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Head-to-Head Model Comparison

Claude Opus 4.7 vs Claude Opus 4.6

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 Opus 4.7 vs Claude Opus 4.6: Benchmarks, Pricing & Speed Comparison

Claude Opus 4.7 by Anthropic and Claude Opus 4.6 by Anthropic represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, Claude Opus 4.7 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

模型直接对比 (VS)

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Anthropic

Claude Opus 4.7

深度推理模型

Anthropic 推出的 Claude Opus 4.7 人工智能模型，具备 1M 上下文窗口，输入价格为 $5/百万 Token，输出价格为 $25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index66 / 100
Input Token:

$5.00 / 1M

Output Token:

$25.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

79.5%

Anthropic

Claude Opus 4.6

深度推理模型

Anthropic 推出的 Claude Opus 4.6 人工智能模型，具备 1M 上下文窗口，输入价格为 $5/百万 Token，输出价格为 $25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index65 / 100
Input Token:

$5.00 / 1M

Output Token:

$25.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

78.4%

核心差异一览

  • Both models cost the same under this workload — choose on capability, not price.
  • Claude Opus 4.7 is faster at 58 tok/s vs 55 tok/s.
  • Claude Opus 4.7 leads on SWE-bench coding (79.5% vs 78.4%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude Opus 4.7Claude Opus 4.6
Intelligence Index
Composite reasoning & knowledge score (0-100)
6665
Provider
AnthropicAnthropic
Input Price
Cost per million input tokens
$5.00 / 1M$5.00 / 1M
Output Price
Cost per million output tokens
$25.00 / 1M$25.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$3.85 / 1M$3.85 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
58 tok/s55 tok/s
SWE-bench Verified
Real-world software engineering
79.5%78.4%
GPQA Diamond
Graduate-level reasoning
77.8%76.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.50 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Claude Opus 4.7
+1.1% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Claude Opus 4.7
+5% faster generation
Context
Tied
1M tokens window

权威基准测试得分对比

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

Claude Opus 4.7Claude Opus 4.6
SWE-bench Verified (Autonomous Coding)

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

79.5%vs78.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

77.8%vs76.5%
MMLU-Pro (Multi-task Language Understanding)

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

89%vs88.2%
LMSYS Chatbot Arena Elo

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

1405ptsvs1395pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

52.8%vs51.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

58 tok/s vs55 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 Opus 4.71.85s
Claude Opus 4.61.90s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Opus 4.710.47s
Claude Opus 4.610.99s

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 Opus 4.7 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
Claude Opus 4.6 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
These models cost the same under this workload — no savings either way.Break-even

Claude Opus 4.7 · Strengths & Key Advantages

  • 由 Anthropic 研发的高精度 Adaptive Enterprise Reasoning Transformer 架构，支持 1M 上下文窗口
  • 极具竞争力的 API 定价（输入 $5/1M，输出 $25/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Claude Opus 4.6 · Strengths & Key Advantages

  • 由 Anthropic 研发的高精度 Megacontext Frontier Reasoning Transformer 架构，支持 1M 上下文窗口
  • 极具竞争力的 API 定价（输入 $5/1M，输出 $25/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Token Use & Task Economics

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

MetricClaude Opus 4.7Claude Opus 4.6
Cost per Task
Estimated USD to finish one task
$5.49$5.49
Output Tokens / Task
Tokens generated per task
72K72K
Reasoning Tokens / Task
Thinking tokens burned per task
37K37K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureClaude Opus 4.7Claude Opus 4.6
Official API Model ID (SDK)claude-opus-4-7-20260401claude-opus-4-6-20260201
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-042026-02
Knowledge CutoffFebruary 2026December 2025
ArchitectureAdaptive Enterprise Reasoning TransformerMegacontext Frontier Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 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 & TTL$0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL$0.5/1M 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 & SandboxNative Web Search Citations + Code Execution Tool + MCP ConnectorsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationCustom Model Tuning via Amazon Bedrock & Prompt 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)
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 EvaluatedAnthropic 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, visiontext, vision
Output Modalitiestexttext

Claude Opus 4.7 vs Claude Opus 4.6: 常见问题解答 (FAQ)

Which is more intelligent, Claude Opus 4.7 or Claude Opus 4.6?

Claude Opus 4.7 is more intelligent. It scores 66 compared with 65 on the composite Intelligence Index.

Which model is faster?

Claude Opus 4.7 is faster, generating 58 tokens per second compared with 55 tokens per second.

Which model has lower latency?

Claude Opus 4.7 has lower latency, with a time to first token of 1.85s compared with 1.90s.

综合结论

Verdict & Model Selection Recommendation: Choose Claude Opus 4.7 if your priority is Autonomous software testing suites, cybersecurity code analysis, and complex refactors. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.6 if your workflow prioritizes Large repository code refactoring, full-book technical translation, and enterprise legal discovery. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

常见问题 (FAQ)

How does Claude Opus 4.7 compare to Claude Opus 4.6 in API token pricing and cost per token?+

Both Claude Opus 4.7 and Claude Opus 4.6 share matched API token pricing at $5.00 per 1M input tokens and $25.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: Claude Opus 4.7 or Claude Opus 4.6?+

Both Claude Opus 4.7 and Claude Opus 4.6 offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: Claude Opus 4.7 or Claude Opus 4.6?+

Claude Opus 4.7 scores 79.5% on SWE-bench Verified while Claude Opus 4.6 scores 78.4%. For autonomous software engineering and multi-file debugging, Claude Opus 4.7 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between Claude Opus 4.7 and Claude Opus 4.6?+

Claude Opus 4.7 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.6 provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Claude Opus 4.7 and Claude Opus 4.6?+

Claude Opus 4.7 generates approximately 58 tokens per second, while Claude Opus 4.6 delivers 55 tokens per second. In latency-sensitive production environments, Claude Opus 4.7 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Claude Opus 4.7 and Claude Opus 4.6 to optimize costs?+

Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Claude Opus 4.7, and route deep architectural or complex reasoning queries to Claude Opus 4.7. This balanced routing strategy maximizes system throughput and answer quality.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: GetInfoUs Independent AI Benchmarks & Official API Specs