TokenMix Research Lab · 2026-04-12
Groq vs OpenAI: Speed and Cost Comparison for AI APIs in 2026
Last Updated: 2026-04-29
Author: TokenMix Research Lab
Groq Llama 70B at 315 TPS = 4x faster than GPT-4o (80 TPS), GPT-5.4 Mini (80 TPS), Claude Sonnet (60 TPS). TTFT 100ms vs OpenAI 300-400ms. But Groq Llama 70B costs $0.59/$0.79 vs GPT-4o Mini $0.15/$0.60 — speed premium adds 75% input cost. Groq's value: speed, not pure price. Llama 8B at $0.05/$0.08 (750 TPS) is BOTH faster AND cheaper than anything OpenAI offers.
Groq vs OpenAI comes down to one question: is 4x faster inference worth giving up OpenAI's model selection? Groq serves Llama 3.1 70B at 315 tokens per second for $0.59 per million input tokens. GPT-5.4 Mini runs at approximately 80 tokens per second for $0.15/$0.60. Groq is 4x faster and 20% cheaper on input, but it only runs open-source models. OpenAI offers GPT-5.4, GPT-4o, o3, fine-tuning, Assistants API, and a complete ecosystem. If speed is your bottleneck, Groq wins decisively. If model quality and ecosystem matter, OpenAI is irreplaceable. For most teams, the answer is both. All data tracked by TokenMix.ai as of April 2026.
Table of Contents
- Quick Comparison: Groq vs OpenAI
- Why Speed Matters for AI Applications
- Groq Speed Benchmarks: How Fast Is 315 TPS?
- OpenAI Speed and Model Range
- Groq vs GPT Cost Comparison
- Model Quality: Open-Source vs Proprietary
- Full Comparison Table
- When Speed Beats Model Selection
- When Model Selection Beats Speed
- Cost Breakdown at Production Scale
- How Should You Choose Between Groq and OpenAI?
- What's the Bottom Line on Groq vs OpenAI?
- FAQ
Quick Comparison: Groq vs OpenAI
Speed: Groq 70B 315 TPS vs GPT-4o 80 TPS (-4x). TTFT: Groq 0.1s vs OpenAI 0.4s. Pricing: Groq Llama 70B $0.59/$0.79 vs GPT-5.4 Mini $0.15/$0.60 (Mini cheaper input). Quality MMLU: Llama 70B 82% vs GPT-5.4 Mini 87%. Models: Groq runs Llama/Mixtral/Gemma only — no GPT-class quality option. Best-for: real-time apps vs ecosystem features.
| Dimension | Groq (Llama 3.1 70B) | OpenAI (GPT-5.4 Mini) | OpenAI (GPT-4o) |
|---|---|---|---|
| Output Speed | 315 TPS | ~80 TPS | ~80 TPS |
| Time to First Token | ~0.1s | ~0.3s | ~0.4s |
| Input Price | $0.59/M | $0.15/M | $2.50/M |
| Output Price | $0.79/M | $0.60/M | $10.00/M |
| Model Quality (MMLU) | 82% | 87% | 88.7% |
| Models Available | Llama, Mixtral, Gemma | GPT family, o-series | GPT family, o-series |
| Fine-tuning | No | Yes | Yes |
| Function Calling | Basic | Advanced | Advanced |
| Best For | Speed-critical apps | Balanced cost/quality | Maximum quality |
Why Speed Matters for AI Applications
Three real impacts: (1) UX — latency above 2 sec increases user abandonment 20-30%. 200-token response: 2.5s at 80 TPS vs 0.63s at 315 TPS — difference between "instant" and "noticeable lag". (2) Throughput — Groq processes 4x more requests/min on same client hardware. (3) Token cost — hardware efficiency enables lower per-token pricing despite speed premium.
Inference speed is not a vanity metric. It directly affects user experience, system throughput, and cost.
User experience impact: Research shows that response latency above 2 seconds increases user abandonment by 20-30%. A chatbot generating 200 tokens of response at 80 TPS takes 2.5 seconds. The same response at 315 TPS takes 0.63 seconds. The difference between "instant" and "noticeable lag."
System throughput: For applications processing queues -- document analysis, content moderation, batch classification -- speed directly translates to throughput. A Groq-powered pipeline processes 4x more requests per minute than an OpenAI-powered equivalent, assuming the same hardware on the client side.
Token generation cost: Faster inference means less server time per request. While the pricing models are per-token (not per-second), Groq's hardware efficiency enables lower per-token pricing despite the speed premium.
