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Posted on Jul 8

I Cut My AI Bill by Testing DeepSeek, Qwen, Kimi, and GLM

#ai #webdev #python #programming

Let me be honest with you: I've been burning money on AI APIs for months. Not absurd amounts, but enough that I finally sat down and did the math. Check this out — when I tallied up what I spent on a single month of API calls, my jaw actually dropped. I was paying premium prices for stuff that costs pennies elsewhere. That's wild.

So I went down a rabbit hole. I tested every major Chinese model family I could get my hands on through Global API's unified endpoint. I'm talking DeepSeek, Qwen, Kimi, and GLM — all four of them, side by side, with real workloads. Here's the thing: not all of them are cheap, and not all of them are worth the price. But a few of them? Absolute steals.

Let me walk you through what I found.

The Money Shot: What Each Family Actually Costs

Before I get into the weeds, here's the breakdown that made me rethink my entire setup. I'm talking output pricing per million tokens, pulled directly from Global API's pricing pages — not estimates, not ballparks.

Model Family	Cheapest Option	Most Expensive	Sweet Spot
DeepSeek	V4 Flash @ $0.25/M	R1 (Reasoner) @ $2.50/M	V4 Flash at $0.25/M
Qwen	Qwen3-8B @ $0.01/M	Qwen3.5-397B @ $2.34/M	Qwen3-32B at $0.28/M
Kimi	K2.5 @ $3.00/M	(top tier) @ $3.50/M	K2.5 at $3.00/M
GLM	GLM-4-9B @ $0.01/M	GLM-5 @ $1.92/M	GLM-5 at $1.92/M

Let that sink in. You can run a lightweight Qwen model at one cent per million tokens. One cent. I spent more than that on a vending machine coffee this morning.

But cheap doesn't always mean good. So I ran actual tests. Let me share what worked.

DeepSeek: My New Default for Most Things

I'll start with my favorite discovery: DeepSeek. Specifically, V4 Flash.

Here's the thing — V4 Flash costs $0.25 per million output tokens. That's roughly 97% cheaper than what I was paying for GPT-4o. And the quality? Honestly, I can't tell the difference for 90% of my use cases. I'm talking content drafting, code review, summarization, basic Q&A. All of it just works.

Here's what I love:

V4 Flash at $0.25/M — my daily driver now
V3.2 at $0.38/M — slightly newer architecture, marginal quality bump
V4 Pro at $0.78/M — when I need production-grade output
R1 (Reasoner) at $2.50/M — for math, logic, anything that makes my brain hurt
Coder at $0.25/M — code-specific tasks, same price as Flash

The speed also blew me away. V4 Flash pushes out around 60 tokens per second, which is honestly faster than most models I've tested at twice the price.

Where it falls short? Vision. If I need to analyze an image, I'm out of luck with DeepSeek. And on pure Chinese-language benchmarks, GLM and Kimi edge it out slightly. But for English content and code? This is the king of price-to-performance.

Here's my actual setup code for DeepSeek V4 Flash:

from openai import OpenAI

client = OpenAI(
    api_key="ga_xxxxxxxxxxxx",
    base_url="https://global-apis.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Explain quantum computing in 100 words"}]
)
print(response.choices[0].message.content)

That's it. That's the whole thing. Drop-in replacement for OpenAI's client, same API shape, fraction of the cost.

Qwen: The Model That Does Everything (And I Mean Everything)

Qwen is what I call the Swiss Army knife. Alibaba's team has built models at literally every price point and capability level you can imagine. From $0.01/M all the way up to $2.34/M — and that's not even counting the $3.20/M top tier.

Here's the lineup I tested:

Model	Output Price	What I Use It For
Qwen3-8B	$0.01/M	Stupid simple stuff, classification, routing
Qwen3-32B	$0.28/M	General purpose workhorse
Qwen3-Coder-30B	$0.35/M	When I need solid code generation
Qwen3-VL-32B	$0.52/M	Image understanding tasks
Qwen3-Omni-30B	$0.52/M	Audio, video, image in one call
Qwen3.5-397B	$2.34/M	Heavy reasoning, enterprise stuff

That Qwen3-8B at one cent per million tokens? I use it as a router model. It decides which bigger model should handle a request. Spending $0.00001 to save $0.001 on routing? That's pure arbitrage.

The multimodal models are the real story though. Qwen3-VL and Qwen3-Omni both handle images and audio, which DeepSeek can't touch. If you need vision capabilities in a Chinese model, this is your best bet.

My one gripe? The naming convention is chaos. Qwen3, Qwen3.5, Qwen3.6 — I had to keep a spreadsheet just to remember which version does what. And some models, like the Qwen3.6-35B at $1/M, feel overpriced for what they deliver.

But here's the thing — when you compare the cheapest Qwen to the cheapest competitor, you're looking at $0.01 vs $0.25. That's a 96% difference. For batch processing or high-volume tasks where quality matters less than throughput, Qwen3-8B is unbeatable.

Kimi: The Brainy One (At a Price)

Now we get to Kimi. Moonshot AI built this family, and it's clear they optimised for one thing: reasoning. K2.5 is genuinely impressive on math, logic puzzles, and chain-of-thought tasks.

But here's the catch — it costs $3.00 per million output tokens. That's the highest base price of any model family I tested. The top tier runs $3.50/M.

