How I Tested Every AI API for Speed in 2026 — A Dev's Take
I'll be honest with you — I almost shipped a chatbot that felt like it was powered by a 56k modem. The first time a user demo'd it on screen, I watched their face fall as the loading cursor spun for two full seconds before the first word appeared. That's an eternity in real-world UX. So I went down a rabbit hole. I wanted to know which AI APIs actually feel fast, not just which ones look fast on a spec sheet. What followed was two weeks of running benchmarks across 15 different models on Global API's infrastructure, and let me show you everything I learned.
Let me walk you through what I found, why it matters, and how you can run your own speed tests in about ten minutes.
Why I Care So Much About Latency
Here's the thing — when I'm building a product, every model looks roughly the same in a code editor. You send a prompt, you get text back, you ship the feature. But the moment real users start clicking buttons, the difference between a snappy 200ms response and a sluggish 2-second one becomes obvious. I've seen products die not because the AI was bad, but because users got tired of waiting.
The rule of thumb I keep hearing (and that matches my own tests) is that every 100ms of extra latency eats into your conversion rate. For chat interfaces in particular, anything over 400ms starts feeling sluggish to most people. And that's where TTFT — Time to First Token — comes in. It's the metric that matters most for interactive apps, because it measures how long until the user sees something on screen.
So I picked 15 models, fired them up through Global API, and let the benchmarks rip.
How I Set Up the Tests
Before we get to the rankings, here's exactly how I ran everything. I'm a stickler for reproducibility, so if you want to repeat my results, you can.
- When: May 20, 2026
- Where: US East (Ohio) and Asia (Singapore)
- Prompt: "Explain recursion in 200 words"
- Output length: roughly 150 tokens per run
- Runs: 10 iterations per model, then I averaged them
- Streaming: Yes, SSE for all of them
-
Endpoint:
https://global-apis.com/v1
I picked the recursion prompt because it's representative — short enough to keep latency measurements clean, but complex enough that the model can't just spit out a memorized answer in one chunk.
The Big Speed Leaderboard
Okay, here's the moment you've been waiting for. Drum roll please — these are the 15 models I tested, ranked from screaming fast to "definitely a thinking model."
| Rank | Model | TTFT (ms) | Tokens/sec | Provider | $/M Output |
|---|---|---|---|---|---|
| 🥇 | Step-3.5-Flash | 120 | 80 | StepFun | $0.15 |
| 🥈 | DeepSeek V4 Flash | 180 | 60 | DeepSeek | $0.25 |
| 🥉 | Hunyuan-TurboS | 200 | 55 | Tencent | $0.28 |
| 4 | Qwen3-8B | 150 | 70 | Qwen | $0.01 |
| 5 | Qwen3-32B | 250 | 45 | Qwen | $0.28 |
| 6 | Doubao-Seed-Lite | 220 | 50 | ByteDance | $0.40 |
| 7 | Hunyuan-Turbo | 280 | 42 | Tencent | $0.57 |
| 8 | GLM-4-32B | 300 | 38 | Zhipu | $0.56 |
| 9 | Qwen3.5-27B | 350 | 35 | Qwen | $0.19 |
| 10 | DeepSeek V4 Pro | 400 | 30 | DeepSeek | $0.78 |
| 11 | MiniMax M2.5 | 450 | 28 | MiniMax | $1.15 |
| 12 | GLM-5 | 500 | 25 | Zhipu | $1.92 |
| 13 | Kimi K2.5 | 600 | 20 | Moonshot | $3.00 |
| 14 | DeepSeek-R1 | 800 | 15 | DeepSeek | $2.50 |
| 15 | Qwen3.5-397B | 1200 | 10 | Qwen | $2.34 |
A quick note about the bottom of the table — those slow speeds on R1, K2.5, and the giants like Qwen3.5-397B aren't because they're poorly optimized. They're "reasoning" or "thinking" models, meaning the model pauses internally to plan before spitting out visible tokens. That extra thinking is by design, and for many tasks it's worth it. But for a snappy chat UI? Probably not your first pick.
My Honest Take on the Top Three
Let me dig into the podium a bit, because each of these won for a different reason.
Step-3.5-Flash at 80 tok/s and 120ms TTFT is the raw speed king. Honestly, when I first saw this number I thought something was wrong — it felt unreal. But I reran it three times and the average held. At $0.15 per million output tokens, it's also dirt cheap. The catch? It leans toward lighter tasks. If you need GPT-4o-class reasoning, this probably isn't your daily driver.
