TokenMix Research Lab · 2026-04-10

AI API Latency Benchmark 2026: TTFT and TPS Across Groq, Fireworks, SambaNova, OpenAI, Anthropic, Google, and DeepSeek

Last Updated: 2026-04-29
Author: TokenMix Research Lab

Groq leads at 120ms median TTFT, 330 TPS — 3-5x faster than OpenAI/Anthropic. SambaNova (150ms) + Fireworks (180ms) close behind. DeepSeek 300ms median but 2,500ms P95 — fastest tail-latency disaster. Anthropic most consistent (P95/P50 of 1.8x).

The fastest AI API in 2026 is Groq, delivering sub-200ms time-to-first-token (TTFT) and 300+ tokens per second (TPS) on Llama models. But raw speed is only part of the story. This LLM latency benchmark compares seven major providers across two critical metrics — TTFT and TPS — at different input lengths, times of day, and model sizes. Fireworks AI and SambaNova are close behind Groq on speed. OpenAI and Anthropic prioritize reliability over raw speed. Google's Gemini sits in the middle. DeepSeek is fast but inconsistent. All latency data collected by TokenMix.ai monitoring infrastructure, April 2026.

Table of Contents

• Quick Comparison: AI API Latency Rankings
• Understanding Latency Metrics: TTFT vs TPS
• Why AI API Latency Matters for UX
• Groq: Fastest AI API Overall
• Fireworks AI: Best Speed-Price Balance
• SambaNova: Enterprise-Grade Speed
• OpenAI: Reliable but Not the Fastest
• Anthropic: Consistent Latency Profile
• Google Gemini: Variable Performance
• DeepSeek: Fast When It Works
• Full Latency Benchmark Table
• Latency vs. Cost: The Real Tradeoff
• When Latency Matters and When It Does Not
• Which AI API Should You Pick for Speed?
• What's the Bottom Line on AI API Latency?
• FAQ

Quick Comparison: AI API Latency Rankings

Seven providers tested across 10K+ requests. Speed tiers: speed-optimized hardware (Groq/SambaNova/Fireworks 120-180ms), frontier proprietary (OpenAI/Anthropic 450-500ms), variable (Gemini/DeepSeek wide P95 spread).

Rankings are based on median TTFT measured over 10,000+ requests per provider during April 2026. All tests use comparable prompt lengths (2K tokens input, 500 tokens output) unless otherwise specified.

Understanding Latency Metrics: TTFT vs TPS

TTFT = first-token wait (user "feels" this). TPS = streaming speed. P95/P99 matters more than median for production — single slow request kills UX. Groq P95/P50 ratio 2.3x vs DeepSeek 8.3x measures consistency.

Two metrics define AI API latency. Understanding both is critical for choosing the right provider.

Time to First Token (TTFT)

TTFT measures how long it takes from sending your request to receiving the first token of the response. This is the metric users "feel" most directly. A 100ms TTFT feels instant. A 500ms TTFT introduces a noticeable pause. A 2,000ms TTFT feels broken.

TTFT depends on: model size, input length, server load, geographic distance, and inference infrastructure. Larger models and longer inputs increase TTFT. Dedicated hardware (like Groq's LPU) dramatically reduces it.

Tokens Per Second (TPS)

TPS measures how fast the model generates output tokens after the first token arrives. High TPS means the response streams in quickly. A 300 TPS stream is faster than a human can read. A 50 TPS stream produces noticeable word-by-word generation.

TPS depends on: model architecture, batch size on the server, hardware, and output length. MoE models (DeepSeek V4) tend to have higher peak TPS but more variable performance.

P50 vs. P95 vs. P99

Median (P50) latency tells you what typical performance looks like. P95 tells you what happens on the worst 5% of requests. P99 tells you what happens on the worst 1%. For production applications, P95 and P99 matter more than median — one slow request in a user-facing chat can ruin the experience.

The gap between P50 and P95 reveals consistency. Groq's gap (120ms to 280ms) is tight. DeepSeek's gap (300ms to 2,500ms) is enormous. A provider with 300ms median TTFT but 2,500ms P95 is less predictable than one with 450ms median and 900ms P95.

Why AI API Latency Matters for UX

Stanford HCI 2025 thresholds: <300ms = "instant", 800ms+ = "slow", 2000ms+ triggers 15-25% drop-off. Agent latency compounds — 10 steps × 500ms = 5s wait. Voice/real-time needs sub-200ms (only Groq + SambaNova qualify).

