Head-to-Head Model Comparison

Gemini 3.1 Pro vs Claude Sonnet 4.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期:October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 3.1 Pro vs Claude Sonnet 4.5: Benchmarks, Pricing & Speed Comparison

Google 的 Gemini 3.1 Pro 与 Anthropic 的 Claude Sonnet 4.5 是当前备受关注的两款主力 AI 模型。Gemini 3.1 Pro 提供 1.05M Token 上下文窗口,API 定价为每百万 Token 输入 $2.00 / 输出 $12.00;而 Claude Sonnet 4.5 提供 1M Token 上下文窗口,定价为每百万 Token 输入 $3.00 / 输出 $15.00。以下为两者的基准测试、延迟与成本全面对比。

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Google

Gemini 3.1 Pro

顶级编程与软件工程

Google 推出的 Gemini 3.1 Pro 是一款专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程大模型,具备 1.0M 上下文窗口、110 Token/秒生成速度与 86/100 综合智能指数(每百万 Token $2 输入 / $12 输出)。

Intelligence Index86 / 100
Input Token:

$2.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

Anthropic

Claude Sonnet 4.5

旗舰前沿大模型

Anthropic 推出的 Claude Sonnet 4.5 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型,具备 1M 上下文窗口、90 Token/秒生成速度与 91/100 综合智能指数(每百万 Token $3 输入 / $15 输出)。

Intelligence Index91 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

77.8%

核心差异一览

  • Gemini 3.1 Pro is 1.3x cheaper for API usage (input + output tokens combined).
  • Gemini 3.1 Pro has a larger context window (1M vs 1M tokens).
  • Gemini 3.1 Pro is faster at 110 tok/s vs 90 tok/s.
  • Claude Sonnet 4.5 leads on SWE-bench coding (77.8% vs 74.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3.1 ProClaude Sonnet 4.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8691
Provider
GoogleAnthropic
Input Price
Cost per million input tokens
$2.00 / 1M$3.00 / 1M
Output Price
Cost per million output tokens
$12.00 / 1M$15.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.95 / 1M$2.31 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K64K
Throughput Speed
Tokens generated per second
110 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
74.5%77.8%
GPQA Diamond
Graduate-level reasoning
86.5%70.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.30 / 1M

各项指标谁更胜一筹?

At-a-glance decision framework
Coding
Claude Sonnet 4.5
+3.3% SWE-bench lead
Cost
Gemini 3.1 Pro
1.3x cheaper monthly runtime
Speed
Gemini 3.1 Pro
+22% faster generation
Context
Gemini 3.1 Pro
1.05M tokens window

权威基准测试得分对比

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

Gemini 3.1 ProClaude Sonnet 4.5
SWE-bench Verified (Autonomous Coding)

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

74.5%vs77.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

86.5%vs70.2%
MMLU-Pro (Multi-task Language Understanding)

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

89.2%vs84.5%
LMSYS Chatbot Arena Elo

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

1475ptsvs1355pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs10.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs50.1%
SciCode (Scientific Computing)

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

N/Avs43.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs72.6%
AIME (Advanced Math Competition)

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

94%vs84.7%
Long-Context Reasoning

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

N/Avs74.8%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs90 tok/s

延迟与响应速度

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 3.1 Pro0.78s
Claude Sonnet 4.50.90s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.1 Pro5.33s
Claude Sonnet 4.56.46s

实时 Token 成本模拟器

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini 3.1 Pro Monthly API Bill
$14.55 / month
$2/1M in · $12/1M out
Claude Sonnet 4.5 Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
Gemini 3.1 Pro saves approximately $3.90 / month (21%) under this workload.1.3x Savings

Gemini 3.1 Pro · Strengths & Key Advantages

  • 提供 1.0M 上下文窗口与最高 66K 输出容量,核心基准表现优异(SWE-bench Verified 74.5% · GPQA Diamond 86.5%)
  • 高达 110 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 780 毫秒
  • 极具竞争力的 API 定价(每百万 Token $2 输入 / $12 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在大规模高频调用场景下,建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本

Claude Sonnet 4.5 · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 64K 输出容量,核心基准表现优异(SWE-bench Verified 77.8% · GPQA Diamond 70.2%)
  • 高达 90 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 900 毫秒
  • 极具竞争力的 API 定价(每百万 Token $3 输入 / $15 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在大规模高频调用场景下,建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本

Token Use & Task Economics

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

MetricGemini 3.1 ProClaude Sonnet 4.5
Cost per Task
Estimated USD to finish one task
$1.85$2.10
Output Tokens / Task
Tokens generated per task
52K55K
Reasoning Tokens / Task
Thinking tokens burned per task
26K30K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3.1 ProClaude Sonnet 4.5
Official API Model ID (SDK)models/gemini-3-1-proclaude-sonnet-4-5-20251101
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-022025-11
Knowledge CutoffNovember 2025March 2025
ArchitecturePathways High-Capacity Reasoning ArchitectureHybrid Reasoning
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.5/1M with configurable TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedAnthropic 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 TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audio, videotext, vision
Output Modalitiestexttext

Gemini 3.1 Pro vs Claude Sonnet 4.5: 常见问题解答 (FAQ)

Which is more intelligent, Gemini 3.1 Pro or Claude Sonnet 4.5?

Claude Sonnet 4.5 is more intelligent. It scores 91 compared with 86 on the composite Intelligence Index.

Which model is faster?

Gemini 3.1 Pro is faster, generating 110 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Gemini 3.1 Pro is cheaper at $1.95 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.1 Pro has lower latency, with a time to first token of 0.78s compared with 0.90s.

Which model has a larger context window?

Gemini 3.1 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.

综合结论

综合来看,Gemini 3.1 Pro(适合:专为自主软件工程、跨文件代码重构与复杂调试打造的旗舰编程任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)与 Claude Sonnet 4.5(适合:面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)各具优势:Claude Sonnet 4.5 在综合智能与编程基准测试中表现更强,Gemini 3.1 Pro 拥有更快的 Token 生成速度,而 Gemini 3.1 Pro 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面,Gemini 3.1 Pro 和 Claude Sonnet 4.5 哪个更强?+

在综合智能指数与编程基准评测中,Claude Sonnet 4.5 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Gemini 3.1 Pro 与 Claude Sonnet 4.5 的 API 价格哪个更便宜?+

Gemini 3.1 Pro 的 API 价格为输入 $2.00 / 输出 $12.00(每百万 Token),Claude Sonnet 4.5 为输入 $3.00 / 输出 $15.00。综合混合成本来看,Gemini 3.1 Pro 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度?+

Gemini 3.1 Pro 支持 1.05M Token 上下文窗口,而 Claude Sonnet 4.5 支持 1M Token。在流式生成速度方面,Gemini 3.1 Pro 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

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

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

数据来源: Google 与 Anthropic 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。