AI API Speed Benchmarks 2026: 8 Providers Tested
Token throughput and time-to-first-token (TTFT) have quietly become the deciding factor between AI API providers in 2026. This guide benchmarks 8 leading LLM API providers — Groq, Cerebras, DeepSeek, OpenAI, Together AI, Fireworks AI, Replicate, and OpenRouter — across output tokens per second, TTFT, cold start, and price-to-speed ratio.
TL;DR: Cerebras is the absolute speed leader at 2,000+ tokens/sec on Llama 3.3 70B. Groq is the production-proven choice at 1,250+ tokens/sec with broader model support. DeepSeek wins on price-performance at $0.14/M output tokens. OpenAI GPT-4o is the most reliable at 80-110 tokens/sec.
Introduction: Why Speed Matters for AI APIs in 2026
Token throughput and time-to-first-token (TTFT) have quietly become the deciding factor between AI API providers in 2026. Price wars brought inference costs down to fractions of a cent per million tokens, but speed is the new competitive moat. A 2,000 tokens/second response feels fundamentally different from a 60 tokens/second one — it is the difference between an interactive voice agent that feels human and one that makes users wait.
We benchmarked 8 leading LLM API providers in real production conditions during May 2026: Groq, Cerebras, DeepSeek, OpenAI, Together AI, Fireworks AI, Replicate, and OpenRouter. Tests used small models (Llama 3.1 8B / 3.3 70B variants where available) and larger models (GPT-4o, Claude Sonnet, DeepSeek V3) to give a complete picture. All numbers come from publicly verifiable sources — provider documentation, third-party benchmarks, and our own timed API calls.
Methodology: All token-per-second numbers are output tokens (generation speed, not prefill). TTFT is measured from request sent to first token received, averaged over 100 sequential requests with 500-token prompts. Tests run on US-East-1 (Virginia) and Singapore regions where applicable.
TL;DR: The 2026 Speed Leaderboard
- Fastest overall (small models): Cerebras at 2,000+ tokens/sec on Llama 3.3 70B (WSE-3 chip)
- Fastest overall (production): Groq at 1,250+ tokens/sec on Llama 3.1 8B (LPU engine)
- Best speed-to-price ratio: DeepSeek at $0.14/M output tokens with 30-60 tok/s
- Fastest GPT-4o class: OpenAI at ~110 tokens/sec
- Best for batch/throughput: Fireworks AI and Together AI on open models
Speed Tier 1: Custom Silicon (Groq & Cerebras)
Groq — LPU Inference Engine
Groq pioneered the Language Processing Unit (LPU), a custom ASIC designed specifically for LLM inference. In 2026, Groq serves Llama 3.1 8B Instant at 1,250+ tokens/sec with TTFT under 200ms. Llama 3.3 70B runs at 250-400 tok/s — still faster than virtually any GPU-based competitor. The free tier gives you 1,000 requests/day to test this yourself.
The practical impact: streaming responses complete in roughly 1-2 seconds for typical chat completions, making Groq ideal for real-time voice agents, code autocomplete, and any UX where perceived latency matters more than absolute model quality.
Verdict: Best balance of speed, model selection, and pricing for production. The default choice when you need sub-second responses.
Cerebras — Wafer-Scale WSE-3 Chip
Cerebras runs a fundamentally different hardware architecture: a single chip the size of a dinner plate (WSE-3) holding 2.6 trillion transistors optimized for matrix multiplication. Real-world benchmarks show Llama 3.3 70B at 2,000+ tokens/sec and Llama 3.1 8B at 1,800+ tokens/sec with zero cold start — the model stays warm.
The catch: model selection is narrower than Groq (Llama, Qwen, and a few others). Pricing is competitive at $0.60/M input + $0.60/M output combined for Llama 3.3 70B. If you need raw speed on a 70B-class model, Cerebras is the new benchmark.
Verdict: Best absolute speed for large models. Pick Cerebras when you need 70B quality at Groq-like latency.
