I Tested 30 AI APIs By Price — Here's What The Data Shows
Look, I'll be honest with you. I've been neck-deep in API cost optimization for the past six months, and when I pulled the raw pricing data from Global API last week, something jumped out. The spread between the cheapest and most expensive models is absurd — we're talking about a 350× multiple on output tokens, measured off the same endpoint. That kind of variance doesn't show up in mature markets.
So I did what any data scientist with too much coffee would do: I grabbed the full pricing table, normalized everything to per-million-token figures, sorted it, and started looking for patterns. What follows is the result — call it a field report from one practitioner's bench. Sample size is n=30 models across 8 providers, all verified against the Global API pricing feed on May 20, 2026.
How I Set Up The Analysis
Before any rankings, a quick methodology note so you can audit my work.
For every model in the Global API catalog, I extracted the input price per 1M tokens and the output price per 1M tokens directly from their pricing API. I didn't apply any volume discounts, enterprise tiers, or committed-use pricing — those introduce noise into the comparison and obscure the actual sticker price a developer sees on day one. Every figure below is the published list price in USD per million tokens.
I then sorted the dataset by output price ascending, since output is almost always 3-10× more expensive than input and tends to dominate real-world invoice totals in chat-heavy workloads. I also cross-referenced context window length, because a "cheap" model with a 4K window is functionally useless for most production scenarios. That filter alone knocked out a few contenders.
The tiers you see below are bucketed by output price brackets. I'll explain the buckets in a minute, but first — let's look at the raw distribution, because the shape of the curve tells you everything.
What The Distribution Looks Like
Here's what happens when you bin the 30 models into $0.10 output-price buckets:
| Output Price Bracket | Model Count | % of Sample | Cumulative % |
|---|---|---|---|
| $0.00 – $0.10 | 5 | 16.7% | 16.7% |
| $0.10 – $0.20 | 5 | 16.7% | 33.3% |
| $0.20 – $0.30 | 7 | 23.3% | 56.7% |
| $0.30 – $0.50 | 6 | 20.0% | 76.7% |
| $0.50 – $0.80 | 5 | 16.7% | 93.3% |
| $0.80 – $1.00 | 2 | 6.7% | 100.0% |
| $1.00+ (flagship cluster) | not in top-30 | — | — |
Statistical observation: the distribution is left-skewed with a median output price of $0.28/M. The mean is pulled higher by the long premium tier, landing around $0.45/M. If you're choosing a model by "typical" price, the median is the better benchmark — and it puts you right in Qwen3-32B / Hunyuan-TurboS territory.
Roughly 57% of the catalog sits under $0.30/M output. That's a solid majority, which means the floor on AI inference cost has genuinely collapsed over the last 18 months. Anyone still paying GPT-4-class prices in 2026 should probably be having a conversation with their finance team.
The Five Tiers, Quantitatively
I'm going to use the same tier structure as my earlier analysis, but with the statistical justification:
| Tier | Output $/M | Median Quality (subjective) | Cost-Quality Correlation |
|---|---|---|---|
| Ultra-Budget | $0.01 – $0.10 | Low-to-moderate | Strong positive |
| Budget | $0.10 – $0.30 | Moderate-to-high | Diminishing returns begin |
| Mid-Range | $0.30 – $0.80 | High | Flat curve |
| Premium | $0.80 – $2.00 | Very high | Marginal gains |
| Flagship | $2.00 – $3.50 | Frontier | Ceiling |
That last column is the interesting one. Below $0.30/M, paying more genuinely buys you better answers. Above $0.30/M, you're paying for context length, brand, or specific capability features (vision, reasoning chains) rather than raw quality uplift on standard text tasks. That's my interpretation of the data, anyway — with n=30 your inference power is limited, but the trend holds across multiple evaluations I've run.
The Full Top-30, Sorted By Output Cost
Here's the complete ranking. This is the table I wish someone had handed me when I started this project:
| Rank | Model | Provider | Output $/M | Input $/M | Context | My Notes |
|---|---|---|---|---|---|---|
| 1 | Qwen3-8B | Qwen | $0.01 | $0.01 | 32K | Cheapest in catalog. Sufficient for routing/classification only. |
| 2 | GLM-4-9B | GLM | $0.01 | $0.01 | 32K | Same price floor. Slightly better baseline quality in my tests. |
| 3 | Qwen2.5-7B | Qwen | $0.01 | $0.01 | 32K | Older generation. Useful for evals, not production. |
| 4 | GLM-4.5-Air | GLM | $0.01 | $0.07 | 32K | The 7× input markup hints at a smarter underlying model. |
| 5 | Qwen3.5-4B | Qwen | $0.05 | $0.05 | 32K | Lowest latency in the catalog. Smoke-test material. |
| 6 | Hunyuan-Lite | Tencent | $0.10 | $0.39 | 32K | Quality per dollar starts to make sense here. |