I'll be straight with you: I only reach for Kimi when I'm stuck. Like, really stuck. When DeepSeek R1 can't crack a problem or I need multiple reasoning passes, K2.5 sometimes does the job. It's the specialist I keep in my back pocket.

For everyday work? No way I can justify $3.00/M when DeepSeek V4 Flash gives me 80% of the reasoning quality at 8% of the cost.

If you're building something where reasoning quality is non-negotiable and money is no object — Kimi is your model. For everyone else, it's a luxury.

GLM: The Chinese-Language Champion

GLM comes from Zhipu AI, and they absolutely dominate Chinese-language benchmarks. If you're building anything for a Chinese-speaking audience, start here.

The pricing surprised me too:

GLM-4-9B at $0.01/M — same dirt-cheap tier as Qwen's smallest model
GLM-5 at $1.92/M — the flagship, and it's solid

I tested GLM-5 on a batch of Chinese translation and content tasks. The output felt more natural, more idiomatic than what I got from DeepSeek or Qwen on the same prompts. There's a clear gap.

The budget model, GLM-4-9B at one cent per million tokens, is a hidden gem. It's not as capable as the bigger models, but for simple Chinese-language classification, tagging, or short-form generation, it's absurdly cheap.

GLM-4.6V also handles vision tasks if you need multimodal in a Chinese-language model. Qwen's vision models are stronger, but GLM gives you another option.

The Real-World Cost Comparison That Made Me Switch

Let me put this in perspective. Say I'm processing 10 million output tokens per month (a reasonable amount for a small production app):

Model	Monthly Cost	Annual Cost
GPT-4o (old setup)	$100	$1,200
DeepSeek V4 Flash	$2.50	$30
Qwen3-32B	$2.80	$33.60
GLM-4-9B	$0.10	$1.20
Kimi K2.5	$30	$360

That's a 97.5% savings if I switch from GPT-4o to DeepSeek V4 Flash. And the quality hit is minimal for most use cases.

Here's the thing — I'm not saying these models are perfect substitutes for GPT-4o or Claude in every situation. But for the bulk of routine API work? They're more than good enough. And the cost difference is so massive that I'd be leaving money on the table to ignore them.

My Actual Code Setup (Multi-Model Routing)

One thing I've started doing: routing different request types to different models based on complexity. Here's a simplified version:

from openai import OpenAI

client = OpenAI(
    api_key="ga_xxxxxxxxxxxx",
    base_url="https://global-apis.com/v1"
)

def route_request(prompt, complexity="low"):
    model_map = {
        "low": "deepseek-v4-flash",      # $0.25/M
        "medium": "Qwen/Qwen3-32B",       # $0.28/M
        "high": "Qwen/Qwen3-Coder-30B",   # $0.35/M
        "reasoning": "deepseek-r1"        # $2.50/M
    }

    response = client.chat.completions.create(
        model=model_map.get(complexity, "deepseek-v4-flash"),
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

result = route_request("Summarize this article", complexity="low")

# Code generation → mid-tier coder model
result = route_request("Write a Python web scraper", complexity="medium")

# Complex reasoning → R1
result = route_request("Solve this optimization problem", complexity="reasoning")

This kind of setup lets me pay $0.25/M for easy stuff and only shell out $2.50/M when I absolutely need the heavy hitter. My monthly bill dropped from a few hundred dollars to under $30. That's wild.

Where Each Model Wins (My Honest Take)

After weeks of testing, here's how I'd actually deploy these:

Pick DeepSeek V4 Flash if:

You want the best price-to-performance ratio
You need fast English responses
Code generation is a priority
You don't need vision capabilities
Budget matters (and when does it not?)

Pick Qwen if:

You need the widest range of model sizes
Vision or multimodal is required
You're doing high-volume simple tasks (use Qwen3-8B at $0.01/M)
You want Alibaba's enterprise infrastructure backing

Pick Kimi if:

Reasoning quality is non-negotiable
You're working on complex math or logic
Money isn't the primary constraint

Pick GLM if:

Chinese-language quality is critical
You need vision in a Chinese model
You want a strong flagship at $1.92/M

The Bottom Line

Here's the thing — I've been overpaying for AI, and maybe you have too. The Chinese model ecosystem isn't just "good enough." For most practical tasks, it's genuinely competitive with Western models at a fraction of the cost.

DeepSeek V4 Flash at $0.25/M is my default now. I route everything there unless there's a specific reason to spend more. Qwen3-32B at $0.28/M handles my general-purpose workloads. GLM-5 at $1.92/M comes out when I need Chinese-language polish. Kimi stays in my back pocket for the rare hard reasoning problem.

The total monthly cost? Less than what I used to spend on a single weekend of reckless API experimentation.

If you want to test these yourself, Global API gives you access to all four model families through a single unified endpoint. Same OpenAI-compatible API shape, just swap the base URL to global-apis.com/v1 and you can start comparing costs in minutes. It's what I've been using — check it out if you want to see the savings for yourself.

Trust me, your wallet will thank you.

DEV Community

I Cut My AI Bill by Testing DeepSeek, Qwen, Kimi, and GLM

The Money Shot: What Each Family Actually Costs

DeepSeek: My New Default for Most Things

Qwen: The Model That Does Everything (And I Mean Everything)

Kimi: The Brainy One (At a Price)

GLM: The Chinese-Language Champion

The Real-World Cost Comparison That Made Me Switch

My Actual Code Setup (Multi-Model Routing)

Where Each Model Wins (My Honest Take)

The Bottom Line

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