I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take

I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take

I'll be honest — I went into this expecting a clear winner. I came out with a scatter plot, three regressions, and a deeper appreciation for why "best" is the most dangerous word in machine learning.

Over the past three weeks I've been grinding through prompts with ten different LLMs, all routed through the same endpoint, scoring every output on a 1–10 rubric that I tried very hard not to bias. The pricing data is pulled directly from the provider pages. The scores are mine. If you disagree with a score, you're probably right — n=1 per task per model is a laughably small sample size, and I say that as someone who publishes papers with bigger samples. But trends still emerged. Let me walk you through what I found.

Before I touch a single benchmark, here's the cast. I've grouped them by family so you can see the obvious concentration in the open-source Chinese ecosystem, which personally I find fascinating — three of the top five are DeepSeek or Qwen variants.

One quick note on Ga-Standard — it's a routing layer that picks a backend model per request. So the score fluctuates. I averaged across runs.

Five prompts. Each one designed to probe a different cognitive layer:

Scoring rubric: correctness (40%), code quality (25%), documentation (15%), edge-case coverage (20%). I capped myself at 10 minutes per evaluation to avoid scope creep, which is itself a form of bias — slower reviewers tend to dock points for verbosity.

Every model was hit with identical prompts, identical temperature (0.2), identical system message. I rotated the order to control for fatigue.

The Ga-Standard asterisk is doing a lot of work here. It's the highest value-per-dollar on the list, but it's not a model — it's a router. Treat it like a moving target.

I plotted quality against price and ran a quick Pearson correlation. The result: r = 0.31, p ≈ 0.38. Not statistically significant. In English: spending more does not reliably buy you better code generation in 2026. The cheap tier is competitive with the premium tier more often than the pricing would suggest.

That single chart changed how I think about LLM procurement. We pay 10x for Kimi K2.5 versus DeepSeek V4 Flash and get, on average, 0.3 points of quality. That's not a deal — that's a rounding error.

Easy opener. Or so I thought.

DeepSeek-R1 won this round by being the only model that volunteered a complexity analysis without being asked. That's a reasoning premium, and it's the reason R1 sits at $2.50/M output. You're paying for the inner monologue.

The bug:

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. Not a single one missed it. That's either a sign these benchmarks are too easy, or that async debugging is well-represented in training data. Probably both.

Tie: DeepSeek V4 Flash and Qwen3-Coder-30B. I'd give the slight edge to Qwen3-Coder-30B on production-readiness because it wrapped the fix in a try/catch without being prompted.

This is where the tiers separated.

R1 doesn't just write Dijkstra — it writes the Dijkstra you'd write if you were being graded. That's the reasoning tax. At $2.50/M output, it's also where your cost projection starts to matter.

I fed each model a deliberately vulnerable Go snippet: SQL injection, unchecked error, goroutine leak. Pure security triage.

The goroutine leak was a deliberately tricky ask. R1 caught it. Premium tier matters here. If you're shipping Go services in prod, the $2.50/M for R1 starts to look cheap relative to a CVE.

The big one. Filter, paginate, error handling, the works.

R1's output was 180 lines for what should have been 60. But every edge case was covered. Whether that's a feature or a bug depends on your team. In a startup shipping fast, you want Qwen3-Coder-30B. In a regulated environment, you want R1 and a longer PR review cycle.

Let's say you're a team of 10 engineers, each making ~200 LLM calls per day averaging 500 output tokens. That's 1M tokens/day.

So your premium-tier stack — R1 + Kimi — costs $1,980/year versus $90/year for DeepSeek V4 Flash. That's a 22x delta. Quality delta? About 0.7 points on my rubric, or 8%. Statistically, depending on your tolerance, that's not nothing. But it's also not a Ferrari-vs-Toyota situation.

If I were CFO of an engineering org I'd route 80% of calls through DeepSeek V4 Flash and reserve R1 for genuinely hard problems. That's a $720 annual saving on a 10-person team while keeping quality within 0.4 points of the best.

Here's the production snippet I've been running my tests against. Global API gives you a single endpoint that hits every model in this benchmark, which means I didn't have to manage ten different SDKs. Genuinely a quality-of-life improvement.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["GLOBAL_API_KEY"],
    base_url="https://global-apis.com/v1"
)

def review_code(model: str, code: str, language: str) -> str:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": f"You are a senior {language} engineer."},
            {"role": "user", "content": f"Review this code for security and performance:\n\n{code}"}
        ],
        temperature=0.2,
        max_tokens=1500
    )
    return response.choices[0].message.content

def smart_review(code: str, language: str, difficulty: str) -> str:
    if difficulty == "hard":
        return review_code("deepseek-r1", code, language)
    return review_code("deepseek-v4-flash", code, language)

That last function is exactly the routing logic I'd ship. Cheap model for the 90% case, expensive model for the 10% that matters.

I bucketized by tier and computed mean score. Standard deviation included for the skeptics.

The mid tier is the worst of both worlds — wider variance, no price advantage. The premium tier has higher variance too, but the upside is real (Kimi K2.5 hit 9.2 on the Express task). The budget tier is the most consistent. Mean of 8.6 with a 0.21 std dev is remarkable for $0.25/M.

If you forced me to pick one model for a coding startup: