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Cerebras vs Google: Token Pricing, Speed & Intelligence

Full comparison of Cerebras and Google — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

Cerebras

Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth

Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.

Voice AIReal-time chatSpeedInteractive codingStreaming
Open-weight hostHosts open weights

Google

Gemini 2.5 — the largest context window at the lowest frontier price

Google DeepMind's Gemini family offers some of the most competitive frontier pricing, with Gemini 2.5 Pro delivering top-tier intelligence at $1.25/1M input tokens. The 1M+ token context window is the largest available. Gemini 2.5 Flash is a standout efficient model for vision and multimodal tasks.

VisionLong-contextCodingMultimodalCost-efficiency
Proprietary models

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

Live token pricing

Strengths & weaknesses

Cerebras

4,500+ tokens/sec on Llama 3.1 8B — fastest inference available
Sub-50ms time-to-first-token for real-time applications
Wafer-scale chip architecture eliminates GPU memory bottlenecks
Competitive pricing for the throughput delivered
OpenAI-compatible API
Very limited model selection — only a few Llama variants
No vision or multimodal support
No fine-tuning capability

Google

1M+ token context window — largest available
Best price-per-intelligence at frontier tier ($1.25/1M input)
Native multimodal: text, image, audio, video
Gemini 2.5 Flash is the best efficient vision model
Free tier available via Google AI Studio
No open-weight models
Complex tiered pricing based on context length
API reliability has historically lagged OpenAI

Key differentiators

Cerebras

Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications.

Google

Gemini 2.5 Pro delivers frontier-tier intelligence at $1.25/1M input tokens — the best price-to-performance ratio among all frontier models.

Frequently asked questions

Cerebras FAQs

How fast is Cerebras inference?

Cerebras delivers 4,500+ tokens/second on Llama 3.1 8B — roughly 10× faster than GPU-based providers like Groq (1,200 t/s) or Together AI (350 t/s). This makes it the fastest inference option available.

What is a Cerebras wafer-scale chip?

The Cerebras CS-3 chip is fabricated on a single silicon wafer rather than individual dies. This gives it 900,000 AI cores and 44GB of on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.

What models does Cerebras support?

Cerebras currently supports Llama 3.1 8B and 70B, and Llama 3.3 70B. The model selection is intentionally limited — Cerebras focuses on delivering extreme speed on a curated set of models rather than broad catalog coverage.

Google FAQs

How much does the Google Gemini API cost?

Gemini 2.5 Pro costs $1.25/1M input tokens (up to 200K context) and $10/1M output. Gemini 2.5 Flash is $0.15/$0.60 per 1M tokens. Gemini 2.0 Flash is even cheaper at $0.10/$0.40 per 1M tokens.

What is the context window for Gemini models?

Gemini 2.5 Pro and Flash both support a 1,048,576-token (1M+) context window — the largest available from any major LLM provider. This makes them ideal for processing entire codebases, books, or long document collections.

Does Gemini support vision and multimodal inputs?

Yes. All Gemini 2.x models natively support images, audio, and video inputs alongside text. Gemini 2.5 Flash is particularly strong for vision tasks at a low cost.

Provider resources

CerebrasWafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth

Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.

Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications.

GoogleGemini 2.5 — the largest context window at the lowest frontier price

Google DeepMind's Gemini family offers some of the most competitive frontier pricing, with Gemini 2.5 Pro delivering top-tier intelligence at $1.25/1M input tokens. The 1M+ token context window is the largest available. Gemini 2.5 Flash is a standout efficient model for vision and multimodal tasks.

Gemini 2.5 Pro delivers frontier-tier intelligence at $1.25/1M input tokens — the best price-to-performance ratio among all frontier models.

Key strengths compared

Cerebras

  • 4,500+ tokens/sec on Llama 3.1 8B — fastest inference available
  • Sub-50ms time-to-first-token for real-time applications
  • Wafer-scale chip architecture eliminates GPU memory bottlenecks

Google

  • 1M+ token context window — largest available
  • Best price-per-intelligence at frontier tier ($1.25/1M input)
  • Native multimodal: text, image, audio, video

Provider category context

Cerebras is a inference api, founded in 2016. Google is a frontier lab, founded in 1998. Cerebras as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Google as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.

How to choose between them

Choose Cerebras if you need 4,500+ tokens/sec on llama 3.1 8b — fastest inference available. Choose Google if you need 1m+ token context window — largest available. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.