Fastest AI APIs 2026 — Speed Benchmarks for 15 Models (TTFT & Tokens/sec)

Speed is the silent killer of user experience. Every 100ms of latency costs you conversions. For AI-powered products, the difference between a 200ms response and a 2000ms response determines whether users stay or leave.

We benchmarked 15 models on TTFT (Time to First Token) and sustained tokens/second across Global API's infrastructure, from multiple geographic regions.

TL;DR: DeepSeek V4 Flash leads at ~60 tok/s with ~180ms TTFT. Step-3.5-Flash is the speed champion at ~80 tok/s. Hunyuan-TurboS is the best budget-fast model at $0.28/M.

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Benchmark Setup

| Parameter | Value | |-----------|-------| | Test Date | May 20, 2026 | | Test Region | US East (Ohio), Asia (Singapore) | | Test Prompt | "Explain recursion in 200 words" | | Output Tokens | ~150 tokens per test | | Iterations | 10 runs, average recorded | | Streaming | Yes (SSE) | | API | Global API (https://global-apis.com/v1) |

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Speed Rankings (Fastest to Slowest)

| 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 |

Note: Reasoning/thinking models (R1, K2.5, K2-Thinking) include internal thinking time before the first visible token.

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Speed by Price Tier

Ultra-Budget (< $0.15/M)

| Model | tok/s | $/M | |-------|-------|-----| | Qwen3-8B | 70 | $0.01 | | Step-3.5-Flash | 80 | $0.15 |

Qwen3-8B is absurd value — 70 tok/s at $0.01/M. For simple tasks where speed matters more than quality, it's unbeatable.

Budget ($0.15-$0.30/M)

| Model | tok/s | $/M | |-------|-------|-----| | DeepSeek V4 Flash | 60 | $0.25 | | Hunyuan-TurboS | 55 | $0.28 | | Qwen3-32B | 45 | $0.28 |

DeepSeek V4 Flash wins — 60 tok/s with GPT-4o-class quality at $0.25/M. The sweet spot.

Mid-Range ($0.30-$0.80/M)

| Model | tok/s | $/M | |-------|-------|-----| | Doubao-Seed-Lite | 50 | $0.40 | | GLM-4-32B | 38 | $0.56 | | Hunyuan-Turbo | 42 | $0.57 | | DeepSeek V4 Pro | 30 | $0.78 |

Speed drops here because these are larger models. V4 Pro at 30 tok/s is slower but noticeably higher quality.

Premium ($0.80+/M)

| Model | tok/s | $/M | |-------|-------|-----| | MiniMax M2.5 | 28 | $1.15 | | GLM-5 | 25 | $1.92 | | Kimi K2.5 | 20 | $3.00 |

These models prioritize quality over speed. Use them when correctness is critical and latency is secondary.

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Geographic Latency

We tested from two regions to measure network impact:

| 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 |

Asian models (Qwen, GLM, Kimi) have ~16-20% lower latency from Asia due to server proximity. DeepSeek is well-distributed globally.

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Real-World Impact

Chat Application (User Experience)

| TTFT | User Perception | |------|----------------| | < 200ms | "Instant" — Excellent UX | | 200-400ms | "Fast" — Acceptable | | 400-800ms | "Noticeable delay" — Some users frustrated | | 800ms+ | "Slow" — Users leave |

Recommendation: Use models with TTFT < 400ms for interactive chat. DeepSeek V4 Flash (180ms) and Qwen3-32B (250ms) are ideal.

Streaming Content Generation

For long-form content (articles, reports), sustained tok/s matters more than TTFT:

| Model | tok/s | 1000-Word Article | |-------|-------|-------------------| | DeepSeek V4 Flash | 60 | ~12 seconds | | Qwen3-32B | 45 | ~16 seconds | | Kimi K2.5 | 20 | ~35 seconds |

API Automation (Non-Interactive)

For background jobs where latency doesn't matter:

| Priority | Best Model | |----------|-----------| | Speed + Quality | DeepSeek V4 Flash | | Pure Speed | Step-3.5-Flash | | Quality over Speed | DeepSeek V4 Pro |

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Streaming Code Example

from openai import OpenAI

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

# Streaming with DeepSeek V4 Flash (~60 tok/s)
stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Write a 500-word article about AI speed"}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

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Key Takeaways

1. DeepSeek V4 Flash is the best balance — 60 tok/s, 180ms TTFT, $0.25/M. No other model delivers this speed-quality-price ratio.
2. Step-3.5-Flash for raw speed — 80 tok/s at $0.15/M. Use it for non-critical content generation.
3. Reasoning models are slow — DeepSeek-R1 and Kimi K2.5 have 600-800ms TTFT due to internal reasoning. Only use when you need the quality.
4. Streaming is essential — Always use stream=True for interactive apps. It makes 800ms TTFT feel like 200ms because the user sees text appearing.
5. Geographic location matters — Asian models are 16-20% faster from Asia. Global API routes to the nearest server automatically.

👉 Test All Models — 100 Free Credits

Benchmarks run May 20, 2026 on Global API infrastructure. Results may vary based on server load and network conditions.