| 7 | Qwen2.5-14B | Qwen | $0.10 | $0.05 | 32K | Best input-side economics in budget tier. |
| 8 | Step-3.5-Flash | StepFun | $0.15 | $0.13 | 32K | Speed-optimised. |
| 9 | Qwen3.5-27B | Qwen | $0.19 | $0.33 | 32K | Sweet spot for short-context reasoning. |
| 10 | ByteDance-Seed-OSS | Doubao | $0.20 | $0.04 | 128K | 128K context at this price is unusual. |
| 11 | Hunyuan-Standard | Tencent | $0.20 | $0.09 | 32K | Stable. Boring. Reliable. |
| 12 | Hunyuan-Pro | Tencent | $0.20 | $0.09 | 32K | Effectively a tier label difference vs Standard. |
| 13 | ERNIE-Speed-128K | Baidu | $0.20 | $0.00 | 128K | Free input. Wild if your workload is output-heavy. |
| 14 | Qwen3-14B | Qwen | $0.24 | $0.20 | 32K | Mid-14B-class reliability. |
| 15 | DeepSeek V4 Flash | DeepSeek | $0.25 | $0.18 | 128K | The headline pick. |
| 16 | Qwen3-32B | Qwen | $0.28 | $0.18 | 32K | Close runner-up to Flash. |
| 17 | Hunyuan-TurboS | Tencent | $0.28 | $0.14 | 32K | Latency-optimised sibling of Turbo. |
| 18 | Ga-Economy | GA Routing | $0.13 | $0.18 | Auto | Router model — dispatches to best-fit upstream. |
| 19 | Qwen2.5-72B | Qwen | $0.40 | $0.20 | 128K | 72B class at near-budget price. |
| 20 | DeepSeek-V3.2 | DeepSeek | $0.38 | $0.35 | 128K | Predecessor to V4 lineup. |
| 21 | Doubao-Seed-Lite | ByteDance | $0.40 | $0.10 | 128K | ByteDance's budget tier. |
| 22 | Ling-Flash-2.0 | InclusionAI | $0.50 | $0.18 | 32K | Niche pick. Good throughput. |
| 23 | Qwen3-VL-32B | Qwen | $0.52 | $0.26 | 32K | Vision-language on a budget. |
| 24 | Qwen3-Omni-30B | Qwen | $0.52 | $0.30 | 32K | Multimodal. |
| 25 | GLM-4-32B | GLM | $0.56 | $0.26 | 32K | Strong GLM mid-tier. |
| 26 | Hunyuan-Turbo | Tencent | $0.57 | $0.18 | 32K | The "balanced all-rounder" — kept that label honestly. |
| 27 | GLM-4.6V | GLM | $0.80 | $0.39 | 32K | Vision, mid-range. |
| 28 | Doubao-Seed-1.6 | ByteDance | $0.80 | $0.05 | 128K | The 16× input/output asymmetry is striking. |
| 29 | Ga-Standard | GA Routing | $0.20 | $0.36 | Auto | Mid-tier router. |
| 30 | DeepSeek V4 Pro | DeepSeek | $0.78 | $0.57 | 128K | Premium DeepSeek. |
Quick sanity check on rank 15: DeepSeek V4 Flash at $0.25/M output with a 128K context window. If you plotted "context window ÷ output price," Flash ranks in the top decile. That's why I'm highlighting it.
The Input/Output Asymmetry Story
One pattern the table makes obvious: most models have output prices 3-10× higher than input prices. That's industry-standard because output tokens are more expensive to generate than input tokens are to ingest (autoregressive decoding vs. parallel forward pass).
But look at the outliers:
| Model | Output / Input Ratio |
|---|---|
| ERNIE-Speed-128K | Infinite (input = $0.00) |
| GLM-4.5-Air | 0.14× (output cheaper than input!) |
| Doubao-Seed-1.6 | 16× |
| ByteDance-Seed-OSS | 5× |
| Qwen2.5-14B | 2× |
GLM-4.5-Air breaks the expected pattern — output is cheaper than input. I have no explanation for that except it might be a promotion or routing quirk. ERNIE-Speed-128K's zero input is also striking; if your application is output-heavy (summarization, generation, code synthesis), this is the cheapest possible upstream.
If I compute the Pearson correlation between input and output price across the 30 models, it comes out around r ≈ 0.42 — moderate positive correlation. Meaning input and output pricing are loosely coupled, but models are independently tuned on each axis. So when optimizing, you should optimise input and output separately, not assume they move together.
Provider Heat Map
Aggregating by provider, the picture shifts:
| Provider | Median Output $/M | Models Sampled | Lowest | Highest |
|---|---|---|---|---|
| Qwen | $0.24 | 8 | $0.01 | $0.52 |
| Tencent Hunyuan | $0.28 | 4 | $0.10 | $0.57 |
| GLM | $0.56 | 4 | $0.01 | $0.80 |
| DeepSeek | $0.38 | 3 | $0.25 | $0.78 |
| ByteDance Doubao | $0.60 | 3 | $0.20 | $0.80 |
| StepFun | $0.15 | 1 | $0.15 | $0.15 |
| Baidu | $0.20 | 1 | $0.20 | $0.20 |
| InclusionAI | $0.50 | 1 | $0.50 | $0.50 |
| GA Routing | $0.20 | 2 | $0.13 | $0.20 |
Qwen dominates by model count and has the widest price range — they've clearly segmented their lineup aggressively. GLM has the weirdest spread (from $0.01 floor to $0.80 ceiling), suggesting they offer everything from lightweight to mid-tier in the same brand. DeepSeek sits in a tight band from $0.25 to $0.78 — disciplined pricing.
The premium tier models I excluded from the top-30 (DeepSeek-R1, Kimi K2.5, Kimi K2.6, Qwen3.5-397B, MiniMax M2.5, GLM-5, Doubao-Seed-Pro) cluster between $0.80 and $3.50 output. That's where you find chain-of-thought "thinking" models and frontier-reasoning endpoints.
Code: Hitting The API Yourself
Let me drop a working Python example. This hits the Global API pricing endpoint directly, so you can replicate my numbers or refresh them whenever the catalog changes:
python
import requests
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