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Neither Cerebras nor Nvidia is a proven universal winner for AI inference. Cerebras publishes high per-user generation speeds for selected models and API prices for specific models; Nvidia publishes Blackwell infrastructure cost-per-token results tied to named software and benchmark configurations. Those figures measure different things, so they do not establish which platform will be cheaper or faster for your workload.
What do the published performance figures show?
A useful head-to-head data point appears in Cerebras’s Form S-1/A, filed May 4, 2026. Its table reports output-speed measurements attributed to Cerebras, alongside an Artificial Analysis benchmark published April 14, 2026. The figures are tokens per second:
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| Model | GPU comparator | Cerebras |
|---|---|---|
| Qwen-3 235B | 262 tokens/s | 873 tokens/s |
| MiniMax M2.5 | 223 tokens/s | 1,039 tokens/s |
| GLM 4.7 | 245 tokens/s | 1,164 tokens/s |
| OpenAI GPT-OSS-120B | 795 tokens/s | 1,735 tokens/s |
| Llama-3.3 70B | 164 tokens/s | 2,457 tokens/s |
The filing’s comparison is evidence for those models and test conditions, not a general result for every Nvidia GPU deployment. The table does not identify the GPU model in the figures summarized here, so it should not be read as a controlled comparison against every current Nvidia configuration.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Cerebras later announced a separate CS-4 result: more than 4,400 tokens per second per user on GPT-OSS-120B with identical prompts, and a claim of up to 30 times the speed of GPU solutions. That August 18, 2026 announcement is a vendor claim; its setup is not established as matching the earlier five-model comparison, so the figures should not be combined into one ranking.
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What do Cerebras’s published API prices buy?
The Cerebras pricing page, accessed October 7, 2026, lists these developer rates and approximate model speeds:
| Model | Approximate speed listed | Input price per million tokens | Output price per million tokens |
|---|---|---|---|
| GPT OSS 120B | Approximately 3,000 tokens/s | $0.35 | $0.75 |
| Qwen 3.8 27B | Approximately 1,850 tokens/s | $0.99 | $1.49 |
These are API token charges, not a full cost estimate for owning or operating inference infrastructure. The same Cerebras pricing page says enterprise production pricing is quote-based and warns that speed varies by model and configuration.
What do Nvidia’s Blackwell cost figures measure?
Nvidia’s published figures are infrastructure benchmark costs attributed to SemiAnalysis InferenceX, not equivalent to the Cerebras API rates above:
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| System and workload | Reported benchmark cost | Reported configuration |
|---|---|---|
| B200 running GPT-OSS-120B | $0.02 per million tokens; Nvidia says the launch figure was $0.11 per million tokens, a fivefold improvement | TensorRT-LLM; SemiAnalysis InferenceX, April 2026 |
| GB300 NVL72 | $0.123 per million tokens at 116 tokens/s per user | NVIDIA Dynamo and TensorRT-LLM; SemiAnalysis InferenceX, April 2026 |
Nvidia’s performance page reports these results. The model for the GB300 figure is not stated in the cited summary. Benchmark cost per token alone does not establish a buyer’s complete cost: utilization, deployment and operating costs, capacity, and commercial terms can change the economics. Nor does it provide a directly matched comparison with the Cerebras API rates.
Why can speed and cost rankings differ?
Inference performance is not one number. Per-user decode speed describes how quickly a single user receives generated tokens; aggregate throughput describes how many tokens a system serves across users. Time-to-first-token measures the wait before generation begins. A system can lead on one measure and still miss the latency or throughput target that matters to an application.
The software stack also matters. Nvidia’s reported Blackwell figures name TensorRT-LLM, and the GB300 result additionally names NVIDIA Dynamo. These are part of the benchmark configuration: software optimization can materially affect performance and cost without changing the accelerator hardware.
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Cerebras’s August announcement quotes Dylan Patel, founder and CEO of SemiAnalysis, saying CS-4 improves deployability, reliability, and networking to enable scaling performance to larger models. This is a quote in a Cerebras-hosted announcement, not an independent benchmark conclusion.
How should you compare them for a real workload?
Before choosing a platform, compare the same serving task rather than headline figures from different tests. Record:
- Model and precision: match the exact model version and numerical precision.
- Request shape: use the same prompt length and generated-token length.
- Load and latency target: test the expected concurrency and service-level target.
- Interactive performance: measure time-to-first-token and per-user output speed.
- Capacity: measure aggregate throughput at the target latency, not only peak tokens per second.
- Cost basis: separate input/output API charges from amortized infrastructure cost, and include relevant deployment, utilization, and operating costs.
- Operations and access: account for availability, geography, capacity, software, and whether you want a managed API or to operate hardware and serving yourself.
Cerebras lists self-serve developer access for exploration and quote-based enterprise production capacity. Its pricing page also names AWS Marketplace, OpenRouter, Hugging Face, and Vercel as access partners; listed models, features, capacity, and performance remain subject to availability and applicable terms. Those are distribution paths, not evidence of any particular commercial relationship beyond access.
For a repeatable decision, run the same representative prompts and load against the actual available configurations, then compare measured service quality and total cost for your target volume. The published sources do not provide a single matched, independently audited comparison of current all-in production costs across both providers.
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