AI Model Latency Comparison — TTFT, Throughput, and Real-Time Performance Data

Your Users Perceive Your AI Feature as “Slow” at 3 Seconds but “Instant” at 800ms — The Performance Gap Between Models Is Larger Than You Think

Latency determines user experience for AI features more than quality does. A model that produces a perfect answer in 5 seconds loses to a model that produces a good-enough answer in 800ms — because users abandon before the perfect answer arrives. The latency landscape across AI models spans a 50x range: from 200ms TTFT on Groq-hosted Llama to 10+ seconds on Claude Opus with long context. Choosing the wrong model for a latency-sensitive application means building a technically superior feature that users perceive as broken. This guide provides the latency data across models, the optimization techniques that cut response times by 40-70%, and the framework for making the latency-quality-cost tradeoff.

Latency Fundamentals

Two metrics define AI latency:

Metric	What it measures	Why it matters	User perception
Time to First Token (TTFT)	Time from request sent to first token received	Determines perceived responsiveness in streaming UI	<500ms feels instant; >2s feels slow
Tokens per Second (TPS)	Rate of token generation after first token	Determines how fast the full response appears	>30 TPS feels like typing; <10 TPS feels laggy

Total response time = TTFT + (output tokens ÷ TPS)

For a 200-token response at 50 TPS: Total = TTFT + 4 seconds. If TTFT is 500ms, total is 4.5s. If TTFT is 3s, total is 7s — a 56% increase.

Model Latency Comparison

TTFT (Time to First Token)

Measured on standard 500-token input prompts. P50 and P95 across normal traffic conditions.

Model	Provider	TTFT p50	TTFT p95	Notes
Llama 3.1 8B	Groq	120ms	250ms	Custom LPU hardware — fastest in market
Llama 3.1 70B	Groq	200ms	400ms	LPU advantage scales to large models
Gemini 2.5 Flash	Google	250ms	600ms	Optimized for speed
GPT-4o-mini	OpenAI	300ms	800ms	Fast tier model
GPT-4.1-mini	OpenAI	280ms	750ms	Slightly faster than 4o-mini
Llama 3.1 70B	Together AI	350ms	900ms	Standard GPU inference
GPT-4o	OpenAI	400ms	1,200ms	Frontier performance at moderate speed
GPT-4.1	OpenAI	350ms	1,000ms	Improved over 4o
Claude Haiku 3.5	Anthropic	400ms	1,000ms	Anthropic’s speed tier
Gemini 2.5 Pro	Google	500ms	1,500ms	Frontier model, moderate speed
Claude Sonnet 4	Anthropic	600ms	2,000ms	Frontier quality, slower
Mistral Large 2	Mistral	500ms	1,300ms	Mid-range performance
Claude Opus 4	Anthropic	1,500ms	5,000ms	Highest quality, slowest

Throughput (Tokens per Second)

Output generation speed measured on standard generation tasks.

Model	Provider	TPS (output)	200-token response time	1,000-token response time
Llama 3.1 8B	Groq	800-1,200	0.3s	1.1s
Llama 3.1 70B	Groq	250-400	0.7s	3.5s
Gemini 2.5 Flash	Google	300-500	0.6s	2.5s
GPT-4o-mini	OpenAI	100-200	1.5s	7s
GPT-4.1-mini	OpenAI	120-220	1.3s	6s
GPT-4o	OpenAI	60-100	2.5s	12s
GPT-4.1	OpenAI	70-110	2.2s	11s
Claude Haiku 3.5	Anthropic	80-150	2s	9s
Gemini 2.5 Pro	Google	60-120	2.2s	11s
Claude Sonnet 4	Anthropic	50-90	3s	14s
Mistral Large 2	Mistral	60-100	2.5s	12s
Claude Opus 4	Anthropic	30-60	5s	22s

Key insight: Groq’s custom hardware delivers 5-10x throughput advantage over standard GPU inference. This gap is hardware-specific — Groq’s LPU architecture is purpose-built for sequential token generation. Standard GPU inference (Together, Fireworks, cloud providers) clusters around 60-200 TPS for most models.

Total Response Time (TTFT + Generation)

Complete end-to-end latency for a typical 200-token output:

Model	Provider	Short response (50 tokens)	Medium response (200 tokens)	Long response (1,000 tokens)
Llama 3.1 8B	Groq	0.2s	0.4s	1.2s
Llama 3.1 70B	Groq	0.4s	0.9s	3.7s
Gemini 2.5 Flash	Google	0.4s	0.9s	2.8s
GPT-4o-mini	OpenAI	0.6s	1.8s	7.3s
GPT-4o	OpenAI	0.9s	2.9s	12.4s
Claude Haiku 3.5	Anthropic	0.8s	2.4s	9.4s
Claude Sonnet 4	Anthropic	1.0s	3.6s	14.6s
Gemini 2.5 Pro	Google	0.9s	2.7s	11.5s
Claude Opus 4	Anthropic	2.3s	6.5s	23.5s

Context Length Impact on Latency

TTFT increases with input length — the model must process all input tokens before generating the first output token.

Input length	GPT-4o TTFT	Claude Sonnet 4 TTFT	Gemini 2.5 Pro TTFT	Impact
500 tokens	400ms	600ms	500ms	Baseline
2,000 tokens	500ms	750ms	550ms	+15-25%
8,000 tokens	700ms	1,000ms	700ms	+40-75%
32,000 tokens	1,200ms	2,000ms	1,000ms	+100-230%
100,000 tokens	3,000ms	5,000ms	2,000ms	+300-730%
200,000 tokens	N/A (limit)	8,000ms	3,500ms	Extreme

Prompt caching mitigates this: With Anthropic’s prompt caching, a 100K-token system prompt that’s cached has TTFT comparable to a 2K-token prompt — the cached tokens are not reprocessed. OpenAI’s automatic caching provides similar (but less dramatic) speedup.

