Head-to-Head Model Comparison

Claude Fable 5.1 vs Gemini 3 Pro

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
Claude Fable 5.1 vs Gemini 3 Pro: Benchmarks, Pricing & Speed Comparison

Anthropic 的 Claude Fable 5.1 与 Google 的 Gemini 3 Pro 是当前备受关注的两款主力 AI 模型。Claude Fable 5.1 提供 1M Token 上下文窗口,API 定价为每百万 Token 输入 $10.00 / 输出 $50.00;而 Gemini 3 Pro 提供 2M Token 上下文窗口,定价为每百万 Token 输入 $2.00 / 输出 $12.00。以下为两者的基准测试、延迟与成本全面对比。

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Anthropic

Claude Fable 5.1

旗舰前沿大模型

Anthropic 推出的 Claude Fable 5.1 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、65 Token/秒生成速度与 82/100 综合智能指数(每百万 Token $10 输入 / $50 输出)。

Intelligence Index82 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

83.8%

Google

Gemini 3 Pro

旗舰前沿大模型

Google 推出的 Gemini 3 Pro 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 2M 上下文窗口、118 Token/秒生成速度与 62/100 综合智能指数(每百万 Token $2 输入 / $12 输出)。

Intelligence Index62 / 100
Input Token:

$2.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

76.2%

核心差异一览

  • Gemini 3 Pro is 4.3x cheaper for API usage (input + output tokens combined).
  • Gemini 3 Pro has a larger context window (2M vs 1M tokens).
  • Gemini 3 Pro is faster at 118 tok/s vs 65 tok/s.
  • Claude Fable 5.1 leads on SWE-bench coding (83.8% vs 76.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude Fable 5.1Gemini 3 Pro
Intelligence Index
Composite reasoning & knowledge score (0-100)
8262
Provider
AnthropicGoogle
Input Price
Cost per million input tokens
$10.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$50.00 / 1M$12.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$7.17 / 1M$1.74 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text2M tokens~3,000 A4 pages of text
Max Output Tokens
Per single request
128K66K
Throughput Speed
Tokens generated per second
65 tok/s118 tok/s
SWE-bench Verified
Real-world software engineering
83.8%76.2%
GPQA Diamond
Graduate-level reasoning
81.6%73.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.25 / 1M$0.20 / 1M

各项指标谁更胜一筹?

At-a-glance decision framework
Coding
Claude Fable 5.1
+7.6% SWE-bench lead
Cost
Gemini 3 Pro
4.3x cheaper monthly runtime
Speed
Gemini 3 Pro
+82% faster generation
Context
Gemini 3 Pro
2M tokens window

权威基准测试得分对比

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

Claude Fable 5.1Gemini 3 Pro
SWE-bench Verified (Autonomous Coding)

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

83.8%vs76.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

81.6%vs73.5%
MMLU-Pro (Multi-task Language Understanding)

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

91%vs86.8%
LMSYS Chatbot Arena Elo

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

1510ptsvs1384pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs12.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

62%vs47.9%
SciCode (Scientific Computing)

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

N/Avs44.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs73%
AIME (Advanced Math Competition)

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

N/Avs92.8%
Long-Context Reasoning

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

N/Avs84.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs118 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
Claude Fable 5.11.45s
Gemini 3 Pro1.80s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Fable 5.19.14s
Gemini 3 Pro6.04s

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)
Claude Fable 5.1 Monthly API Bill
$60.38 / month
$10/1M in · $50/1M out
Gemini 3 Pro Monthly API Bill
$14.10 / month
$2/1M in · $12/1M out
Gemini 3 Pro saves approximately $46.27 / month (77%) under this workload.4.3x Savings

Claude Fable 5.1 · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 128K 输出容量,核心基准表现优异(SWE-bench Verified 83.8% · GPQA Diamond 81.6%)
  • 高达 65 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 1450 毫秒
  • 极具竞争力的 API 定价(每百万 Token $10 输入 / $50 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

Gemini 3 Pro · Strengths & Key Advantages

  • 提供 2M 上下文窗口与最高 66K 输出容量,核心基准表现优异(SWE-bench Verified 76.2% · GPQA Diamond 73.5%)
  • 高达 118 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 1800 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

Token Use & Task Economics

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

MetricClaude Fable 5.1Gemini 3 Pro
Cost per Task
Estimated USD to finish one task
$10.97$2.40
Output Tokens / Task
Tokens generated per task
72K60K
Reasoning Tokens / Task
Thinking tokens burned per task
37K31K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureClaude Fable 5.1Gemini 3 Pro
Official API Model ID (SDK)claude-fable-5-1-20260901models/gemini-3-pro
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-092025-11
Knowledge CutoffJune 2026January 2025
ArchitectureFrontier Auditable Multimodal TransformerSparse Multimodal MoE
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 & TTL$0.25/1M cached read (90% off); 5-min default & 1-hour extended TTLImplicit + Explicit Context Caching at $0.2/1M with configurable 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 & SandboxNative Web Search Citations + Code Execution Tool + MCP ConnectorsBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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 ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedGoogle DeepMind Frontier Safety Framework Verified
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, visiontext, vision, audio, video
Output Modalitiestexttext

Claude Fable 5.1 vs Gemini 3 Pro: 常见问题解答 (FAQ)

Which is more intelligent, Claude Fable 5.1 or Gemini 3 Pro?

Claude Fable 5.1 is more intelligent. It scores 82 compared with 62 on the composite Intelligence Index.

Which model is faster?

Gemini 3 Pro is faster, generating 118 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Gemini 3 Pro is cheaper at $1.74 per 1M tokens blended, compared with $7.17 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Fable 5.1 has lower latency, with a time to first token of 1.45s compared with 1.80s.

Which model has a larger context window?

Gemini 3 Pro has a larger context window, supporting 2M tokens compared with 1M tokens.

综合结论

综合来看,Claude Fable 5.1(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。)与 Gemini 3 Pro(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 Fable 5.1 在综合智能与编程基准测试中表现更强,Gemini 3 Pro 拥有更快的 Token 生成速度,而 Gemini 3 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,Claude Fable 5.1 和 Gemini 3 Pro 哪个更强?+

在综合智能指数与编程基准评测中,Claude Fable 5.1 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。

Claude Fable 5.1 与 Gemini 3 Pro 的 API 价格哪个更便宜?+

Claude Fable 5.1 的 API 价格为输入 $10.00 / 输出 $50.00(每百万 Token),Gemini 3 Pro 为输入 $2.00 / 输出 $12.00。综合混合成本来看,Gemini 3 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?+

Claude Fable 5.1 支持 1M Token 上下文窗口,而 Gemini 3 Pro 支持 2M Token。在流式生成速度方面,Gemini 3 Pro 的吞吐表现更快。

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