2026 Free AI API Speed Ranking: Cerebras Leads, Groq & Mistral Close Behind
Test period: 2026-08-24 to 2026-08-25, Beijing time; models are each channel's primary free-tier model; sample size n=200 requests/channel.
1. Overall Speed Ranking (output tokens/s, higher is better)
| Rank | Channel | Model | Output speed (tokens/s) | Time to first token (ms) | Context limit | Stability (1–5) |
|---|---|---|---|---|---|---|
| 1 | Cerebras | Llama 3.3 70B | 1200–2000 | 180–320 | 128K | 4.3 |
| 2 | Groq | Llama 3.3 70B | 500–1000 | 220–380 | 128K | 4.6 |
| 3 | Mistral | Mistral Small | 300–600 | 260–420 | 32K | 4.5 |
| 4 | Cloudflare Workers AI | Llama 3.1 8B | 80–150 | 300–500 | 8K | 4.2 |
| 5 | Hugging Face Inference | Meta-Llama-3-8B | 40–90 | 350–600 | 8K | 3.8 |
| 6 | Together AI (free credits) | Llama 3.1 70B | 200–400 | 280–450 | 128K | 4.0 |
Note: Together AI's free credits are limited ($25 on signup); once exhausted, it switches to paid. The rest are long-term free tiers.
2. Key Metrics Explained
1) Output speed (tokens/s)
Determines "how long to finish a sentence." For real-time chat/streaming summaries, aim for ≥300 t/s.
2) Time to first token (TTFT)
Determines "how soon you see the first character after clicking." Interactive products should target ≤400ms.
3) Context limit
Long-document processing and multi-turn dialogue benefit from larger context; 128K is better suited for enterprise-grade applications.
4) Stability
Composite score (1–5) based on 7-day availability, rate-limit frequency, and error rates.
3. Scenario-Based Selection Advice
- Real-time chatbots: Cerebras or Groq (speed-first)
- Long-document summarization / RAG: Cerebras / Groq / Together (128K context)
- Low-cost lightweight tools: Cloudflare Workers AI (generous free tier, ideal for lower DAU)
- Multi-model routing for resilience: Our Unified API supports automatic fallback "Cerebras → Groq → Mistral"
4. Why Is Cerebras So Fast?
Cerebras uses the Wafer-Scale Engine (WSE-3), integrating trillions of transistors on a single chip with massive on-chip SRAM, avoiding the HBM bandwidth bottlenecks common in GPUs. Its inference stack is end-to-end optimized for Transformers, enabling 70B models to reach ~2000 t/s.
5. Test Methodology (Reproducible)
- Prompt: fixed 50-token instruction + request for 500-token output;
- Concurrency: single-connection serial requests to avoid interference;
- Region: Beijing egress (Cloudflare/Groq/Cerebras all route to US-West);
- Statistics: trim top/bottom 10% outliers, report median and interquartile range.
6. Update Policy
- Monthly updates (immediate updates for major model/channel changes);
- Submit your实测 data to verify@samai.cc for inclusion in the next edition.
7. Next Steps
- Read "Cerebras Free Inference API" for a quick start;
- Use our Unified API to switch between free channels with one call;
- Follow the "Rankings" category — upcoming editions will cover cost, quality, and regional availability.
🚀 Get Started: One-Click Free API Access
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👉 Register on Apishare.cc → Get your unified API Key
📊 Want to see more free model rankings? Check out the Sep 2026 Free LLM API Rankings →
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