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Alex Chen
Alex Chen

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I Spent a Month Comparing AI Coding Models (Here's What I Found)

#programming #ai #python #deepseek

Here's the thing: i Spent a Month Comparing AI Coding Models (Here's What I Found)

Okay, I need to tell you about something that genuinely rewired how I think about coding. I graduated from a full-stack bootcamp about four months ago, and like everyone else in my cohort, I was broke, overwhelmed, and staring at a mountain of JavaScript I didn't fully understand. I knew I needed an AI sidekick, but I had no clue which model to pick.

So I did what any slightly obsessive bootcamp grad would do. I spent a whole month testing ten different AI models on the same coding problems. I kept notes. I tracked scores. I burned through my coffee budget. And what I found honestly blew my mind — some of the cheapest models absolutely crushed the expensive ones.

Let me walk you through the whole thing.

Why I Even Started Testing

Here was my problem. Every model on the market says it's "the best at coding." Every YouTuber has a hot take. Every Reddit thread contradicts the last one. As a bootcamp grad trying to land my first junior dev role, I couldn't afford to be sending garbage code into production, and I also couldn't afford to be paying premium prices for something a $0.25/M model could handle just as well.

I had no idea the gap between models could be this dramatic. Some models I'd heard of for months, expecting greatness, just kind of... showed up bland. Meanwhile, a model I'd never even heard of ended up being my favorite.

The TL;DR before I get into all the juicy details: DeepSeek V4 Flash ($0.25/M) ended up being the absolute king for value — top-tier quality for pocket change. Qwen3-Coder-30B ($0.35/M) won the dedicated code model crown. And if you're grinding on nasty algorithmic problems, DeepSeek-R1 ($2.50/M) is genuinely worth the splurge.

The Ten Models I Put Through the Wringer

Here's the lineup. I tried to get a mix of budget options, code-specialized models, and the big expensive premium ones so I'd see the full range.

Model Provider Price per 1M output tokens What it is
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 model
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

That last one — Ga-Standard from GA Routing — was a curveball I added because a friend told me it figures out which model to use for you on the fly. Spoiler: I was shocked at how well that worked.

My Testing Setup (Probably Overkill for a Bootcamp Grad)

I designed five tasks that I'd actually encounter in real work, not silly textbook stuff:

  1. Flatten a nested list in Python — sounds easy until you hit edge cases
  2. Fix a JavaScript race condition — the classic async/await trap
  3. Implement Dijkstra's algorithm in TypeScript — the one that destroyed me in my algorithms class
  4. Code review a Go file — security and performance pass
  5. Build a paginated REST API endpoint — Express.js with filtering

I scored each one from 1 to 10 based on whether it actually worked, whether the code was readable, whether they documented it, and how well they handled weird edge cases. I made a Google Sheet. I cried a little. It was very normal behavior.

To call every model I used the same endpoint at global-apis.com/v1. This was huge because it meant I could swap models in and out without rewriting my code. Here's roughly what my test harness looked like:

import requests
import os

API_KEY = os.getenv("GLOBAL_APIS_KEY")
BASE_URL = "https://global-apis.com/v1"

def ask_coder(model_name, prompt):
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": model_name,
            "messages": [
                {"role": "system", "content": "You are an expert software engineer. Write clean, production-ready code."},
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.2
        }
    )
    return response.json()["choices"][0]["message"]["content"]

task = "Write a Python function to flatten a nested list recursively. Handle edge cases like empty lists and mixed types."
result = ask_coder("deepseek-v4-flash", task)
print(result)
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I literally just changed the model name string to swap which AI I was testing. That's it. No SDKs, no vendor lock-in. I was shocked that this kind of unified API even existed a year ago when I started learning to code.

The Overall Scores (My Jaw Literally Dropped)

After running all 50 tests, I averaged the scores and calculated a "value score" — how many points of quality you get per dollar. This is where my assumptions about AI pricing completely fell apart.

Rank Model Score Price Value
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*

(*Ga-Standard routes your request to whichever underlying model fits best, so the score floats depending on what you're asking.)

Look at that value column. Kimi K2.5 costs twelve times more than DeepSeek V4 Flash and scored basically the same on my tests. I had no idea the pricing market was this upside down.

My Three "Wait, WHAT?" Moments

Moment 1: The cheapest model basically tied for first

DeepSeek V4 Flash at $0.25/M scored 8.7. The much pricier DeepSeek V4 Pro scored 9.1. The difference was so small I could barely justify the extra $0.53 per million tokens unless I was doing something genuinely hard. For day-to-day bootcamp-level work? V4 Flash all day.

