Head-to-Head Model Comparison

Gemini 2.0 Pro vs Claude 3.7 Sonnet

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Gemini 2.0 ProProfile

Claude 3.7 SonnetProfile

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Popular Matchups:Gemini 2.0 Pro vs Claude 3.7

Google

Gemini 2.0 Pro

Modelo Principal de Fronteira

O Gemini 2.0 Pro da Google é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 2M, velocidade de 95 tokens/s, índice de inteligência de 52/100 e t

Intelligence Index52 / 100

Input Token:

$1.25 / 1M

Output Token:

$5.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

63.8%

Anthropic

Claude 3.7 Sonnet

Líder em Programação e Código

O Claude 3.7 Sonnet da Anthropic é um modelo de IA projetado para engenharia de software autônoma, refatoração de código e depuração avançada, oferecendo janela de contexto de 200K, velocidade de 72 tokens/s, índice de inteligência de 48/10

Intelligence Index48 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

70.3%

Principais Diferenças em Resumo

Gemini 2.0 Pro is 2.7x cheaper for API usage (input + output tokens combined).
Gemini 2.0 Pro has a larger context window (2M vs 200K tokens).
Gemini 2.0 Pro is faster at 95 tok/s vs 72 tok/s.
Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 63.8%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Gemini 2.0 Pro	Claude 3.7 Sonnet
Intelligence Index Composite reasoning & knowledge score (0-100)	52	48
Provider	Google	Anthropic
Input Price Cost per million input tokens	$1.25 / 1M	$3.00 / 1M
Output Price Cost per million output tokens	$5.00 / 1M	$15.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.97 / 1M	$2.31 / 1M
Context Window Maximum input + output tokens	2M tokens~3,000 A4 pages of text	200K tokens~300 A4 pages of text
Max Output Tokens Per single request	8K	64K
Throughput Speed Tokens generated per second	95 tok/s	72 tok/s
SWE-bench Verified Real-world software engineering	63.8%	70.3%
GPQA Diamond Graduate-level reasoning	71.5%	65%
Cached Input Price Prompt caching discount per 1M tokens	$0.31 / 1M	$0.30 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Claude 3.7 Sonnet

+6.5% SWE-bench lead

Cost

Gemini 2.0 Pro

2.7x cheaper monthly runtime

Speed

Gemini 2.0 Pro

+32% faster generation

Context

Gemini 2.0 Pro

2M tokens window

Pontuações de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Gemini 2.0 ProClaude 3.7 Sonnet

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

63.8%vs70.3%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

71.5%vs65%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

79.8%vs78.2%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1330ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

11.4%vs8.9%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

42.1%vs35.2%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

38.3%vs40.1%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65.6%vs64.8%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

82.2%vs61.3%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

71.8%vs71.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs72 tok/s

Latência e Tempo de Resposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Gemini 2.0 Pro0.62s

Claude 3.7 Sonnet0.52s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemini 2.0 Pro5.88s

Claude 3.7 Sonnet7.46s

Simulador Interativo de Custo de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemini 2.0 Pro Monthly API Bill

$6.84 / month

$1.25/1M in · $5/1M out

Claude 3.7 Sonnet Monthly API Bill

$18.45 / month

$3/1M in · $15/1M out

Gemini 2.0 Pro saves approximately $11.61 / month (63%) under this workload.2.7x Savings

Gemini 2.0 Pro · Strengths & Key Advantages

Janela de contexto de 2M com limite máximo de saída de 8K tokens (SWE-bench Verified 63.8% · GPQA Diamond 71.5%)
Taxa de transferência rápida de 95 tokens/segundo com latência inicial (TTFT) de 620 ms
Economia de API competitiva ($1.25/$5 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Claude 3.7 Sonnet · Strengths & Key Advantages

Janela de contexto de 200K com limite máximo de saída de 64K tokens (SWE-bench Verified 70.3% · GPQA Diamond 65%)
Taxa de transferência rápida de 72 tokens/segundo com latência inicial (TTFT) de 520 ms
Economia de API competitiva ($3/$15 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Gemini 2.0 Pro	Claude 3.7 Sonnet
Cost per Task Estimated USD to finish one task	$1.59	$1.42
Output Tokens / Task Tokens generated per task	60K	41K
Reasoning Tokens / Task Thinking tokens burned per task	33K	22K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 2.0 Pro	Claude 3.7 Sonnet
Official API Model ID (SDK)	models/gemini-2-pro	claude-3-7-sonnet-20250201
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date	2025-02	2025-02
Knowledge Cutoff	January 2025	November 2024
Architecture	Multimodal Deep Reasoning Sparse MoE	Hybrid Reasoning MoE
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.31/1M with configurable TTL	$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Native Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio, video	text, vision
Output Modalities	text	text

Gemini 2.0 Pro vs Claude 3.7 Sonnet: Respostas Rápidas (FAQ)

Which is more intelligent, Gemini 2.0 Pro or Claude 3.7 Sonnet?

Gemini 2.0 Pro is more intelligent. It scores 52 compared with 48 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Pro is faster, generating 95 tokens per second compared with 72 tokens per second.

Which model is cheaper to run?

Gemini 2.0 Pro is cheaper at $0.97 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude 3.7 Sonnet has lower latency, with a time to first token of 0.52s compared with 0.62s.

Which model has a larger context window?

Gemini 2.0 Pro has a larger context window, supporting 2M tokens compared with 200K tokens.

Veredito

Tanto Gemini 2.0 Pro (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) quanto Claude 3.7 Sonnet (Indicado para engenharia de software autônoma, refatoração de código e depuração avançada, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Gemini 2.0 Pro lidera em inteligência geral e programação, Gemini 2.0 Pro oferece geração mais rápida e Gemini 2.0 Pro apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Gemini 2.0 Pro ou Claude 3.7 Sonnet?+

Nas avaliações de inteligência e código, Gemini 2.0 Pro leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Gemini 2.0 Pro ou Claude 3.7 Sonnet?+

O Gemini 2.0 Pro custa $1.25 / $5.00 por 1M de tokens (entrada/saída) e o Claude 3.7 Sonnet custa $3.00 / $15.00. No custo combinado, Gemini 2.0 Pro é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Gemini 2.0 Pro suporta 2M tokens de contexto contra 200K tokens do Claude 3.7 Sonnet, enquanto Gemini 2.0 Pro entrega maior velocidade de geração.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemini 2.0 Pro vs Claude 3.7 Sonnet

Troque e Compare Qualquer Modelo

Gemini 2.0 Pro

Claude 3.7 Sonnet

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Gemini 2.0 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Claude 3.7 Sonnet · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Gemini 2.0 Pro vs Claude 3.7 Sonnet: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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