TTFT with Prompt Caching

Input length	Claude Sonnet 4 (no cache)	Claude Sonnet 4 (cached)	Speedup
2,000 tokens (all cached)	750ms	450ms	1.7x
8,000 tokens (all cached)	1,000ms	500ms	2.0x
32,000 tokens (all cached)	2,000ms	600ms	3.3x
100,000 tokens (all cached)	5,000ms	700ms	7.1x

Latency Optimization Techniques

Technique	TTFT reduction	TPS improvement	Implementation effort	Cost impact
Prompt caching	30-85% (long prompts)	0%	Low (API headers)	50-90% input cost savings
Model routing	50-70% (simple tasks to fast model)	Proportional to model speed	Medium	40-70% cost savings
Streaming	0% (TTFT same) but perceived latency drops	0%	Low	0%
Prompt compression	10-30% (shorter input = faster processing)	0%	Low-medium	20-40% input savings
Output length limits	0%	Reduces total time proportionally	Low	Proportional output savings
Regional routing	10-30% (reduced network latency)	0%	Medium	0%
Speculative decoding	0%	20-40% TPS increase (self-hosted)	High	GPU overhead
Quantization	10-20% (self-hosted)	20-50% TPS increase	Medium	Reduced GPU cost
Batch inference	N/A (async)	N/A	Medium	50% cost reduction

Combined Optimization Example

Baseline	+ Prompt caching	+ Model routing	+ Output limits	+ Streaming	Total improvement
TTFT: 2,000ms	600ms (-70%)	400ms (simple→fast model)	400ms (no change)	400ms (no change)	TTFT: 80% reduction
Total: 14s	12s (faster TTFT)	4s (faster model)	2.5s (shorter output)	2.5s (perceived: 0.4s)	Perceived: 97% improvement

Real-Time Application Requirements

Application	Max acceptable TTFT	Max acceptable total	Recommended model tier
Chat interface	500ms-1s	3-5s (streaming OK)	Fast tier (GPT-4o-mini, Flash, Groq Llama)
Autocomplete/suggestions	200ms	500ms	Ultra-fast (Groq Llama 8B, Flash)
Real-time translation	500ms	2s	Fast tier with streaming
Voice assistant	300ms	2s	Ultra-fast with streaming
Code completion	300ms	1-2s	Ultra-fast (Groq, Fireworks optimized)
Content moderation	500ms	1s	Fast tier (no streaming needed)
Document processing	2-5s	30-120s	Any tier (not real-time)
Report generation	5-10s	60-300s	Quality tier (latency not critical)

The Latency-Quality-Cost Triangle

Every model sits at a different point in the triangle. You can optimize for two of three:

Optimization target	Model choice	What you sacrifice
Low latency + low cost	Groq Llama 8B, Gemini Flash	Quality (Tier 3 model)
Low latency + high quality	GPT-4o-mini, Groq Llama 70B	Cost (premium fast inference)
High quality + low cost	Claude Sonnet 4 (cached), GPT-4o (batch)	Latency (caching/batch adds constraints)
All three (closest)	Gemini 2.5 Flash	Best compromise but not best at any single dimension

The Cascade Pattern for Latency-Sensitive Applications

Step	Action	Latency budget	Quality
1	Fast model generates initial response	0-500ms	Good (80% quality)
2	User sees streaming response	500ms-2s	Perceived as responsive
3	Background: quality model validates/refines	2-5s (async)	Excellent
4	If refinement differs significantly, update displayed response	5-8s	Best of both

This pattern shows a fast response immediately and upgrades it silently — users perceive ultra-low latency while getting frontier quality on responses that need it.

How to Apply This

Use the token-counter tool to estimate your input and output token counts — input length directly affects TTFT, and output length determines total response time.

Profile your actual latency requirements before choosing a model. The difference between “chat needs 500ms TTFT” and “batch processing is fine at 5s” changes the model selection entirely.

Implement streaming for any user-facing generation. Streaming doesn’t reduce actual latency but reduces perceived latency by 70-90%. A 10-second response that starts appearing at 500ms feels fast. A 10-second response that appears all at once feels broken.

Use prompt caching aggressively with long system prompts. If your system prompt is >2,000 tokens, caching reduces TTFT by 2-7x — the single highest-impact latency optimization available.

Profile p95, not p50. Average latency hides the worst user experiences. A model with 500ms p50 and 5,000ms p95 means 5% of users wait 10x longer. p95 is what determines user perception of reliability.

Honest Limitations

Latency measurements reflect standard API conditions; enterprise tiers, dedicated capacity, and traffic patterns affect real-world performance. Groq’s LPU advantage is hardware-specific and may narrow as other providers optimize their infrastructure. TTFT and TPS vary with server load — numbers represent typical conditions, not guaranteed performance. Prompt caching TTFT improvements depend on cache hit rate; cold starts (first request, cache miss) show no improvement. The cascade pattern adds implementation complexity and may confuse users if the displayed response changes after initial rendering. Regional routing improvements depend on user-to-datacenter distance; global user bases see mixed results. Model providers frequently update inference infrastructure, causing latency characteristics to change without notice. Self-hosted latency depends heavily on GPU type, batch size, and quantization — the generic numbers here assume standard cloud GPU configurations.