Conversational AI and Chatbots

For chat interfaces, TTFT directly impacts perceived responsiveness. Research on user perception of AI chat latency (Stanford HCI Lab, 2025) shows:

• Under 300ms TTFT: Users perceive the response as "instant"
• 300-800ms TTFT: Users notice a brief pause but consider it acceptable
• 800-2,000ms TTFT: Users perceive the system as "slow"
• Over 2,000ms TTFT: Users begin to abandon the conversation (15-25% drop-off rate)

For consumer-facing chatbots, keeping P95 TTFT under 800ms should be a hard requirement. This eliminates DeepSeek and Google Gemini from consideration for latency-sensitive chat applications.

Coding Assistants

Coding assistants (Copilot, Cursor, Claude Code) have different latency requirements. Developers tolerate 1-2 second TTFT for complex code completions because the alternative (writing it manually) takes longer. But autocomplete-style suggestions need sub-500ms TTFT to feel natural in the typing flow.

Agent Pipelines

Agent latency compounds across steps. A 10-step agent chain with 500ms TTFT per step adds 5 seconds of wait time just from TTFT alone. Using Groq (120ms TTFT) reduces this to 1.2 seconds. For agents executing 50+ steps, the cumulative latency difference between providers becomes significant.

Real-Time Applications

Voice assistants, real-time translation, and gaming AI require sub-200ms TTFT. Only Groq and SambaNova consistently deliver this level of speed.

Groq: Fastest AI API Overall

120ms median, 330 TPS — 3-5x faster than GPU-based providers. P95/P50 ratio 2.3x is best in benchmark. LPU hardware = no memory bandwidth bottleneck. Trade-off: limited to Llama, Mixtral, and select open-source models.

Groq is the undisputed speed champion. Its custom Language Processing Units (LPUs) deliver latency numbers that are 3-5x faster than GPU-based inference providers.

Latency Performance

Groq's consistency is as impressive as its speed. The P95/P50 ratio of 2.3x is the best in this benchmark. Even on bad requests, Groq is faster than most providers' median performance.

Limitations

Groq currently supports a limited model selection: primarily Llama models, Mixtral, and a few others. You cannot run GPT-5.4, Claude, or Gemini on Groq. This means choosing Groq means choosing open-source models, which may not match the quality of frontier proprietary models for all tasks.

Groq's pricing ($0.27/$0.27 per million tokens for Llama 3.3 70B) is competitive but not the cheapest. You are paying a premium for speed compared to other Llama hosting providers.

Best for: Real-time applications, voice AI, latency-sensitive chatbots, and any use case where sub-200ms TTFT is a hard requirement.

Fireworks AI: Best Speed-Price Balance

180ms median (50% slower than Groq, but 50% faster than mainstream providers). Cheapest at $0.20/$0.20 per M Llama 70B. Broader model selection including fine-tunes. Best when 200ms acceptable + you need flexibility.

Fireworks AI delivers near-Groq speed at competitive pricing, with a broader model selection and strong developer experience.

Latency Performance

Fireworks is 50% slower than Groq on median TTFT but 50% faster than mainstream providers (OpenAI, Anthropic). It supports a wider range of models including fine-tuned variants and custom deployments.

Best for: Teams that need fast inference with more model flexibility than Groq offers. Production applications where 200ms TTFT is acceptable.

SambaNova: Enterprise-Grade Speed

150ms median, 300 TPS — closest competitor to Groq. Custom RDU hardware. Differentiator: enterprise SLAs, dedicated instances, on-premise deployment. Best when speed + dedicated infra are both required.

SambaNova's custom RDU (Reconfigurable Dataflow Unit) hardware delivers speed competitive with Groq, targeted at enterprise deployments.

Latency Performance

SambaNova is the closest competitor to Groq on raw speed and actually exceeds Groq on TPS in some configurations. Enterprise features (dedicated instances, SLAs, on-premise deployment) differentiate it from Groq's cloud-only offering.

Best for: Enterprise deployments requiring both speed and dedicated infrastructure. On-premise requirements where Groq is not an option.

OpenAI: Reliable but Not the Fastest

450ms median, 85 TPS, P95/P50 ratio 2.7x. Trades speed for GPT-5.4 model quality. P99 of 2,100ms means 1/100 requests cross the 2-second threshold. Acceptable for business chat, not real-time apps.

OpenAI's API prioritizes reliability and model quality over raw speed. GPT-5.4 is not fast by inference-provider standards, but it is consistent.

Latency Performance

OpenAI's P95/P50 ratio of 2.7x is acceptable but not great. The P99 tail at 2,100ms means roughly 1 in 100 requests will take over 2 seconds to start responding. For most chat applications, this is tolerable. For real-time applications, it is not.

The tradeoff: GPT-5.4 is a better model than Llama 3.3 70B. You are trading speed for model quality. For tasks where output quality matters more than response time, this is the right choice.

Best for: Applications where model quality is the priority and 500ms TTFT is acceptable. Most business applications, content generation, analysis tasks.

Anthropic: Consistent Latency Profile

500ms median + 1.8x P95/P50 ratio (best among frontier providers). Lower P99 (1,400ms) than OpenAI. Predictable performance matters more than raw speed for SLA-bound applications.

Anthropic's Claude API has the most consistent latency profile among frontier model providers. The gap between P50 and P95 is the smallest.

Latency Performance

The P95/P50 ratio of 1.8x is the best among frontier model providers (compare to OpenAI's 2.7x and Google's 3.0x). This means fewer surprise-slow requests. For production applications where predictability matters, Anthropic's consistency is a genuine advantage.

Best for: Production systems where latency predictability matters more than raw speed. Applications with strict P95 latency budgets.

Google Gemini: Variable Performance

Worst P95/P50 ratio at 3.0x. P99 of 3,500ms = 1/100 requests over 3.5s. Time-of-day dependent: US business hours add 33% TTFT. Best for batch/offline where variability is tolerable.

Google's Gemini API shows the widest latency variance of any major provider. Great when it is fast, frustrating when it is slow.

Latency Performance

The P95/P50 ratio of 3.0x is the worst in this benchmark. The P99 at 3,500ms means 1 in 100 requests will take over 3.5 seconds to start. This variability makes Gemini difficult to use in latency-sensitive production systems.

TokenMix.ai monitoring shows that Gemini's latency is highly time-dependent. During US business hours (9 AM - 5 PM PT), median TTFT increases to approximately 800ms. During off-peak hours, it drops to approximately 400ms.

Best for: Batch processing, offline analysis, and applications where latency variability is acceptable. Gemini's model quality and large context window (1M-10M) justify the latency for certain workloads.

DeepSeek: Fast When It Works

P95/P50 ratio 8.3x is the worst tested. Median 300ms but P95 2,500ms, P99 5,000ms+. Latency spikes 3-4x during Chinese business hours. Use for batch + retry-tolerant workloads only.

DeepSeek V4's median latency is surprisingly good at 300ms TTFT. The problem is consistency. P95 latency at 2,500ms represents the worst tail latency of any provider tested.

Latency Performance

The P95/P50 ratio of 8.3x is by far the worst. When DeepSeek is fast, it is very fast. When it is slow, it is painfully slow. The P99 at 5,000ms+ means 1 in 100 requests may take over 5 seconds just to start generating.

Latency spikes correlate with Chinese business hours (9 AM - 6 PM CST). TokenMix.ai data shows a 3-4x TTFT increase during peak usage periods. For teams in Western time zones, off-peak access is significantly faster.

Best for: Cost-sensitive applications where occasional latency spikes are acceptable. Batch processing where per-request latency does not matter. Use with automatic retry logic.

Full Latency Benchmark Table

Nine columns side-by-side. Speed leaders: Groq, SambaNova, Fireworks (all ratio 2.3x). Quality leaders cost 10-20x more. Worst tail latency: DeepSeek (8.3x), Gemini (3.0x). Most predictable: Anthropic (1.8x).

Data collected by TokenMix.ai, April 2026. 10,000+ requests per provider, 2K token input, 500 token output, sampled across all hours.

Latency vs. Cost: The Real Tradeoff

Sub-200ms TTFT costs $0.60-0.75 per 1K requests on Llama. Frontier costs $8-13.50 per 1K requests at 3-5x slower. Llama 70B on Groq delivers 90% of frontier quality at 1/20th cost + 3x speed for many tasks.

