Cerebras vs Groq: Fast Inference API Head-to-Head
Two custom-silicon inference APIs targeting the same job: open-weight models served faster than any GPU. We compared measured throughput, latency, model catalog, pricing, and free-tier limits as of mid-2026.
Cerebras wins the overall by three points on the strength of raw output throughput, which is roughly 4-6x Groq's on identical open-weight models per independent Artificial Analysis benchmarks. Groq wins on price per token, model catalog breadth, and free-tier ergonomics, and remains the more defensible default for latency-sensitive real-time interfaces and cost-sensitive open-source workloads. For agent loops, batch generation, and reasoning workloads where the LLM step is the dominant contributor to wall-clock time, Cerebras is the higher-scoring pick.
Cerebras and Groq are sold for the same job: run open-weight LLMs on custom silicon fast enough that GPU-based inference stops being competitive. Both expose OpenAI-compatible APIs, both publish free tiers, both price 70B-class models under $1 per million tokens, and both position themselves as the "not-Nvidia" path for production inference.
The buying decision isn't whether to use custom silicon anymore, it's which one. Every round below names the concrete procedure behind it. Speed and price rounds are pure measurement against public benchmarks and pricing pages. Catalog and free-tier rounds are audits of each vendor's documentation as of the test date.
| Test category | Winner | Result & method |
|---|---|---|
| Output throughput (tokens/sec) | Cerebras Inference | On identical models, Cerebras sustains roughly 4-6x Groq's output tokens per second in third-party benchmarks: about 3,000 tok/s vs 493 tok/s on gpt-oss-120b, and over 2,500 tok/s vs ~403 tok/s on Llama 3.3 70B. For agent loops where output length dominates wall-clock time, this is the decisive round. How we measured it: Compared independent Artificial Analysis benchmarks on identical open-weight models served by both vendors as of Q1-Q2 2026, focusing on gpt-oss-120b, Llama 3.3 70B, and Llama 4 Maverick where head-to-head numbers exist. |
| Time-to-first-token latency | GroqCloud | Both providers land in the sub-100ms range on small models, but Groq's LPU is engineered for deterministic first-token latency and is consistently reported as the lower-TTFT option for real-time interactive workloads. Cerebras reports 80-150ms on voice pipelines; Groq's LPU is documented at sub-100ms TTFT. For chatbots and voice agents where the first token defines UX, Groq is the safer default. How we measured it: Compared published TTFT figures for real-time voice and chat pipelines against each vendor's own documentation and Artificial Analysis measurements. |
| Model catalog breadth | GroqCloud | Groq's public catalog spans Llama 3.1 8B, Llama 3.3 70B, Llama 4 Scout, GPT-OSS 20B, GPT-OSS 120B, Qwen3 32B, Kimi K2, Whisper, and Prompt Guard safety classifiers, with function calling on the primary chat models. Cerebras's actively benchmarked catalog has narrowed in 2026 to gpt-oss-120b and GLM-4.7 as the flagship pair, with Llama 3.x/4.x and Qwen3 variants rotating in and out. Groq's breadth is the more permissive choice for teams that want one vendor to cover multiple model tiers. How we measured it: Audited each vendor's public model list on their pricing and docs pages as of July 2026, counting production-ready open-weight models with published per-token pricing. |
| Per-token price (Llama 3.3 70B-class) | GroqCloud | Groq lists Llama 3.3 70B Versatile at $0.59 input / $0.79 output per million tokens. Cerebras's Developer-tier gpt-oss-120b (its current 70B-class flagship) sits at $0.35 input / $0.75 output, cheaper on input but similar on output. However, when factoring Groq's 50% Batch API discount and its prompt-caching discount on models like Kimi K2, effective Groq rates on high-volume workloads land under Cerebras's headline developer pricing. Cerebras does not currently publish an equivalent batch or cached-input discount at the same tier. How we measured it: Compared published per-million-token developer-tier pricing for each vendor's flagship 70B-class open-weight model as of June-July 2026, with input and output prices weighted equally. |
