AI API Response Time Comparison 2026: TTFT and Speed Benchmarks for Every Major Provider

TokenMix Research Lab ยท 2026-04-13

AI API Response Time Comparison 2026: TTFT and Speed Benchmarks for Every Major Provider

AI API Response Time Comparison: TTFT and Tokens-Per-Second Benchmarks for Every Major Provider (2026)

[Groq](https://tokenmix.ai/blog/groq-api-pricing) delivers a 0.15-second time-to-first-token. DeepSeek takes 2.0 seconds. That is a 13x difference in how fast your users see the first word appear. AI API response time is the most overlooked factor in model selection -- developers compare benchmarks and pricing but ignore the metric that users actually feel. This comparison covers TTFT (time-to-first-token) and TPS (tokens per second) benchmarks for every major AI API, explains when speed matters, and tells you which provider to pick for latency-critical applications. All data from [TokenMix.ai](https://tokenmix.ai) monitoring, April 2026.

Table of Contents

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Quick Comparison: AI API Response Time Rankings

| Rank | Provider + Model | TTFT | TPS | Total (100 tok) | Price Input $/M | | --- | --- | --- | --- | --- | --- | | 1 | Groq Llama 3.3 8B | 0.15s | 350 tok/s | 0.43s | $0.05 | | 2 | Groq Llama 3.3 70B | 0.20s | 250 tok/s | 0.60s | $0.17 | | 3 | Fireworks Llama 3.3 70B | 0.35s | 180 tok/s | 0.91s | $0.20 | | 4 | OpenAI GPT-4.1 mini | 0.30s | 120 tok/s | 1.13s | $0.40 | | 5 | Google Gemini 2.0 Flash | 0.40s | 150 tok/s | 1.07s | $0.10 | | 6 | OpenAI GPT-5.4 | 0.50s | 80 tok/s | 1.75s | $2.50 | | 7 | Anthropic Claude Haiku 3.5 | 0.50s | 90 tok/s | 1.61s | $0.80 | | 8 | Anthropic Claude Sonnet 4 | 0.80s | 70 tok/s | 2.23s | $3.00 | | 9 | Google Gemini 3.1 Pro | 0.70s | 60 tok/s | 2.37s | $1.25 | | 10 | DeepSeek V4 | 2.00s | 60 tok/s | 3.67s | $0.30 |

All benchmarks: median values from 10,000 requests, US East region, 500-token input prompt, 100-token output, measured by TokenMix.ai monitoring infrastructure, April 2026.

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Why AI API Response Time Matters

Users perceive AI chat interfaces differently based on response time thresholds.

| TTFT Range | User Perception | Use Case Impact | | --- | --- | --- | | < 0.3s | Instant -- feels like typing | Chat assistants, autocomplete | | 0.3-0.8s | Fast -- acceptable for conversation | Customer support, search | | 0.8-1.5s | Noticeable delay -- users wait | Content generation, analysis | | 1.5-3.0s | Slow -- users get impatient | Background tasks only | | > 3.0s | Unacceptable for interactive use | Batch processing |

Research on chat UIs shows that a 1-second increase in TTFT reduces user engagement by 15-20%. For products where AI is the core experience (chatbots, writing assistants, code copilots), response time directly impacts retention.

**The [streaming](https://tokenmix.ai/blog/ai-api-streaming-guide) paradox:** Even with streaming, TTFT determines perceived speed. A model that takes 2 seconds to start streaming but generates tokens at 100 tok/s will feel slower than a model that starts in 0.2 seconds at 60 tok/s. Users judge speed by when text first appears.

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Understanding TTFT vs TPS vs Total Latency

Three metrics define AI API response time. Each matters for different reasons.

**TTFT (Time to First Token):** The time between sending your request and receiving the first token. This is what users perceive as "speed." TTFT includes network latency, queue time, model loading (if cold start), and time for the model to generate the first token.

**TPS (Tokens Per Second):** The rate at which tokens are generated after the first token. Higher TPS means shorter total response times for long outputs. TPS varies by model size, hardware, and batching strategy.

**Total Latency:** TTFT + (output tokens / TPS). For a 100-token response on GPT-4.1 mini: 0.30s + (100 / 120) = 1.13s total.

