Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25): The Ultimate Performance & Pricing Comparison

Deep dive into reasoning, benchmarks, and latency insights.

The Final Verdict in the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) Showdown

After a comprehensive analysis, Claude Opus 4.6 (Adaptive Reasoning) emerges as the superior choice for users prioritizing raw intelligence and reasoning. However, DeepSeek R1 0528 (May '25) remains a highly competitive option, especially for scenarios demanding faster response times and cost-efficiency. This detailed Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) comparison will break down every critical metric to help you make the best decision.

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Model Snapshot

Key decision metrics at a glance.

Claude Opus 4.6 (Adaptive Reasoning)

Anthropic

Reasoning: 6
Coding: 5
Multimodal: 4
Long Context: 7
Blended Price / 1M tokens: $0.010
P95 Latency: 1000ms
Tokens per second: 71.11tokens/sec

DeepSeek R1 0528 (May '25)

DeepSeek

Reasoning: 8
Coding: 2
Multimodal: 2
Long Context: 3
Blended Price / 1M tokens: $0.002
P95 Latency: 1000ms
Tokens per second: —

Overall Capabilities

The capability radar provides a holistic view of the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) matchup. This chart illustrates each model's strengths and weaknesses at a glance, forming a cornerstone of our Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) analysis.

This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `Claude Opus 4.6 (Adaptive Reasoning)` vs `DeepSeek R1 0528 (May '25)`.

Benchmark Breakdown

For a granular look, this chart directly compares scores across standardized benchmarks. In the critical MMLU Pro test, a key part of the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) debate, Claude Opus 4.6 (Adaptive Reasoning) scores 60 against DeepSeek R1 0528 (May '25)'s 80. This data-driven approach is essential for any serious Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) comparison.

This grouped bar chart provides a side-by-side comparison for each benchmark metric.

Speed & Latency

Speed is a crucial factor in the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) decision for interactive applications. The metrics below highlight the trade-offs you should weigh before shipping to production.

Time to First Token

Claude Opus 4.6 (Adaptive Reasoning)300ms

DeepSeek R1 0528 (May '25)300ms

Tokens per Second

Claude Opus 4.6 (Adaptive Reasoning)71.11

DeepSeek R1 0528 (May '25)25

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The Economics of Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25)

Power is only one part of the equation. This Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) pricing analysis gives you a true sense of value.

Pricing Breakdown

Compare input and output pricing at a glance.

Real-World Cost Scenario

To process 1M input tokens and generate 250k output tokens, Claude Opus 4.6 (Adaptive Reasoning) would cost $0.011, whereas DeepSeek R1 0528 (May '25) would cost $0.002. This practical calculation is vital for any developer considering the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) choice.

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Which Model Wins the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) Battle for You?

Choose Claude Opus 4.6 (Adaptive Reasoning) if...

Cost is a secondary concern to power in your Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) decision.

You need the most advanced reasoning capabilities available.

Your use case demands cutting-edge AI performance.

Choose DeepSeek R1 0528 (May '25) if...

You need a highly responsive model for user-facing applications.

Your budget is a primary consideration in the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) choice.

You are developing at scale where operational costs are critical.

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Your Questions about the Claude Opus 4.6 (Adaptive Reasoning) vs DeepSeek R1 0528 (May '25) Comparison

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Data source: https://artificialanalysis.ai/