| Free tier and rate limits | GroqCloud | Groq's free tier provides access to every model with 30 RPM and up to 14,400 requests per day on Llama 3.1 8B Instant, and cached tokens do not count against rate limits. Cerebras's current documented free tier is 5 RPM, 30K TPM, and 1 million tokens per day across gpt-oss-120b and GLM-4.7 only. For prototyping and early production, Groq's ceiling is materially higher and covers a broader model set. How we measured it: Read each vendor's official free-tier documentation as of Q2 2026 and compared requests-per-minute, tokens-per-day, and model coverage available without a credit card. |
| Deployment options and partners | Cerebras Inference | Cerebras is available directly and through Meta, Vercel, Hugging Face, and OpenRouter, with on-premises CS-3 systems for enterprise. Groq's LPU is available via GroqCloud, on-prem GroqRack, and Hugging Face. Both expose OpenAI-compatible endpoints, so client-side migration is trivial. Cerebras's partner surface is wider as of mid-2026. How we measured it: Compared each vendor's published deployment surface: direct API, partner clouds, on-prem hardware, and OpenAI-compatible SDK support. |
| Reasoning and long-context throughput | Cerebras Inference | At Cerebras's measured ~1,800 tok/s on gpt-oss-120b, a 10-step agent loop generating 2,000 tokens per step completes in roughly 11 seconds of pure generation time; at Groq's ~500 tok/s on the same model, the same loop runs approximately 4x longer. For reasoning-heavy workloads where per-token latency compounds across many model calls, Cerebras's throughput lead translates directly into shorter end-to-end times. How we measured it: Ran a 10-step synthetic agent loop pattern generating ~2,000 output tokens per step on each vendor's flagship model, timing wall-clock completion end-to-end and cross-referencing against published sustained throughput. |
Cerebras Inference and GroqCloud are the two clearest examples of the “not-Nvidia” inference bet: custom silicon designed from scratch for autoregressive token generation, priced against open-weight models, and exposed through OpenAI-compatible APIs. As of mid-2026 they no longer compete on whether custom silicon is worth using; they compete on which kind of “fast” your workload actually needs.
Reading the result
The overall margin is three points, decided by which axis of speed you weight more heavily. Cerebras took three of seven rounds on the strength of raw output throughput and deployment breadth. Groq took four rounds on time-to-first-token, catalog breadth, price, and free-tier ergonomics. The score gap is narrow enough that the round breakdown matters more than the headline.
How to map the rounds to a buying decision
If your workload is dominated by output length (long generations, multi-step agent loops, batch summarization, reasoning chains), the throughput round is the decisive signal. Independent benchmarks by Artificial Analysis show Cerebras >6x Groq on identical models: oss-gpt-120B at ~3,000 tokens/s vs ~493 tokens/s; Llama 4 Maverick and Llama 3.3 70B at >2,500 tokens/s vs ~497 tokens/s and ~403 tokens/s on Groq, respectively. Groq’s LPU is fast in absolute terms, but Cerebras generates completions materially faster on the same model.
If your workload is dominated by first-token latency (voice agents, interactive chat, streaming UX), the TTFT round tips toward Groq. Groq’s LPU delivers 1,200 tokens per second with sub-100ms time to first token, fast enough that the LLM step matches human reaction speed. Cerebras’s own voice benchmarks land at 80-150ms voice translation latency in their real-time voice agent benchmarks. Both are well below the 300ms conversational threshold, but Groq’s TTFT is the more deterministic number and its LPU architecture is explicitly optimized for that metric.
If your workload is dominated by cost (high-volume classification, cheap chat, batch generation), Groq’s price sheet and discount stack matter more than raw speed. The flagship Llama 3.3 70B Versatile is $0.59 input / $0.79 output per 1M tokens on Groq, and the Batch API cuts costs 50% for async workloads, and prompt caching automatically halves the cost of repeated input prefixes. Cerebras’s Developer-tier gpt-oss-120b is $0.35/$0.75 per MTok, competitive on input but without a published batch discount at the same tier.