**Which metric to optimize depends on your use case:**

| Use Case | Primary Metric | Why | | --- | --- | --- | | Chat assistant | TTFT | Users judge speed by first word | | Content generation | TPS | Long outputs need fast throughput | | API pipeline (no user) | Total latency | End-to-end time is all that matters | | Autocomplete | TTFT | Sub-200ms needed for inline suggestions | | Batch processing | TPS | Maximize throughput per dollar |

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TTFT Benchmarks: Time to First Token

Detailed TTFT benchmarks across input sizes. TTFT increases with input length because the model must process the full prompt before generating.

TTFT by Input Size (Median, US East)

| Provider + Model | 100 tokens | 500 tokens | 2K tokens | 10K tokens | 50K tokens | | --- | --- | --- | --- | --- | --- | | Groq Llama 3.3 8B | 0.10s | 0.15s | 0.22s | 0.45s | 1.20s | | Groq Llama 3.3 70B | 0.14s | 0.20s | 0.30s | 0.65s | 1.80s | | Fireworks Llama 70B | 0.25s | 0.35s | 0.50s | 0.95s | 2.50s | | OpenAI GPT-4.1 mini | 0.20s | 0.30s | 0.45s | 0.90s | 2.20s | | Google Gemini Flash | 0.30s | 0.40s | 0.55s | 1.00s | 2.00s | | OpenAI GPT-5.4 | 0.35s | 0.50s | 0.75s | 1.50s | 3.50s | | Anthropic Haiku 3.5 | 0.35s | 0.50s | 0.70s | 1.30s | 3.00s | | Anthropic Sonnet 4 | 0.55s | 0.80s | 1.10s | 2.00s | 4.50s | | Google Gemini 3.1 Pro | 0.50s | 0.70s | 1.00s | 1.80s | 3.80s | | DeepSeek V4 | 1.50s | 2.00s | 2.80s | 4.50s | 8.00s |

**Key observations:**

P95 TTFT (Tail Latency)

Median TTFT tells you the typical experience. P95 tells you the worst 5% of requests -- important for SLA-sensitive applications.

| Provider + Model | Median TTFT | P95 TTFT | P95/Median Ratio | | --- | --- | --- | --- | | Groq Llama 3.3 70B | 0.20s | 0.45s | 2.3x | | OpenAI GPT-4.1 mini | 0.30s | 0.80s | 2.7x | | Google Gemini Flash | 0.40s | 1.10s | 2.8x | | Anthropic Haiku 3.5 | 0.50s | 1.50s | 3.0x | | DeepSeek V4 | 2.00s | 6.00s | 3.0x |

Groq has the tightest tail latency (P95 is only 2.3x median). DeepSeek has the widest spread, with P95 TTFT hitting 6 seconds -- problematic for real-time applications.

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TPS Benchmarks: Tokens Per Second

TPS determines how fast long responses complete. For a 1,000-token response, the difference between 60 tok/s (16.7s) and 300 tok/s (3.3s) is massive.

| Provider + Model | TPS (Streaming) | TPS (Non-Streaming) | 1K Token Output | | --- | --- | --- | --- | | Groq Llama 3.3 8B | 350 tok/s | 400 tok/s | 2.9s | | Groq Llama 3.3 70B | 250 tok/s | 280 tok/s | 4.0s | | Fireworks Llama 70B | 180 tok/s | 200 tok/s | 5.6s | | Google Gemini Flash | 150 tok/s | 170 tok/s | 6.7s | | OpenAI GPT-4.1 mini | 120 tok/s | 140 tok/s | 8.3s | | Anthropic Haiku 3.5 | 90 tok/s | 110 tok/s | 11.1s | | OpenAI GPT-5.4 | 80 tok/s | 95 tok/s | 12.5s | | Anthropic Sonnet 4 | 70 tok/s | 85 tok/s | 14.3s | | Google Gemini 3.1 Pro | 60 tok/s | 75 tok/s | 16.7s | | DeepSeek V4 | 60 tok/s | 70 tok/s | 16.7s |

**Groq is 4-6x faster than traditional cloud providers on TPS.** This is the LPU advantage -- purpose-built silicon for sequential token generation. For applications that generate long outputs (content writing, code generation), Groq's speed advantage compounds.

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Total Response Time by Task Type

Total response time varies dramatically depending on the task's input and output length.

| Task | Input Tokens | Output Tokens | Groq 70B | GPT-4.1 mini | Gemini Flash | DeepSeek V4 | | --- | --- | --- | --- | --- | --- | --- | | **Chat reply** | 500 | 100 | 0.6s | 1.1s | 1.1s | 3.7s | | **Email draft** | 200 | 500 | 2.2s | 4.5s | 3.7s | 10.3s | | **Code function** | 300 | 400 | 1.8s | 3.6s | 3.1s | 8.7s | | **Document summary** | 5,000 | 300 | 1.8s | 3.4s | 2.6s | 9.5s | | **Blog post** | 500 | 2,000 | 8.2s | 17.0s | 13.7s | 35.3s | | **Code review** | 10,000 | 1,000 | 4.7s | 9.2s | 7.7s | 21.2s |

For short interactions (chat, Q&A), all providers except DeepSeek deliver acceptable response times. For long-form generation (blog posts, detailed code), Groq is 2-4x faster than cloud providers.

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Provider Deep Dive: What Causes Speed Differences

**Groq: 0.15-0.20s TTFT.** Groq uses custom Language Processing Units (LPUs) designed specifically for LLM inference. Their deterministic memory access patterns eliminate the bottlenecks that GPU-based inference faces. The trade-off: limited model selection (Llama variants only) and higher cost per token than some alternatives.

**OpenAI: 0.30-0.50s TTFT.** OpenAI runs on a massive GPU cluster with sophisticated batching. TTFT varies with load -- during peak hours (US business hours), expect 20-30% higher TTFT. GPT-4.1 mini is faster than [GPT-5.4](https://tokenmix.ai/blog/gpt-5-api-pricing) because the smaller model requires less compute per token.

**Google: 0.40-0.70s TTFT.** Google uses custom TPU hardware. Gemini Flash is optimized for speed, while [Gemini Pro](https://tokenmix.ai/blog/gemini-api-pricing) prioritizes quality. Google's infrastructure is global, so latency varies less by region than other providers.

**Anthropic: 0.50-0.80s TTFT.** Claude runs on AWS with A100/H100 GPUs. TTFT is consistent but not best-in-class. Anthropic has not prioritized inference speed -- their focus is on safety and output quality.

**DeepSeek: 2.0s TTFT.** DeepSeek's servers are in China, adding ~150ms of network latency for US/EU users. Their infrastructure appears less optimized for low-latency serving. During Chinese business hours, TTFT can spike to 3-5 seconds. Use US-hosted alternatives ([Together AI](https://tokenmix.ai/blog/together-ai-review) or TokenMix.ai) for lower latency.

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Speed vs Cost: The Trade-off Table

Faster is not always more expensive. Some fast providers are also cheap.

| Provider + Model | TTFT | Input $/M | Speed Rank | Cost Rank | Best Balance? | | --- | --- | --- | --- | --- | --- | | Groq Llama 8B | 0.15s | $0.05 | 1st | 1st | Yes (simple tasks) | | Groq Llama 70B | 0.20s | $0.17 | 2nd | 3rd | Yes (quality + speed) | | Gemini Flash | 0.40s | $0.10 | 5th | 2nd | Yes (cost-sensitive) | | GPT-4.1 mini | 0.30s | $0.40 | 4th | 5th | Yes (ecosystem) | | DeepSeek V4 | 2.00s | $0.30 | 10th | 4th | No (too slow for cost) | | Claude Haiku 3.5 | 0.50s | $0.80 | 7th | 7th | No (slower + pricier) |

**The surprise: Groq Llama 8B is both the fastest and cheapest.** The limitation is model capability -- an 8B parameter model cannot match GPT-4.1 mini on complex tasks. But for classification, routing, and simple generation, it is unbeatable.

**[DeepSeek V4](https://tokenmix.ai/blog/deepseek-api-pricing) looks cheap on paper but its 2-second TTFT makes it poor value for interactive use.** It is only viable for batch processing where latency does not matter. For a complete cost analysis, see our [tokens per dollar guide](https://tokenmix.ai/blog/how-many-tokens-per-dollar).

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When Speed Matters (And When It Does Not)

| Application | Speed Matters? | Recommended TTFT Target | Model Suggestion | | --- | --- | --- | --- | | Chat assistant | Yes, critically | < 0.5s | Groq 70B or GPT-4.1 mini | | Autocomplete/copilot | Yes, critically | < 0.3s | Groq 8B | | Customer support bot | Yes | < 0.8s | GPT-4.1 mini or Gemini Flash | | Content generation (UI) | Somewhat | < 1.5s | Any mid-tier model | | API pipeline (no user) | No | Any | Cheapest capable model | | Batch data processing | No | N/A | Batch API + cheapest model | | Document summarization | Somewhat | < 2.0s | Gemini Flash (long context) | | Background analysis | No | Any | DeepSeek V4 (cheapest) |

**Rule of thumb:** If a human is waiting for the response, TTFT under 1 second matters. If it is a backend pipeline, optimize for cost per token, not speed.

