Google vs Groq: Token Pricing, Speed & Intelligence

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

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

Groq

LPU-powered inference — the fastest tokens per second available

Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.

SpeedReal-time chatVoice AICost-efficiencyBatch processing

Open-weight hostHosts open weights

Key metrics

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 & weaknesses

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

Groq

750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference

Sub-100ms time-to-first-token for real-time applications

Very competitive pricing on open-weight models

OpenAI-compatible API

Free tier available

Limited model selection vs. Together AI or Fireworks

No vision model support on most models

No fine-tuning capability

Key differentiators

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.

Groq

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

Frequently asked questions

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.

Groq FAQs

How fast is Groq inference?

Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B. This is 4–5× faster than typical GPU-based providers, making it ideal for real-time applications.

How much does Groq cost?

Llama 3.3 70B costs $0.59/1M input and $0.79/1M output tokens. Llama 3.1 8B is just $0.05/$0.08 per 1M tokens — among the cheapest options for a capable open-weight model.

What is a Groq LPU?

A Language Processing Unit (LPU) is Groq's custom silicon designed specifically for sequential token generation. Unlike GPUs which are optimised for parallel matrix operations, LPUs excel at the autoregressive decoding step that dominates LLM inference latency.

Provider resources

Google

Provider page Website Pricing Docs

Groq

Provider page Website Pricing Docs

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

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

Groq — LPU-powered inference — the fastest tokens per second available

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

Key strengths compared

Google

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

Groq

▸750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
▸Sub-100ms time-to-first-token for real-time applications
▸Very competitive pricing on open-weight models

Provider category context

Google is a frontier lab, founded in 1998. Groq is a inference api, founded in 2016. Google as a frontier lab trains and serves its own proprietary models. Groq as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.

How to choose between them

Choose Google if you need 1m+ token context window — largest available. Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — fastest gpu-class inference. 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.