DeepSeek V4 Flash is the model I keep coming back to. 180ms TTFT and 60 tokens per second, with quality I've found comparable to heavier models. At $0.25 per million output tokens, it's the sweet spot I recommend to most builders right now. If I had to pick one model for a generic chat product, it'd be this one.
Hunyuan-TurboS from Tencent clocks in at 200ms TTFT and 55 tok/s for $0.28 per million. It's the budget-fast champion — slightly slower than V4 Flash but consistently reliable. I noticed it handles Chinese-to-English translation particularly well, which makes sense given its lineage.
Speed Sorted by How Much You Want to Spend
Here's how I'd group things based on what you're willing to spend per million output tokens. Let me show you the breakdown:
The "Wow, That's Cheap" Tier (under $0.15/M)
- Qwen3-8B at 70 tok/s for $0.01/M
- Step-3.5-Flash at 80 tok/s for $0.15/M
I'll be real with you — Qwen3-8B at one cent per million tokens is borderline absurd. For a high-volume, lower-stakes workload (think: autocomplete suggestions, simple classification, content tagging), this model is hard to beat. It punches way above its price.
The Sweet Spot Tier ($0.15–$0.30/M)
- DeepSeek V4 Flash at 60 tok/s for $0.25/M
- Hunyuan-TurboS at 55 tok/s for $0.28/M
- Qwen3-32B at 45 tok/s for $0.28/M
This is the tier I keep recommending to people. DeepSeek V4 Flash earns my top spot here because the combination of speed, quality, and price is hard to argue with.
The Mid-Range ($0.30–$0.80/M)
- Doubao-Seed-Lite at 50 tok/s for $0.40/M
- GLM-4-32B at 38 tok/s for $0.56/M
- Hunyuan-Turbo at 42 tok/s for $0.57/M
- DeepSeek V4 Pro at 30 tok/s for $0.78/M
Speed starts dropping here because you're paying for bigger, smarter models. The quality jump is real though — V4 Pro at 30 tok/s is noticeably more thoughtful than V4 Flash.
Premium ($0.80+/M)
- MiniMax M2.5 at 28 tok/s for $1.15/M
- GLM-5 at 25 tok/s for $1.92/M
- Kimi K2.5 at 20 tok/s for $3.00/M
These are the "I really need this to be correct" tier. If you're generating code that's about to ship to production, or doing financial analysis, this is where I'd land.
Geography Actually Matters
One thing that surprised me — server proximity makes a real difference. I tested from both US East and Asia, and here's what came back:
| Model | US East TTFT | Asia TTFT | Diff |
|---|---|---|---|
| DeepSeek V4 Flash | 180ms | 150ms | -30ms |
| Qwen3-32B | 250ms | 210ms | -40ms |
| GLM-5 | 500ms | 420ms | -80ms |
| Kimi K2.5 | 600ms | 480ms | -120ms |
The pattern is clear: Chinese-origin models (Qwen, GLM, Kimi) get a 16–20% latency haircut when called from Asia. Kimi K2.5 in particular saved a whopping 120ms just from being closer to its home region. DeepSeek was the most geographically balanced — its 30ms difference is basically noise.
The takeaway? If your users are concentrated in a specific region, pick a model that's hosted close to them. Sounds obvious, but I've definitely seen people ignore this.
What These Numbers Actually Mean for Users
Let me translate this into something you can use. Here's the user-perception mapping I've internalized from running these tests and watching real users interact:
| TTFT | What users feel |
|---|---|
| Under 200ms | "Instant" — the gold standard |
| 200–400ms | "Fast" — totally acceptable |
| 400–800ms | "Noticeable delay" — some users get twitchy |
| 800ms+ | "Slow" — users start thinking it's broken |
For any interactive chat product, I'd aim for under 400ms TTFT. That puts DeepSeek V4 Flash (180ms), Qwen3-8B (150ms), Step-3.5-Flash (120ms), Hunyuan-TurboS (200ms), and Doubao-Seed-Lite (220ms) all in the sweet spot.
Let Me Show You How to Test This Yourself
Here's the practical bit. If you want to recreate my exact tests, here's how I'd do it. First, install the OpenAI Python client — Global API speaks the same wire protocol, so you can use it as a drop-in replacement.
python
import time
import statistics
from openai import OpenAI
client = OpenAI(
api_key="YOUR_GLOBAL_API_KEY",
base_url="https://global-apis.com/v1"
)
def benchmark_model(model_name, prompt="Explain recursion in 200 words", runs=10):
ttft_list = []
tps_list = []
for _ in range(runs):
start = time.perf_counter()
first_token_time = None
token_count = 0
stream = client.chat.completions.create(
model=model_name,
messages=[{"role": "user", "content": prompt}],
stream=True
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