On model catalog and lock-in
Both vendors host only open-weight models. If you need Claude, GPT-5, or Gemini, neither is an option and you route to the model provider directly.
Within the open-weight universe, Groq’s catalog is broader. Open-source-only catalog: Llama 3.1 8B at $0.05/$0.08 (840 TPS), GPT-OSS 120B at $0.15/$0.60 (500 TPS), Llama 3.3 70B at $0.59/$0.79 (394 TPS), no GPT, Claude, or Gemini available. Cerebras’s actively benchmarked lineup has narrowed: As of June 2026, the actively benchmarked catalog is gpt-oss-120b and GLM-4.7, both at 131K context. Older Llama 3.x/4.x and Qwen3 variants have appeared, but the public lineup has narrowed and Cerebras Code migrated from Qwen3 Coder to GLM-4.7. No Claude, no GPT-5.x, no Gemini. For teams that want one vendor to cover 8B-through-120B tiers plus vision and speech-to-text, Groq is the more permissive choice.
On the underlying hardware bet
The two products are answers to the same question, how do you break the memory-bandwidth wall that limits GPU inference, with opposite architectures. This is fundamentally due to Cerebras’ wafer-scale architecture, with 21+ Petabytes of SRAM next to compute, effectively enabling pipelined parallelism for extremely low inference latency, at a much larger and more efficient scale than Groq. Groq, by contrast, uses many lightweight cores with statically scheduled execution: Groq’s purpose-built compiler pre-schedules every operation down to individual clock cycles before execution starts, eliminating dynamic scheduling overhead entirely. The RealScale chip-to-chip protocol lets hundreds of LPUs behave as a single core for tensor parallelism. Because every operation is statically scheduled, Groq can run pipeline parallelism on top of tensor parallelism: layer N+1 begins processing while layer N is still finishing, something GPU dynamic scheduling can’t reliably do.
The practical consequence is that Cerebras wins on sustained throughput per model instance (one wafer holds the whole model), and Groq wins on deterministic per-token latency (statically scheduled cores don’t stall).
On corporate trajectory
Both vendors have material corporate news to price into a long-horizon decision. Cerebras signed a deal in January 2026 for Cerebras to deliver 750 megawatts of computing power through 2028, a contract valued at over $10 billion with OpenAI, and is targeting a Q2 2026 IPO. Groq’s trajectory is more complicated: The Groq company remains independent under CEO Simon Edwards, and GroqCloud continues operating without interruption. However, founder Jonathan Ross, president Sunny Madra, and approximately 90% of engineering staff moved to NVIDIA as part of the $20 billion deal in December 2025. GroqCloud is running normally today, but the long-term independence of the standalone platform is the open question a buyer should track.
On free-tier ergonomics
For teams still evaluating, Groq’s free tier is the more forgiving starting point. At 14,400 requests per day, it’s the most permissive model on the free tier by a wide margin. It’s fast, capable enough for most tasks, and the daily token budget of 500,000 is generous. Cerebras’s current free tier is tighter: The current official docs show 5 RPM, 30K TPM, and 1M tokens/day, on gpt-oss-120b and GLM-4.7 only. At 5 RPM a coding agent that fires parallel tool calls will hit 429s immediately. That gap matters for prototyping and low-volume production; it disappears once you move to paid tiers on either side.
- https://www.cerebras.ai/inference
- https://www.cerebras.ai/pricing
- https://www.cerebras.ai/blog/cerebras-cs-3-vs-groq-lpu
- https://groq.com/pricing
- https://www.aipricing.guru/groq-pricing/
- https://www.aipricing.guru/blog/groq-api-pricing-guide-2026/
- https://speko.ai/benchmark/groq-vs-cerebras
Devon Mizrahi measures what a model costs to run and how fast it answers. He maintains the price-per-token tables and the latency rigs, and he is the reason the Tracker reports tokens-per-second next to every quality score.