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Groq vs DeepInfra
Groq sells speed from its LPU on a handful of models. DeepInfra sells the lowest prices across 150+ open models. The workload's patience decides it.
By The Subconscious Team · Updated
Groq vs DeepInfra: key differences
One host optimizes seconds, the other optimizes cents. Groq's LPU publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B, several times what GPU hosts manage, with tail latency that stays close to the median. DeepInfra runs GPUs and competes on price, with Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. Groq is also cheap on its small models, so the real gap is breadth. DeepInfra lists 150+ models across text, image and speech. Groq's catalog is small and shrinking after Llama 3.3 70B and Llama 3.1 8B shut down on August 16, 2026.
Neither handles custom weights. Groq hosts no fine-tuned models, and DeepInfra has no managed fine-tuning. Context limits need checking on both: Groq caps around 131K, and DeepInfra's FP4 DeepSeek V4 Pro caps at 66K. DeepInfra's heavy default quantization can cut quality, so teams should pin precision per model. For a voice agent or tight multi-step loop, Groq's speed pays for itself. For bulk extraction, tagging and synthetic data where nobody is waiting, DeepInfra's price and catalog win.
What Groq and DeepInfra do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 27B
Full Groq profileDeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra profileShould you choose Groq or DeepInfra?
Groq
Choose Groq for
- Voice agents where pauses sound awkward
- Multi-call agent loops that need each step fast
- Predictable latency for strict SLAs
DeepInfra
Choose DeepInfra for
- Bulk extraction and tagging at the lowest price
- Picking from 150+ open models without contracts
- Fast access to new Hugging Face releases
Groq vs DeepInfra at a glance
| Attribute | Groq | DeepInfra |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | DeepSeek V4 Flash, Llama 3.1 8B |
| Speed | 500–1,000 tok/s | ~33 tok/s on DeepSeek V4 Pro (FP4) |
| Price | Near the floor on small models | From $0.02 per 1M |
| Customization | No fine-tuned model hosting | No managed fine-tuning |
| Deployment | GroqCloud API | Shared API, no contracts |
| Long context | Around 131K max | 66K on FP4 DeepSeek V4 Pro |
Frequently asked questions
What is the difference between Groq and DeepInfra?
Groq sells speed from its LPU on a handful of models. DeepInfra sells the lowest prices across 150+ open models. The workload's patience decides it.
When should I choose Groq over DeepInfra?
Voice agents where pauses sound awkward; Multi-call agent loops that need each step fast; Predictable latency for strict SLAs.
When should I choose DeepInfra over Groq?
Bulk extraction and tagging at the lowest price; Picking from 150+ open models without contracts; Fast access to new Hugging Face releases.
Is Groq or DeepInfra cheaper?
Groq: Near the floor on small models. DeepInfra: From $0.02 per 1M. The cheaper choice depends on the model and workload.
Which has more context, Groq or DeepInfra?
Groq: Around 131K max. DeepInfra: 66K on FP4 DeepSeek V4 Pro.
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If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.