TokenMix.ai provides latency-aware routing that automatically selects the fastest available provider for time-sensitive requests and the cheapest provider for background tasks.

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How to Optimize AI API Response Time

**1. Choose the right region.** Deploy your server close to your AI provider's data center. OpenAI and Anthropic are primarily US East. Groq is US-based. DeepSeek is in China. A cross-continent request adds 100-300ms.

**2. Use streaming.** Streaming reduces perceived latency by showing tokens as they arrive. Even if total response time is the same, users perceive streaming responses as 50-80% faster than waiting for a complete response. See our [streaming tutorial](https://tokenmix.ai/blog/how-to-stream-ai-api-response) for implementation.

**3. Minimize input tokens.** TTFT scales with input length. A 500-token prompt has ~50% lower TTFT than a 5,000-token prompt. Compress system prompts and trim unnecessary context.

**4. Use [prompt caching](https://tokenmix.ai/blog/prompt-caching-guide).** Cached prompts skip re-processing of the prefix, reducing TTFT by 30-50% on subsequent requests. OpenAI and Anthropic both offer automatic caching.

**5. Implement fallback routing.** When your primary provider spikes on latency, route to a backup. Example: default to GPT-4.1 mini, fall back to Gemini Flash if GPT TTFT exceeds 1 second.

**6. Pre-warm connections.** Keep persistent HTTP/2 connections to AI providers. Connection setup adds 50-100ms on the first request. Most SDKs handle this automatically with connection pooling.

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Conclusion

AI API response time varies by 13x across providers -- from Groq's 0.15-second TTFT to DeepSeek's 2.0 seconds. For interactive applications, this difference is the gap between a product that feels instant and one that feels broken.

Groq is the speed leader but is limited to Llama models. OpenAI GPT-4.1 mini offers the best balance of speed (0.3s TTFT), quality, and ecosystem. Gemini Flash is the best budget option with competitive speed. DeepSeek is only suitable for non-real-time workloads.

For production applications, use [TokenMix.ai](https://tokenmix.ai) to monitor real-time latency across all providers and set up automatic failover routing when your primary provider's response time degrades.

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FAQ

What is the fastest AI API in 2026?

Groq is the fastest AI API with 0.15s TTFT on Llama 3.3 8B and 0.20s on [Llama 3.3 70B](https://tokenmix.ai/blog/llama-3-3-70b). Among major cloud providers, OpenAI GPT-4.1 mini leads at 0.30s TTFT. Groq achieves this speed using custom LPU hardware designed specifically for LLM inference, not general-purpose GPUs.

What is TTFT and why does it matter?

TTFT (Time to First Token) measures how long after sending a request until the first word appears in the response. It is the primary metric for perceived AI speed in chat interfaces. Research shows a 1-second TTFT increase reduces user engagement by 15-20%. For interactive applications, target TTFT under 0.5 seconds.

Why is DeepSeek API so slow?

DeepSeek's servers are located in China, adding 100-300ms of network latency for US/EU users. The inference infrastructure also appears less optimized for low-latency serving compared to Groq or OpenAI. During peak Chinese business hours, TTFT can spike to 3-5 seconds. Use US-hosted providers like Together AI or TokenMix.ai for faster DeepSeek model access.

Does streaming reduce total response time?

No, streaming does not reduce total response time. The model generates tokens at the same speed regardless of streaming. However, streaming reduces perceived latency by 50-80% because users see text appearing immediately instead of waiting for the complete response. For chat UIs, streaming is essential for good UX.

How do I test AI API response time myself?

Measure TTFT by recording the timestamp when you send the request and when you receive the first streaming chunk. Measure TPS by counting output tokens and dividing by generation time (total time minus TTFT). Run at least 100 requests at different times of day for reliable median and P95 values. TokenMix.ai publishes automated benchmarks updated daily.

Can I get Groq-level speed from OpenAI or Anthropic?

Not currently. Groq's speed advantage comes from dedicated inference hardware (LPUs), not software optimization. OpenAI and Anthropic use GPUs which have different memory access patterns. The speed gap may narrow as GPU inference software improves, but Groq will likely maintain a 2-3x TTFT advantage through 2026.

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*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [Groq Benchmarks](https://groq.com), [OpenAI Status](https://status.openai.com), [TokenMix.ai Monitoring](https://tokenmix.ai), [Fireworks AI](https://fireworks.ai)*