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Cerebras vs Modal: Token Pricing, Speed & Intelligence

Full comparison of Cerebras and Modal — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.

Cerebras

CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B

Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.

Voice AIReal-time chatSpeedInteractive codingStreamingAgentic workflows
Open-weight hostHosts open weights

Modal

Managed inference API — 10 shared-endpoint models with per-token pricing

Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.

InferenceCost-efficiencyOpen-sourceChat
Open-weight hostHosts open weights

Key metric comparison

Cerebras
Modal
Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths and weaknesses

Cerebras

GPT OSS 120B: high-throughput inference on CS-4 wafer-scale silicon
Qwen 3.8 27B: fast efficient-tier inference
CS-4 chip: 58× larger than GPUs, purpose-built for AI inference
Significantly faster than conventional GPU inference — enables interactive use cases
Self-serve Developer tier — check cerebras.ai for current signup offers
Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel
Limited model selection vs. Together AI or Fireworks
No vision or multimodal support
No fine-tuning on Developer tier

Modal

Managed hardware and autoscaling — no infrastructure to operate
Published per-token rates including cached-input pricing
OpenAI-style per-token billing across all 10 models
Cached-input pricing available on every model in the catalog
Small fixed catalog of 10 models
Model-specific concurrency limits on shared pools
Prices are published rates re-read 2026-10-31, not continuously collected

Key differentiators

Cerebras

Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.

Modal

Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.

Frequently asked questions

Cerebras FAQs

How fast is Cerebras inference?

Cerebras delivers significantly higher throughput than conventional GPU-based providers on GPT OSS 120B and Qwen 3.8 27B, enabling real-time agentic workflows and interactive AI applications. Check cerebras.ai for current benchmark figures.

What is the Cerebras CS-4 chip?

The CS-4 is Cerebras's latest wafer-scale chip, 58× larger than a GPU. It provides 900,000+ AI cores and massive on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.

What models does Cerebras support?

Cerebras supports GPT OSS 120B and Qwen 3.8 27B on the Developer tier. Enterprise adds access to all models including production and preview models. Check cerebras.ai/pricing for current rates.

Modal FAQs

What models does Modal offer on Shared Endpoints?

Modal Shared Endpoints currently serve 10 models: GLM 5.3 Flash NVFP4, DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, GLM 5.3 Flash, Muse Spark Open, GLM 5.3, Inkling NVFP4, Qwen3.8-Max, Qwen3.8-Max VL, and Kimi K3. Use the model table above for per-model pricing and capability details. Source: Modal's published Shared Endpoints catalog, re-read 2026-10-31.

How much does Modal charge per 1M tokens?

Modal Shared Endpoints input prices range from $0.15/1M (GLM 5.3 Flash NVFP4) to $3.00/1M (Kimi K3). All models include cached-input pricing. See the model table for full per-model rates.

Does Modal offer prompt caching?

Yes. All 10 models in the Modal Shared Endpoints catalog have published cached-input pricing, ranging from $0.005/1M (DeepSeek V4.1 Flash 1017) to $0.30/1M (Kimi K3). Depending on the model, the cached-input rate is 77.5% to 98% below the standard input rate. Source: Modal's published Shared Endpoints rates, re-read 2026-10-31.

Provider resources

Cerebras

Provider page
Website
PricingDocs

Modal

Provider page
Website
PricingDocs

Cerebras — CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B

Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.

Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.

Modal — Managed inference API — 10 shared-endpoint models with per-token pricing

Modal Shared Endpoints serve a fixed catalog of models from Modal-managed pools. Modal manages the hardware and autoscaling, and usage is billed per token. Shared Endpoints have model-specific concurrency limits on concurrent in-flight requests. Prices are published rates from modal.com/docs/guide/supported-models.md, re-read 2026-10-31.

Modal Shared Endpoints provide managed per-token inference on a fixed 10-model catalog with published cached-input pricing and no infrastructure to manage.

Key strengths compared

Cerebras

  • GPT OSS 120B: high-throughput inference on CS-4 wafer-scale silicon
  • Qwen 3.8 27B: fast efficient-tier inference
  • CS-4 chip: 58× larger than GPUs, purpose-built for AI inference
  • Limited model selection vs. Together AI or Fireworks
  • No vision or multimodal support

Modal

  • Managed hardware and autoscaling — no infrastructure to operate
  • Published per-token rates including cached-input pricing
  • OpenAI-style per-token billing across all 10 models
  • Small fixed catalog of 10 models
  • Model-specific concurrency limits on shared pools

Key weaknesses compared

Cerebras

  • Limited model selection vs. Together AI or Fireworks
  • No vision or multimodal support
  • No fine-tuning on Developer tier

Modal

  • Small fixed catalog of 10 models
  • Model-specific concurrency limits on shared pools
  • Prices are published rates re-read 2026-10-31, not continuously collected

Provider category context

Cerebras is a inference api, founded in 2016. Modal is a inference api. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.

How to choose between them

Both Cerebras and Modal host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.