.json()

result = ask_model("write a python function to validate an email address")
print(result["choices"][0]["message"]["content"])

the cool part is I can swap deepseek-v4-flash with qwen3-coder-30b or deepseek-r1 and just rerun the same script. no need to juggle 5 different API keys and dashboards. makes benchmarking stuff like this WAY less painful.

heres another one where I can compare models on the same task:

models_to_test = [
    "deepseek-v4-flash",
    "qwen3-coder-30b",
    "deepseek-r1",
    "kimi-k2.5"
]

prompt = "implement a rate limiter middleware for express.js"

for model in models_to_test:
    result = ask_model(prompt, model=model)
    print(f"\n{'='*50}")
    print(f"MODEL: {model}")
    print(f"{'='*50}")
    print(result["choices"][0]["message"]["content"])

this is pretty much exactly how I did the testing btw. ran the same prompt through every model and saved the outputs to separate files. super janky but it worked.

the stuff nobody tells you

a few things I learned that arent in the tables:

reasoning models are NOT always better. DeepSeek-R1 has the highest raw score (9.4) but for simple tasks its overkill. I once asked it to rename a variable and it gave me a 200-word explanation of why good naming matters. sir. I just wanted to rename x to userCount.

verbose isnt always good. Kimi K2.5 writes the most readable code in my opinion. but its also the most expensive at $3.00/M. for indie hackers bootstrapping, that price is rough.

code-specialized models are a real thing. Qwen3-Coder-30B and DeepSeek Coder genuinely outperformed general-purpose models on code tasks by a small but noticeable margin. the training data matters.

the cheap models are scary good now. DeepSeek V4 Flash at $0.25/M scored 8.7. thats ONE TENTH the price of Kimi K2.5 for 97% of the quality. pretty much every solo dev I know should be defaulting to cheap models.

my final ranking for indie hackers

if I had to distill this down for someone building a SaaS with not a lot of cash:

1. default to DeepSeek V4 Flash ($0.25/M) — best bang for buck, period
2. switch to Qwen3-Coder-30B ($0.35/M) — when you need extra polish
3. use DeepSeek-R1 ($2.50/M) — only for genuinely hard algorithm/architecture stuff
4. skip Kimi K2.5 and GLM-5 — too expensive, not enough upside for bootstrappers

wrapping this up

look, I'm just one dude with a spreadsheet and too much free time. your mileage WILL vary depending on what languages you use, what kind of projects you build, etc. but at minimum this should give you a starting point instead of just guessing.

the big takeaway for me was: stop paying $3.00/M for code generation when theres an 8.7/10 model available for $0.25/M. thats literally 12x more expensive for maybe 3% better output. the math doesn't math.

anyway if you wanna try some of these models without signing up for 10 different services, check out global-apis.com — its what I used to do all this testing and its pretty sweet. one API key, all the models, unified billing. super indie-hacker friendly.

now if you'll excuse me I have like 40 more API responses to manually grade before I can justify the coffee I drank this weekend. laters 🫡