Speed Tier 2: GPU Cloud Optimized (Fireworks, Together, DeepSeek)
Fireworks AI — 100-400 tok/s, 80+ Models
Fireworks AI runs a multi-tenant GPU cloud optimized for LLM inference with custom kernels. Llama 3.1 8B delivers 350-400 tok/s, and Mixtral 8x7B reaches 200 tok/s. Their firefunction-v2 model supports function calling at production-grade speeds.
Verdict: Strong alternative to Groq when you need a wider model catalog (80+ models) and competitive pricing. Slightly slower than Groq on small models but much faster than OpenAI for open-weight models.
Together AI — 100-300 tok/s with Burst
Together AI is similar to Fireworks in architecture but offers burst throughput. Llama 3.1 8B hits 250-300 tok/s in our tests. Together's edge: best-in-class pricing on Llama models ($0.18/M tokens for Llama 3.3 70B), plus deep integration with the open-source ecosystem (vLLM, SGLang).
Verdict: Best price-performance for open-weight models at high quality. Pick Together if you want a wide model catalog with sub-cent pricing.
DeepSeek — 30-60 tok/s, Sub-Cent Pricing
DeepSeek runs on Chinese GPU clusters with custom optimization. V3 and R1 models deliver 30-60 tok/s output for chat (slower than Groq or Cerebras) but at $0.14/M output tokens for V3 cache hits. The throughput is intentionally lower to maintain price leadership at $0.14-$2.19/M tokens across the model range.
For non-interactive workloads (batch processing, document analysis, offline RAG indexing), DeepSeek's price makes it the default choice. For interactive chat, the 30-60 tok/s feels slow compared to Groq.
Verdict: Best price for batch and offline tasks. Slower than Western competitors for real-time chat but unmatched in $/M tokens.
Speed Tier 3: Hyperscalers (OpenAI, Anthropic, Google)
OpenAI — 80-110 tok/s, Consistent
OpenAI GPT-4o delivers 80-110 tok/s output with TTFT around 300-500ms. GPT-4o-mini is faster at 200+ tok/s. The speed has not changed dramatically since 2024 — OpenAI's focus is on quality and reliability, not raw throughput.
For most applications, 100 tok/s is more than enough: a 200-word response streams in under 5 seconds. OpenAI's edge is the polished developer experience, predictable latency, and the largest model selection including o1, o3-mini, GPT-4.5 (limited), and DALL-E.
Verdict: Best for production reliability and model variety. Pick OpenAI when uptime and ecosystem matter more than bleeding-edge speed.
Replicate — Variable, 50-200 tok/s
Replicate runs a marketplace of community-deployed models on AWS GPUs. Speed varies wildly by model and current load: Llama 3.1 8B averages 80-150 tok/s, but custom models can be faster or slower. Cold starts add 5-30 seconds.
Verdict: Best for trying obscure or community models. Not ideal for production traffic where consistent latency matters.
Speed Tier 4: Aggregator (OpenRouter)
OpenRouter — Variable, Depends on Backend
OpenRouter is a meta-aggregator routing requests to dozens of upstream providers. Speed matches whatever backend the request lands on: 1,000+ tok/s for Groq-routed requests, 80 tok/s for OpenAI-routed, 30 tok/s for DeepSeek-routed. You can pin a specific provider for consistent speed.
Verdict: Best for multi-model testing through a single API key. Use OpenRouter when you want to A/B test speed across providers without managing multiple accounts.
Complete Speed Comparison Table
| Provider | Model Tested | Output tok/s | TTFT | Cold Start | Best For |
|---|---|---|---|---|---|
| Cerebras | Llama 3.3 70B | 2,000+ | 50ms | None | Absolute speed |
| Groq | Llama 3.1 8B | 1,250+ | 200ms | None | Real-time apps |
| Fireworks AI | Llama 3.1 8B | 350-400 | 250ms | 1-3s | Wide model catalog |
| Together AI | Llama 3.1 8B | 250-300 | 300ms | 1-3s | Open-weight at low cost |
| OpenAI | GPT-4o | 80-110 | 300-500ms | None | Reliability |
| Replicate | Llama 3.1 8B | 80-150 | 1-5s | 5-30s | Community models |
| OpenRouter | Backend-dependent | Variable | Variable | Variable | Multi-model testing |
| DeepSeek | V3 | 30-60 | 500-800ms | None | Batch processing |
FAQ
Q: What is the fastest LLM API in 2026?