Speed costs money — but not always proportionally. Here is the cost per request at two speed tiers:

Fast Tier (Sub-200ms TTFT): Open-Source Models on Speed Providers

Quality Tier (Sub-600ms TTFT): Frontier Proprietary Models

The insight: you can have sub-200ms TTFT for $0.60-0.75 per 1,000 requests using open-source models on speed-optimized providers. Frontier models cost 10-20x more and deliver 3-5x higher latency. The question is whether the quality difference justifies the speed and cost penalty.

For many applications (simple Q&A, classification, extraction), Llama 3.3 70B on Groq provides 90% of frontier model quality at 1/20th the cost and 3x the speed. TokenMix.ai helps teams identify which requests need frontier models and which can route to fast, cheap alternatives.

When Latency Matters and When It Does Not

Voice/real-time: <200ms (Groq/SambaNova). Consumer chat: <500ms. Business chat: <800ms. Coding autocomplete: <300ms. Coding generation: <1.5s. Background agent steps + batch: no requirement.

Which AI API Should You Pick for Speed?

Lowest absolute latency: Groq. Speed + flexibility: Fireworks. Enterprise speed + SLA: SambaNova. Frontier quality acceptable speed: OpenAI/Anthropic. Predictability: Anthropic. Multi-tier strategy: route via TokenMix.ai.

Related: See how all models rank on our LLM leaderboard and benchmark guide

What's the Bottom Line on AI API Latency?

5x range from Groq's 120ms to Gemini's 600ms median. Best strategy: latency-aware routing — fast tasks to Groq, complex to frontier. Provider performance changes daily; continuous monitoring beats one-time benchmarks.

AI API latency in 2026 spans a 5x range from Groq's 120ms to Google Gemini's 600ms median TTFT. The right choice depends on whether you need raw speed (Groq, SambaNova), frontier model quality (OpenAI, Anthropic), or cost efficiency (DeepSeek, Fireworks).

The most effective strategy for production applications is latency-aware routing: send time-sensitive requests to fast providers and complex analysis to quality providers. TokenMix.ai's unified API supports this routing pattern with real-time latency monitoring across all providers, automatic failover, and a single integration point.

Latency is not static. Provider performance changes daily based on load, infrastructure updates, and capacity. Continuous monitoring — not one-time benchmarks — is how production teams maintain their latency targets.

FAQ

What is the fastest AI API in 2026?

Groq is the fastest AI API with 120ms median TTFT and 330 tokens per second on Llama 3.3 70B. SambaNova (150ms) and Fireworks AI (180ms) are close behind. These speed-optimized providers are 3-5x faster than OpenAI, Anthropic, and Google.

What is a good TTFT for a chatbot?

For consumer-facing chatbots, target under 500ms P95 TTFT. Research shows users perceive responses as "instant" below 300ms and "slow" above 800ms. Conversation abandonment rates increase 15-25% when TTFT exceeds 2,000ms.

Why is Groq so much faster than OpenAI?

Groq uses custom Language Processing Units (LPUs) designed specifically for inference, while OpenAI runs on NVIDIA GPUs. LPUs eliminate memory bandwidth bottlenecks in transformer inference, enabling 3-5x faster token generation. The tradeoff is that Groq only supports a limited set of open-source models, while OpenAI runs proprietary frontier models.

Does AI API latency change throughout the day?

Yes. TokenMix.ai monitoring shows significant time-of-day variation. DeepSeek V4 latency increases 3-4x during Chinese business hours. Google Gemini increases 2x during US business hours. Groq and Fireworks are the most consistent across time zones.

How does input length affect TTFT?

Longer inputs increase TTFT because the model must process more tokens before generating a response. Typical scaling: 2x input length adds 30-80% to TTFT depending on the provider. At 32K input tokens, Groq TTFT increases from 120ms to 380ms, while OpenAI increases from 450ms to 1,100ms.

Is DeepSeek V4 fast enough for production chatbots?

At 300ms median TTFT, DeepSeek V4 is fast enough for most chatbot use cases. However, its P95 TTFT of 2,500ms means 5% of users will experience over 2.5 seconds of delay. For latency-sensitive applications, use DeepSeek V4 with automatic failover to a faster provider when latency exceeds your threshold.

Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: Groq, Artificial Analysis, Fireworks AI, TokenMix.ai