TokenMix.ai monitors inference speed across all major providers. Groq consistently leads on throughput for the models it offers, by a significant margin.
Groq Speed Benchmarks: How Fast Is 315 TPS?
Groq throughput by model: Llama 8B 750 TPS, Llama 70B 315 TPS, Mixtral 8x7B 480 TPS, Gemma 2 9B 580 TPS. TTFT range: 0.05-0.12s. Comparison: Groq 70B 315 TPS vs OpenAI GPT-4o 80 TPS (-3.9x), Claude Sonnet 60 TPS (-5.3x), Gemini Flash 150 TPS (-2.1x). 100-word response: 0.42s on Groq vs 1.5s on OpenAI — feels instant.
Groq's speed advantage comes from custom LPU (Language Processing Unit) hardware designed specifically for inference. Unlike GPUs that handle both training and inference, Groq's chips are inference-only, optimized for sequential token generation.
Groq speed by model (TokenMix.ai measurements, April 2026):
| Model | Output TPS | Time to First Token | Input Processing Speed |
|---|---|---|---|
| Llama 3.1 8B | 750 TPS | 0.05s | ~2,500 tokens/s |
| Llama 3.1 70B | 315 TPS | 0.10s | ~1,200 tokens/s |
| Llama 3.3 70B | 300 TPS | 0.12s | ~1,100 tokens/s |
| Mixtral 8x7B | 480 TPS | 0.08s | ~1,800 tokens/s |
| Gemma 2 9B | 580 TPS | 0.06s | ~2,200 tokens/s |
What 315 TPS feels like in practice:
- 100-word response: 0.42 seconds (feels instant)
- 500-word response: 2.1 seconds (fast)
- 1,000-word response: 4.2 seconds (acceptable for long generation)
Compared to GPU-based inference:
- OpenAI GPT-4o: ~80 TPS (3.9x slower)
- OpenAI GPT-5.4 Mini: ~80 TPS (3.9x slower)
- Anthropic Claude Sonnet: ~60 TPS (5.3x slower)
- Google Gemini Flash: ~150 TPS (2.1x slower)
Groq's speed lead is not marginal. It is a different league. The question is whether the speed advantage offsets the model limitation.
OpenAI Speed and Model Range
OpenAI fastest model: GPT-4.1 Mini at 120 TPS (still 2.6x slower than Groq 70B). Slowest: o3 at 30 TPS (reasoning model). Quality: GPT-5.4 90.1% MMLU, o3 92%+ — no open-source equivalent. Ecosystem moat: Fine-tuning, Assistants API, Code Interpreter, file search RAG, DALL-E/Whisper/TTS, Realtime voice. Switching to Groq = rebuilding infrastructure that took months.
OpenAI trades speed for breadth. No single model matches Groq's throughput, but OpenAI offers a spectrum of models for every use case.
OpenAI model speed (TokenMix.ai measurements):
| Model | Output TPS | MMLU | Price (Input/Output per M) |
|---|---|---|---|
| GPT-5.4 | ~60 TPS | 90.1% | $2.50/$15.00 |
| GPT-4o | ~80 TPS | 88.7% | $2.50/$10.00 |
| GPT-4o Mini | ~100 TPS | 82.0% | $0.15/$0.60 |
| GPT-4.1 Mini | ~120 TPS | 87.0% | $0.40/$1.60 |
| o3 | ~30 TPS | 92%+ | $10.00/$40.00 |
| o4-mini | ~80 TPS | 89%+ | $1.10/$4.40 |
The ecosystem advantage: Beyond raw models, OpenAI provides:
- Fine-tuning API: Customize models on your data
- Assistants API: Stateful conversation threads
- Code Interpreter: Execute code in sandbox
- File search: Vector-based RAG built in
- DALL-E, Whisper, TTS: Multimodal generation
- Realtime API: Voice conversations
None of these are available through Groq. For teams building complex AI applications that rely on OpenAI's ecosystem, switching to Groq means rebuilding significant infrastructure.
Groq vs GPT Cost Comparison
Same-quality-tier matchup: Groq Llama 70B $0.59/$0.79 vs GPT-4o Mini $0.15/$0.60 — Mini is 75% cheaper input, slightly cheaper output. At 100K req/day: Groq $7,140/mo vs GPT-4o Mini $2,700/mo. Groq Llama 8B at $0.05/$0.08 IS cheaper than any OpenAI model — but quality drops to 73% MMLU. You pay speed premium at 70B class, but win on price at 8B class.
Price comparison requires matching comparable models. Groq's Llama 3.1 70B competes most directly with GPT-4o Mini (similar quality tier).
Per-million token pricing:
| Model | Input | Output | Quality (MMLU) |
|---|---|---|---|
| Groq Llama 3.1 70B | $0.59 | $0.79 | 82% |
| Groq Llama 3.1 8B | $0.05 | $0.08 | 73% |
| Groq Mixtral 8x7B | $0.24 | $0.24 | 71% |
| OpenAI GPT-4o Mini | $0.15 | $0.60 | 82% |
| OpenAI GPT-4o | $2.50 | $10.00 | 88.7% |
Surprise: GPT-4o Mini is cheaper on input. At $0.15 versus Groq's $0.59, GPT-4o Mini costs 75% less per input token. On output, Groq is slightly cheaper ($0.79 vs $0.60) -- wait, GPT-4o Mini is actually cheaper on output too.
So why consider Groq? Three reasons:
Speed premium. You are not just buying tokens -- you are buying speed. 315 TPS versus 100 TPS means 3x faster responses. For latency-sensitive applications, that is worth the cost difference.
Groq's smaller models are genuinely cheap. Llama 8B at $0.05/$0.08 is cheaper than any OpenAI model for simple tasks. Mixtral at $0.24/$0.24 offers balanced pricing.
No vendor lock-in. Groq runs open-source models. You can switch providers or self-host the same models without code changes.
Monthly cost at 100,000 requests/day (2K input, 500 output tokens):
| Provider/Model | Monthly Cost | Speed |
|---|---|---|
| Groq Llama 70B | $7,140 | 315 TPS |
| OpenAI GPT-4o Mini | $2,700 | ~100 TPS |
| OpenAI GPT-4o | $52,500 | ~80 TPS |
| Groq Llama 8B | $630 | 750 TPS |
GPT-4o Mini is 62% cheaper than Groq Llama 70B. But if your application needs responses in under 1 second, Groq is the only option at this quality level. Speed has a price.
Model Quality: Open-Source vs Proprietary
Llama 70B vs GPT-4o Mini (same tier): MMLU tied at 82%. HumanEval Llama 80% vs GPT 87% (-7). GSM8K math 93% vs 96%. MT-Bench 8.2 vs 8.5. Against premium GPT-4o: gap widens 6-11 points across all benchmarks. For classification/extraction/simple gen: quality difference negligible. For coding/complex reasoning/strict structured output: GPT clearly wins.
This is where the Groq vs OpenAI comparison gets nuanced.
Groq is a hardware company, not a model company. It serves models built by others (Meta, Mistral, Google). Model quality depends on the open-source ecosystem, not Groq's engineering.
Current quality gap (open-source vs OpenAI):
| Benchmark | Llama 3.1 70B | GPT-4o Mini | GPT-4o |
|---|---|---|---|
| MMLU | 82% | 82% | 88.7% |
| HumanEval | 80% | 87% | 91% |
| GSM8K | 93% | 96% | 97% |
| MT-Bench | 8.2 | 8.5 | 9.1 |
Against GPT-4o Mini (same quality tier), Llama 70B is competitive on general knowledge (tied at 82% MMLU) but lags on coding (80% vs 87% HumanEval) and math (93% vs 96% GSM8K).
Against GPT-4o (premium tier), the gap widens to 6-11 points. You cannot match GPT-4o quality on Groq because GPT-4o is not available there.
TokenMix.ai observation: For classification, extraction, and simple generation tasks, the quality difference between Llama 70B and GPT-4o Mini is negligible in practice. For coding, complex reasoning, and structured output, GPT models have a measurable edge.
Full Comparison Table
Groq advantages: speed (4x faster), open-model portability (no lock-in), self-host fallback, 8B-class price floor. OpenAI advantages: model quality ceiling, fine-tuning, Assistants API, batch API (50% off), vision/audio, code execution, prompt caching (50% off), 99.7% uptime vs Groq ~99%. Tied: function calling (Groq basic, OpenAI advanced), input pricing competitive at Mini tier.
| Feature | Groq | OpenAI |
|---|---|---|
| Fastest output speed | 750 TPS (8B) / 315 TPS (70B) | ~120 TPS (Mini) |
| Model selection | ~10 open-source models | 10+ proprietary + ecosystem |
| Input pricing (competitive tier) | $0.59/M (70B) | $0.15/M (Mini) |
| Output pricing (competitive tier) | $0.79/M (70B) | $0.60/M (Mini) |
| Fine-tuning | No | Yes |
| Function calling | Basic | Advanced |
| Assistants/stateful | No | Yes |
| Batch API | No | Yes (50% off) |
| Vision | Select models | Yes |
| Audio | No | Yes |
| Code execution | No | Yes |
| Prompt caching | Limited | 50% discount |
| Rate limits | 6,000 RPM (paid) | Tier-based, up to 10,000 RPM |
| Uptime | ~99% | ~99.7% |
| Vendor lock-in | None (open models) | High (proprietary) |
| Self-host fallback | Yes (same models elsewhere) | No |
When Speed Beats Model Selection
Five Groq-decisive scenarios: (1) Real-time chat (315 TPS = instant feel). (2) Live content generation/autocomplete (must keep up with typing). (3) High-throughput queues (4x more items/min). (4) Voice AI (sub-200ms TTFT essential for natural conversation). (5) Multi-turn agentic apps — 10 calls × 2s = 20s on OpenAI vs 10 × 0.5s = 5s on Groq. Latency compounds.
Choose Groq when:
Real-time conversational AI. Chat applications where response latency directly affects user satisfaction. 315 TPS means responses feel instant. Users notice the difference.
Live content generation. Autocomplete, real-time writing assistance, interactive coding suggestions -- any application where the AI needs to keep up with human typing speed.
High-throughput processing. Queue-based systems processing thousands of items where throughput (items/minute) matters more than per-item quality. Document classification, content tagging, sentiment analysis at scale.
Voice AI and speech pipelines. When AI response time is in the critical path of a voice conversation, sub-200ms TTFT is essential. Groq's 100ms TTFT enables natural conversational flow. OpenAI's 300-400ms creates perceptible pauses.
Multi-turn rapid iteration. Agentic applications where the model is called 5-10 times per user action. Total latency accumulates: 10 calls at 2 seconds each = 20 seconds on OpenAI versus 10 calls at 0.5 seconds each = 5 seconds on Groq.
When Model Selection Beats Speed
Four OpenAI-decisive scenarios: (1) Quality ceiling matters — GPT-5.4/o3 simply better than open-source on complex tasks. (2) Need ecosystem (Assistants/fine-tuning/code interpreter/file search/voice — months to rebuild). (3) Strict structured output (GPT JSON reliability superior). (4) 80 TPS is fast enough — most web apps generate 200-token responses in 2.5s with streaming, first token in 300ms.
Choose OpenAI when:
Quality ceiling matters. GPT-5.4 and o3 are simply better than any open-source model on complex tasks. If your application's value proposition depends on maximum quality (premium SaaS, enterprise tools), OpenAI's top tier is unmatched.
You need the ecosystem. Assistants API, fine-tuning, code interpreter, file search, real-time voice -- these are production features that take months to build yourself. If you need them, OpenAI is the only option.
Structured output reliability. GPT models produce valid JSON and follow complex output schemas more reliably than Llama models. For applications with strict output format requirements, this gap matters.
80 TPS is fast enough. For most web applications, 80 TPS generates a 200-token response in 2.5 seconds. With streaming, the first tokens appear in 300ms. Many applications do not need faster than this.
Cost Breakdown at Production Scale
Real-time chatbot (1.5K input + 300 output): GPT-4o Mini cheaper at every volume — 100K req/day Groq 70B $3,210/mo vs Mini $1,350/mo. Groq's value here is speed, not price. Budget alternative Groq Llama 8B: 100K req/day = $369/mo (-73% vs Mini) AND 750 TPS (much faster). For simple tasks where 8B quality suffices, Groq wins both axes.
Scenario: Real-time chatbot (1,500 input / 300 output tokens per request)
| Daily Volume | Groq Llama 70B | GPT-4o Mini | Speed Winner |
|---|---|---|---|
| 10,000/day | $321/mo | $135/mo | Groq (315 TPS) |
| 50,000/day | $1,605/mo | $675/mo | Groq (315 TPS) |
| 100,000/day | $3,210/mo | $1,350/mo | Groq (315 TPS) |
GPT-4o Mini is cheaper at every volume. Groq's value is speed, not price, at the 70B parameter class.
Budget alternative: Groq Llama 8B ($0.05/$0.08)
| Daily Volume | Groq Llama 8B | GPT-4o Mini | Speed Winner |
|---|---|---|---|
| 10,000/day | $37/mo | $135/mo | Groq (750 TPS) |
| 100,000/day | $369/mo | $1,350/mo | Groq (750 TPS) |
For simple tasks where 8B model quality suffices, Groq Llama 8B is both faster and 73% cheaper than GPT-4o Mini.
How Should You Choose Between Groq and OpenAI?
Fastest possible inference: Groq (315 TPS, no GPU provider matches). Cheapest simple tasks: Groq Llama 8B ($0.05/$0.08, 750 TPS). Cheapest moderate quality: GPT-4o Mini ($0.15/$0.60). Maximum quality: OpenAI GPT-5.4/o3. Need fine-tuning/Assistants: OpenAI only. Voice/real-time: Groq (sub-100ms TTFT). Most teams: hybrid via TokenMix.ai — route latency-critical to Groq, quality-critical to OpenAI.
| Your Priority | Choose This | Why |
|---|---|---|
| Fastest possible inference | Groq | 315 TPS, nothing else comes close |
| Cheapest per token (simple tasks) | Groq Llama 8B | $0.05/$0.08, faster than everything |
| Cheapest per token (moderate quality) | OpenAI GPT-4o Mini | $0.15/$0.60, better quality per dollar |
| Maximum model quality | OpenAI GPT-5.4 / o3 | No open-source equivalent |
| Need fine-tuning | OpenAI | Groq does not offer fine-tuning |
| Need Assistants/stateful API | OpenAI | Not available on Groq |
| Voice/real-time conversation | Groq | Sub-100ms TTFT critical |
| Avoid vendor lock-in | Groq | Open models, portable |
| Best of both (speed + quality) | TokenMix.ai | Route by task to either provider |
Related: Compare all model pricing in our complete LLM API pricing comparison
What's the Bottom Line on Groq vs OpenAI?
Speed-vs-ecosystem trade-off. Groq = 4x faster on open-source models. OpenAI = better models + richer platform. Most production apps need both: Groq for speed-sensitive paths (real-time chat, voice AI), OpenAI for quality-sensitive paths (coding, complex reasoning, structured output). TokenMix.ai unified API routes by task type — speed and quality stop being mutually exclusive.
Groq vs OpenAI is a speed-versus-ecosystem trade-off. Groq delivers 4x faster inference on open-source models. OpenAI delivers better models and a richer platform.
The practical answer for production applications: use Groq for speed-sensitive paths (real-time chat, voice AI, interactive UX) and OpenAI for quality-sensitive paths (coding, complex reasoning, structured output).
TokenMix.ai makes this hybrid strategy simple. Route latency-critical requests to Groq, quality-critical requests to OpenAI, and cost-sensitive batch work to whichever is cheaper for the specific model class. One API, both providers, automatic routing.
Speed and quality are not mutually exclusive when your infrastructure is smart enough to route the right request to the right provider. Compare real-time speed benchmarks and pricing at TokenMix.ai.
FAQ
Is Groq faster than OpenAI?
Yes. Groq serves Llama 3.1 70B at 315 tokens per second versus OpenAI's GPT-4o at approximately 80 TPS. That is a 4x speed difference. Groq's smaller models (Llama 8B) reach 750 TPS. No GPU-based provider matches Groq's inference speed.
Is Groq cheaper than OpenAI?
It depends on the model comparison. Groq Llama 70B ($0.59/$0.79) is more expensive than GPT-4o Mini ($0.15/$0.60) per token. But Groq Llama 8B ($0.05/$0.08) is significantly cheaper. You pay a premium for Groq's speed at the 70B parameter class.
Can I run GPT-4o on Groq?
No. Groq only runs open-source models (Llama, Mixtral, Gemma). GPT-4o and GPT-5.4 are proprietary to OpenAI. If you need GPT-class model quality, you must use OpenAI or a compatible provider.
What is an LPU and why is it faster than a GPU?
Groq's Language Processing Unit (LPU) is custom silicon designed exclusively for inference. Unlike GPUs that handle diverse workloads, LPUs are optimized for the sequential token generation pattern of language models. This specialization eliminates the memory bandwidth bottleneck that limits GPU inference speed.
When should I choose speed over model quality?
Choose speed when response latency directly impacts user experience (real-time chat, voice AI), when throughput determines system capacity (batch processing pipelines), or when the application task is simple enough that open-source models perform equivalently to proprietary ones.
Can I use both Groq and OpenAI?
Yes. TokenMix.ai's unified API lets you route requests to Groq for speed-critical paths and OpenAI for quality-critical paths. No separate accounts or SDK changes needed. One API endpoint handles both providers.
Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: Groq Pricing, OpenAI Pricing, TokenMix.ai