Head-to-Head Model Comparison

Claude Opus 4.6 vs Claude Sonnet 4.6

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Reviewed by the GetInfoUs research teamLast reviewed: October 2026Editorial policy
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Claude Opus 4.6 vs Claude Sonnet 4.6: Benchmarks, Pricing & Speed Comparison

Claude Opus 4.6 by Anthropic and Claude Sonnet 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, Claude Sonnet 4.6 holds a notable pricing advantage for token ingestion. On output generation, Claude Sonnet 4.6 is more economical per million generated tokens. 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 Sonnet 4.6 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.

Head-to-Head Comparison

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Popular Matchups:
Anthropic

Claude Opus 4.6

Expert Reasoning

Anthropic's early-2026 frontier breakthrough expanding the Opus reasoning family to a massive 1-million-token context window with deep multi-step planning.

Intelligence Index65 / 100
Input Token:

$5.00 / 1M

Output Token:

$25.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

78.4%

Anthropic

Claude Sonnet 4.6

Production Workhorse

The daily driver enterprise champion of early 2026, pairing a 1-million-token context window with rapid throughput and rock-solid tool calling.

Intelligence Index60 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.1%

Key Differences at a Glance

  • Claude Sonnet 4.6 is 1.7x cheaper for API usage (input + output tokens combined).
  • Claude Sonnet 4.6 is faster at 92 tok/s vs 55 tok/s.
  • Claude Opus 4.6 leads on SWE-bench coding (78.4% vs 76.1%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude Opus 4.6Claude Sonnet 4.6
Intelligence Index
Composite reasoning & knowledge score (0-100)
6560
Provider
AnthropicAnthropic
Input Price
Cost per million input tokens
$5.00 / 1M$3.00 / 1M
Output Price
Cost per million output tokens
$25.00 / 1M$15.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$3.85 / 1M$2.31 / 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
55 tok/s92 tok/s
SWE-bench Verified
Real-world software engineering
78.4%76.1%
GPQA Diamond
Graduate-level reasoning
76.5%74%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.30 / 1M

Who Wins Where?

At-a-glance decision framework
Coding
Claude Opus 4.6
+2.3% SWE-bench lead
Cost
Claude Sonnet 4.6
1.7x cheaper monthly runtime
Speed
Claude Sonnet 4.6
+67% faster generation
Context
Tied
1M tokens window

Empirical Head-to-Head Benchmark Scores

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

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

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

78.4%vs76.1%
GPQA Diamond (Doctorate-Level Science & Math)

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

76.5%vs74%
MMLU-Pro (Multi-task Language Understanding)

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

88.2%vs86.7%
LMSYS Chatbot Arena Elo

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

1395ptsvs1380pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

51.4%vs47.8%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

55 tok/s vs92 tok/s

Latency & Response Time

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 4.61.90s
Claude Sonnet 4.60.65s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Opus 4.610.99s
Claude Sonnet 4.66.08s

Real-Time Interactive Token Cost Simulator

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 4.6 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
Claude Sonnet 4.6 Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
Claude Sonnet 4.6 saves approximately $12.30 / month (40%) under this workload.1.7x Savings

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

Claude Sonnet 4.6 · Strengths & Key Advantages

  • Balanced $3/$15 economics combined with a full 1M token context window
  • Exceptional 92 tokens/sec throughput for interactive developer environments
  • Precise structured JSON output compliance and computer-use agent execution
Considerations & Trade-offs
  • Slightly behind Opus 4.6 on highly abstract mathematical research tasks
  • Requires careful prompt caching configuration to optimize 1M token ingestion costs

Token Use & Task Economics

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

MetricClaude Opus 4.6Claude Sonnet 4.6
Cost per Task
Estimated USD to finish one task
$5.49$3.29
Output Tokens / Task
Tokens generated per task
72K72K
Reasoning Tokens / Task
Thinking tokens burned per task
37K37K

Technical Specifications

Architecture, release timeline, capabilities, and modalities.

FeatureClaude Opus 4.6Claude Sonnet 4.6
Official API Model ID (SDK)claude-opus-4-6-20260201claude-sonnet-4-6-20260201
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-022026-02
Knowledge CutoffDecember 2025December 2025
ArchitectureMegacontext Frontier Reasoning TransformerHigh-Throughput Multimodal Agent 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$0.3/1M 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 & 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 4.6 vs Claude Sonnet 4.6: Quick Answers

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

Claude Opus 4.6 is more intelligent. It scores 65 compared with 60 on the composite Intelligence Index.

Which model is faster?

Claude Sonnet 4.6 is faster, generating 92 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

Claude Sonnet 4.6 is cheaper at $2.31 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Sonnet 4.6 has lower latency, with a time to first token of 0.65s compared with 1.90s.

Verdict

Verdict & Model Selection Recommendation: Choose Claude Opus 4.6 if your priority is Large repository code refactoring, full-book technical translation, and enterprise legal discovery. Its performance in demanding tasks justifies its operational token cost. Choose Claude Sonnet 4.6 if your workflow prioritizes Production coding copilots, full-repo code search, customer agent workflows, and automated workflows. 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 4.6 compare to Claude Sonnet 4.6 in API token pricing and cost per token?+

Claude Opus 4.6 costs $5.00 per 1M input tokens and $25.00 per 1M output tokens, whereas Claude Sonnet 4.6 costs $3.00 input and $15.00 output. Overall, Claude Sonnet 4.6 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

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

Both Claude Opus 4.6 and Claude Sonnet 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 4.6 or Claude Sonnet 4.6?+

Claude Opus 4.6 scores 78.4% on SWE-bench Verified while Claude Sonnet 4.6 scores 76.1%. For autonomous software engineering and multi-file debugging, Claude Opus 4.6 holds the verified benchmark lead.

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

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

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

Claude Opus 4.6 generates approximately 55 tokens per second, while Claude Sonnet 4.6 delivers 92 tokens per second. In latency-sensitive production environments, Claude Sonnet 4.6 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

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

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude Sonnet 4.6 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.6. This tiered routing pattern cuts overall API bills by up to 70%.

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.

Sources: GetInfoUs Independent AI Benchmarks & Official API Specs