AI Model Speed Test: Which Responds Fastest?

Speed Matters More Than You Think

When people compare AI models, they focus on quality. Which model writes better? Which one reasons more accurately? These matter, but speed matters too. If a model takes 8 seconds to respond versus 2 seconds, that delay adds up over dozens of queries per day. Slow models also break your flow of thought.

We timed 12 popular AI models across four task types and measured both time-to-first-token (how quickly the response starts streaming) and total response time. Here are the results.

How We Tested

We used Admix to access all models through the same interface, eliminating differences in app loading time and network routing. Each model received the same 20 prompts across four categories:

• Short answer: Simple factual questions requiring 1-3 sentences
• Medium response: Explanations and summaries requiring 200-400 words
• Long response: Detailed analyses and articles requiring 600+ words
• Code generation: Working code snippets with explanations

All tests were run during US business hours across three days to account for load variation.

Results: Time-to-First-Token

Time-to-first-token (TTFT) is how long you wait before the response starts appearing. This is the most noticeable speed metric because it's the gap between pressing Enter and seeing anything happen.

Model	Average TTFT
GPT-4o	0.3 seconds
Gemini Pro	0.4 seconds
Mistral Medium	0.5 seconds
GPT-5	0.6 seconds
Llama 3.1 70B	0.7 seconds
Mistral Large	0.7 seconds
Claude Sonnet	0.8 seconds
Gemini Ultra	0.9 seconds
DeepSeek V3	1.1 seconds
Grok 3	1.2 seconds
Claude Opus	1.4 seconds
DeepSeek R1	1.8 seconds

GPT-4o is the clear winner in TTFT. The response feels nearly instant. At the other end, DeepSeek R1's nearly 2-second delay is noticeable, though it's doing more "thinking" before responding, which often produces better answers.

Results: Total Response Time (Medium Tasks)

For a typical 300-word response:

Model	Average Total Time
GPT-4o	2.1 seconds
Gemini Pro	2.4 seconds
Mistral Medium	2.8 seconds
GPT-5	3.2 seconds
Mistral Large	3.5 seconds
Claude Sonnet	3.8 seconds
Llama 3.1 70B	4.0 seconds
Gemini Ultra	4.3 seconds
DeepSeek V3	4.8 seconds
Grok 3	5.1 seconds
Claude Opus	5.8 seconds
DeepSeek R1	7.2 seconds

GPT-4o maintains its lead in total time. The difference between the fastest and slowest models is about 5 seconds, which might not sound like much. But multiply that by 50 queries per day and you've lost 4 minutes waiting, or gained 4 minutes by using a faster model.

Speed vs. Quality Tradeoff

There's a clear inverse relationship between speed and quality. The fastest models (GPT-4o, Gemini Pro) are generally less capable than the slowest ones (Claude Opus, DeepSeek R1). This makes sense: more capable models are larger and do more computation per query.

The practical implication: use fast models for simple tasks and save the powerful (slower) models for complex ones. A quick factual question doesn't need Claude Opus. A detailed analysis of a contract doesn't need GPT-4o's speed.

When Speed Matters Most

Conversational use: When you're having a back-and-forth dialogue with an AI, speed is important. Each pause breaks your train of thought. Fast models are better for conversational use.

Batch processing: If you're running 50 prompts through AI (email drafts, data analysis, content generation), the speed differences compound. The fastest model saves you 10-15 minutes over the slowest for a large batch.

Real-time coding: When using AI as a coding assistant, you want responses fast so they arrive before you've moved on mentally. Slow responses get ignored because you've already started solving the problem yourself.

When Speed Doesn't Matter

Important documents: If you're generating a report, proposal, or analysis that will be reviewed and edited, an extra 3 seconds of generation time is irrelevant. Use the best model, not the fastest.

Learning and studying: When you're trying to understand a concept, you actually benefit from a brief pause. It gives you time to think about the question you asked before the answer appears.

Our Recommendation

Don't pick one model based on speed alone. Instead, have access to both fast and powerful models, and switch based on the task. Quick question? GPT-4o. Detailed analysis? Claude Opus. The time you "lose" on slower responses for important tasks is gained back in quality.

Admix makes this switching easy because all models are in one interface. You don't need to open different apps for different models. Pick the right tool for the job, and speed takes care of itself.