Neither Cerebras nor NVIDIA is a universal winner for AI inference. Cerebras publishes high per-user generation speeds for selected models, while NVIDIA’s published Blackwell results emphasize benchmark cost per token for named model and software configurations. Those figures measure different things and are not a like-for-like comparison of total production cost. The right choice depends on your model, latency and throughput targets, pricing basis, and whether you want a managed API or to operate an inference stack.
What the published comparisons actually show
The clearest available head-to-head figures are output-speed measurements for five models shown in Cerebras’s May 2026 Form S-1/A. The filing presents GPU and Cerebras results in tokens per second; it attributes the Cerebras measurements to the company and notes an Artificial Analysis benchmark published April 14, 2026. The GPU figures are specific to that comparison, not a benchmark of every NVIDIA system or deployment. Cerebras Form S-1/A
| Model in the comparison | GPU output speed | Cerebras output speed |
|---|---|---|
| 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 |
These are output-speed figures, not a complete account of latency, throughput at a specified concurrency, or cost. The source does not establish that every other variable—such as precision, prompt and output lengths, or serving configuration—is matched in a way that would make the table a universal procurement benchmark.
A newer Cerebras system claim
In an August 18, 2026 announcement, Cerebras said its CS-4 produced more than 4,400 tokens per second per user on GPT-OSS-120B with identical prompts, and claimed up to 30 times the speed of GPU solutions. This is a vendor claim about the announced CS-4 configuration; it should not be combined with the earlier five-model table as though the systems, software, and test conditions were the same. Cerebras CS-4 announcement
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How to read the cost figures
NVIDIA’s published figures are infrastructure benchmark costs per million tokens, not a customer’s complete bill. In April 2026, NVIDIA’s performance page reported a SemiAnalysis InferenceX result of $0.02 per million tokens for GPT-OSS-120B on a B200 using TensorRT-LLM, compared with $0.11 per million tokens at launch. NVIDIA described the change as a fivefold improvement through software optimization. The same page reported $0.123 per million tokens for a GB300 NVL72 at 116 tokens per second per user, using NVIDIA Dynamo and TensorRT-LLM. These figures apply to the named benchmark configurations. NVIDIA performance benchmarking
Cerebras’s developer pricing, accessed October 7, 2026, lists API charges by input and output tokens. Those rates are not directly comparable with NVIDIA’s infrastructure cost-per-token benchmark: the former are published service prices, while the latter are benchmark costs and do not by themselves establish a buyer’s full deployment cost.
Rank #2
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| Offering or benchmark | Model and configuration | Published figure | What the figure represents |
|---|---|---|---|
| Cerebras developer pricing | GPT OSS 120B | Approximately 3,000 tokens/s; $0.35 per million input tokens; $0.75 per million output tokens | Published API speed and input/output rates; enterprise production pricing is quote-based. |
| Cerebras developer pricing | Qwen 3.8 27B | Approximately 1,850 tokens/s; $0.99 per million input tokens; $1.49 per million output tokens | Published API speed and input/output rates; performance varies by model and configuration. |
| NVIDIA B200 benchmark | GPT-OSS-120B, TensorRT-LLM; SemiAnalysis InferenceX, April 2026 | $0.02 per million tokens; $0.11 per million tokens at launch | NVIDIA-reported benchmark cost, with the launch figure included as the stated comparison; not an API rate or total cost of ownership. |
| NVIDIA GB300 NVL72 benchmark | NVIDIA Dynamo and TensorRT-LLM; 116 tokens/s per user; SemiAnalysis InferenceX, April 2026 | $0.123 per million tokens | NVIDIA-reported benchmark cost at the stated interactivity level; not an API rate or total cost of ownership. |
Cerebras’s current pricing page describes the developer tier as suitable for exploration and says enterprise production pricing is quote-based. It also lists AWS Marketplace, OpenRouter, Hugging Face, and Vercel as access partners; availability of models, features, capacity, and performance depends on availability and applicable terms. Cerebras inference pricing
Why the software stack matters
An accelerator’s results depend partly on the serving software and configuration around it. NVIDIA’s cited cost results specify TensorRT-LLM, with the GB300 NVL72 result also naming NVIDIA Dynamo. NVIDIA’s own comparison of the B200 launch and later benchmark figures attributes the improvement to software optimization. Hardware model alone is therefore not enough to predict performance or cost.
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For Cerebras, the published developer page warns that performance varies by model and configuration. Treat its listed speeds as model-specific reference figures, rather than guarantees for a different prompt mix, concurrency level, or production setup.
How to compare the platforms for your workload
Before choosing, run the same workload through the actual service or deployment configuration you are considering. A defensible comparison should keep the model and serving conditions aligned and compare both user experience and economics.
Rank #4
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- Match the model and precision. Use the same model version and precision where both platforms support them. A different model or precision can change quality, memory use, speed, and cost.
- Use representative prompts and generations. Match prompt length and generated-token length to the traffic you expect, rather than comparing short demonstrations with long production requests.
- Set concurrency and a latency target. Measure the load level you need to serve and the response-time target you must meet. A fast result for one user may not describe behavior under concurrent demand.
- Measure the right speed metrics. Track time to first token and per-user decode speed for responsiveness, as well as aggregate throughput at the latency target for capacity planning.
- Compare costs on the same basis. Keep published API input/output charges separate from amortized infrastructure cost. For a deployment you operate, include utilization, serving software, operations, capacity, and commercial terms in the calculation; a benchmark cost per million tokens alone does not settle total cost.
- Check operational fit. Confirm availability, geography, capacity, service-level commitments, and who is responsible for serving and operating the system.
Which option fits your deployment?
Consider Cerebras when managed access and fast generation are priorities
Cerebras provides published developer API rates and approximate speeds for listed models, with enterprise production pricing available by quote. Its published comparisons show high output speeds on selected models, but whether those results matter for your application depends on the exact model and workload. The developer pricing page lists access through its own offering and named distribution partners.
Consider NVIDIA when you need a specified Blackwell deployment stack
The cited NVIDIA results give model- and configuration-specific cost benchmarks for B200 and GB300 NVL72 systems using named inference software. They can inform an infrastructure evaluation, but do not establish what a particular buyer will pay or achieve at its utilization, latency target, and operating scale.
Best Value
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Choose by measured fit, not by a headline
The available figures do not provide a single matched, independently audited comparison of current all-in production costs across the two providers. Use the published results to identify configurations worth evaluating, then compare the same workload and service requirements on the actual options available to you.
Quick Recap
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