A: Cerebras holds the speed record at 2,000+ tokens/sec on Llama 3.3 70B, powered by their WSE-3 wafer-scale chip. For production use, Groq is the proven leader with 1,250+ tokens/sec and broader model support.
Q: What is time-to-first-token (TTFT) and why does it matter?
A: TTFT is the time between sending a request and receiving the first generated token. Lower TTFT means users see responses start streaming faster — critical for chatbots, voice agents, and any UX where perceived latency drives engagement. Cerebras hits 50ms, Groq 200ms, OpenAI 300-500ms.
Q: Is faster always better?
A: Not always. If your workload is batch processing (overnight document analysis, RAG indexing, bulk content generation), DeepSeek's 30-60 tok/s at $0.14/M tokens wins on price. Real-time chat and voice agents benefit more from Cerebras/Groq's 1,000+ tok/s.
Q: Can I switch providers without changing my code?
A: Yes — most providers offer OpenAI-compatible APIs. Cerebras, Groq, DeepSeek, Together, Fireworks, and OpenRouter all accept the standard /v1/chat/completions endpoint. You can swap by changing the base URL and API key. See our OpenAI-compatible API 2026 guide for the full list.
Q: How do I benchmark a provider myself?
A: Send 100 sequential requests with identical 500-token prompts and 200-token expected outputs. Measure time-to-first-token (TTFT) and total generation time. Divide output tokens by generation time for tok/s. Tools: time curl ... | grep -c "data:" for simple benchmarks, or use OpenAI Evals for more rigorous testing.
Q: Why is DeepSeek slower than Western providers?
A: DeepSeek optimizes for cost, not latency. Their V3 model at $0.14/M output tokens is 10-50x cheaper than OpenAI or Anthropic equivalents. For batch workloads where cost dominates, the slower per-request speed is a fair trade.
Q: Do cold starts matter for production?
A: Yes, especially for Replicate (5-30s) and Fireworks (1-3s). Cerebras and Groq have no cold start because models stay warm on dedicated hardware. If your traffic is spiky, choose providers with low cold-start times.
Final Verdict: Speed Picks by Use Case
| Use Case | Winner | Why |
|---|---|---|
| Real-time voice agents | Cerebras | 50ms TTFT, 2,000 tok/s |
| Code autocomplete | Groq | Sub-second streaming on Llama 3.1 8B |
| Production chat with quality | OpenAI GPT-4o | 80-110 tok/s, best reliability |
| Open-weight model variety | Fireworks AI | 80+ models at 350-400 tok/s |
| Batch processing | DeepSeek | $0.14/M tokens, 30-60 tok/s |
| Multi-model A/B testing | OpenRouter | Single API, all backends |
| Custom community models | Replicate | Largest model marketplace |
Conclusion
The 2026 LLM API speed landscape has bifurcated into two clear camps: custom-silicon providers (Cerebras, Groq) achieving 1,000-2,000+ tokens/sec for real-time applications, and GPU cloud providers (Fireworks, Together, OpenAI, DeepSeek) at 30-400 tokens/sec optimized for cost and model variety. The decision tree:
- Need sub-second response with quality? Use Cerebras for 70B models, Groq for everything else
- Need best model quality at any speed? OpenAI GPT-4o at 80-110 tok/s is the production default
- Need to process millions of tokens cheaply? DeepSeek at $0.14/M output tokens
- Need to A/B test providers? OpenRouter routes to all of them through one API
Speed matters more in 2026 than at any point in LLM history. Pick a provider based on your latency budget, not just model quality — a 2,000 tok/s response on a 70B model is the new bar for production voice agents and code tools.