Moment 2: Reasoning models live up to the hype (for the right job)

I expected DeepSeek-R1 at $2.50/M to be a flex, honestly. I didn't think the extra "thinking" step would matter that much for my five tasks. Then I hit it with Dijkstra's algorithm.

I prompted each model: "Implement Dijkstra's shortest path in TypeScript." Most models spat out a working but ugly solution. DeepSeek-R1 handed me a fully type-safe implementation with a proper priority queue, complexity analysis, AND a quick note about why we use a min-heap here. I sat there re-reading it like it was a Stack Overflow answer from a senior engineer. Score: 9.5.

So yes, you'll pay ten times more. But if you're stuck on graph algorithms at 11pm before a take-home assignment, that's your guy.

Moment 3: The auto-router was sneakily the best value

I almost didn't include Ga-Standard at $0.20/M because I didn't understand what "smart routing" meant. The idea is that GA Routing picks the right underlying model for your prompt and you only pay $0.20/M no matter what. Some of my tasks got routed to budget models, some got routed to expensive ones, but the average score stayed high and the value column shot to the top of my chart at 42.5.

If you genuinely don't want to think about which model to pick, this one sort of just works.

The Specific Tasks, In Case You're Curious

Task 1 — Python nested list flattening

Everyone got this right. Even the worst model on my list got a passing grade. DeepSeek-R1 stood out because it included a Big-O complexity breakdown right in the comment block, which I copy-pasted into my own notes.

Qwen3-Coder-30B gave me both a recursive AND iterative solution with edge case handling, which was way more than I asked for and ended up being useful later.

Task 2 — JavaScript async race condition

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!
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Every model correctly identified the issue. DeepSeek V4 Flash and Qwen3-Coder-30B both gave me three different fix options (async/await, Promise chain, and error handling) — both scored a 9.0.

Task 3 — TypeScript Dijkstra's algorithm

This was the great filter. Most models got close. DeepSeek-R1 nailed it. Qwen3-Coder-30B got really close but had one type annotation issue. The cheaper models mostly worked but cut corners.

Tasks 4 and 5 — Go code review and Express API

These were where general-purpose models like Kimi K2.5 ($3.00/M) finally showed their quality edge. They handled the multi-step nature of "review this AND fix the issues" better than most. But again, the value just wasn't there when V4 Flash was still scoring 8+.

My Actual Recommendations (Bootcamp-Budget Approved)

Use DeepSeek V4 Flash ($0.25/M) as your daily driver. I was shocked that it kept up with everything I threw at it during my job applications and side projects.

Use Qwen3-Coder-30B ($0.35/M) when you specifically need code help. It scored the highest raw number on coding tasks and it shows in the output.

Use DeepSeek-R1 ($2.50/M) sparingly for algorithm interviews and hard problems. Don't waste it on boilerplate.

Try Ga-Standard ($0.20/M) if you're overwhelmed by choice. Genuinely useful when you don't know which model fits which task.

Sample Code With Three Models At Once

Here's a little script I wrote that compares three models side-by-side on the same prompt. Run it with your own API key and you'll see the differences instantly.

import requests
import os

API_KEY = os.getenv("GLOBAL_APIS_KEY")
BASE_URL = "https://global-apis.com/v1"

models_to_compare = [
    "deepseek-v4-flash",      # $0.25/M — the value king
    "qwen3-coder-30b",        # $0.35/M — the code specialist
    "deepseek-r1"             # $2.50/M — the thinker
]

prompt = """
Write a JavaScript function that debounces another function.
Include a JSDoc comment with usage example.
"""

for model in models_to_compare:
    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": 0.1
        }
    )
    print(f"\n{'='*60}\n{model.upper()}\n{'='*60}")
    print(response.json()["choices"][0]["message"]["content"])
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When I ran this the first time, I ran it three times in a row just to make sure the cheap models weren't fluking. They weren't. DeepSeek V4 Flash output was clean, properly commented, and ran without bugs.

My Honest Takeaway

The AI coding space in 2026 is wildly different from what I assumed when I started my bootcamp. The expensive models are good — they're really good. But "really good for $3.00/M" is not actually better than "almost-as-good for $0.25/M" when you're coding twelve hours a day and watching your credit card.

I genuinely thought the priciest model would win every test. I had no idea the gap was so small while the price gap was so enormous. The whole experience made me rethink how I budget my learning costs. Now I spend a few dollars a month to code much faster, instead of paying a tenth of my rent for slightly fancier outputs.

If you're a bootcamp grad, a junior dev, or just

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