Claude Opus 4.7 vs Claude Opus 4.6
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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.
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Claude Opus 4.7
Advanced SoftwareAn enhanced software engineering model built specifically for mission-critical industrial architectures, formal verification, and high-stakes reasoning.
$5.00 / 1M
$25.00 / 1M
1M tokens
79.5%
Claude Opus 4.6
Expert ReasoningAnthropic's early-2026 frontier breakthrough expanding the Opus reasoning family to a massive 1-million-token context window with deep multi-step planning.
$5.00 / 1M
$25.00 / 1M
1M tokens
78.4%
Key Differences at a Glance
- 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%).
| Feature | Claude Opus 4.7 | Claude Opus 4.6 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 65 |
Provider | Anthropic | Anthropic |
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 text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 58 tok/s | 55 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 |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Opus 4.7 · Strengths & Key Advantages
- Industry-leading 79.5% SWE-bench Verified performance in early 2026
- Enhanced formal verification capabilities for mission-critical aerospace and finance software
- Resilient multi-turn error recovery in autonomous coding agents
Considerations & Trade-offs
- Premium output token pricing ($25/1M) requires thoughtful cost governance
- Extended reasoning overhead during complex dependency resolution
Claude Opus 4.6 · Strengths & Key Advantages
- Full 1,000,000 token context window coupled with 128K max output generation
- Major price reduction from $15/$75 down to $5/$25 per million tokens
- Flawless multi-document synthesis and long-horizon software engineering
Considerations & Trade-offs
- Extended thinking cycles introduce notable initial latency (up to ~2s TTFT)
- Output pricing remains substantial for mass consumer query volume
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Opus 4.7 | Claude Opus 4.6 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $5.49 | $5.49 |
Output Tokens / Task Tokens generated per task | 72K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 37K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Opus 4.7 | Claude Opus 4.6 |
|---|---|---|
| Official API Model ID (SDK) | claude-opus-4-7-20260401 | claude-opus-4-6-20260201 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-04 | 2026-02 |
| Knowledge Cutoff | February 2026 | December 2025 |
| Architecture | Adaptive Enterprise Reasoning Transformer | Megacontext Frontier Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM | 4,000 – 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 tokens | Standard 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 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text 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 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 | text, vision |
| Output Modalities | text | text |
Claude Opus 4.7 vs Claude Opus 4.6: Quick Answers
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.
Verdict
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.
Frequently asked questions
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.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs