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Claude Opus 4.6 vs DeepSeek R1

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Claude Opus 4.6 and DeepSeek R1.

Anthropic

Claude Opus 4.6

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...

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DeepSeek

DeepSeek R1

A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.

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Technical Specifications

SpecificationClaude Opus 4.6DeepSeek R1
ProviderAnthropicDeepSeek
Context Window1,000,000 tokens163,840 tokens
Agent Suitability95/10078/100
Time to First Token (TTFT)500 ms1800 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-02-042025-01-20

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4.6

$5.00

DeepSeek R1

$0.70

Output Price per Million Tokens

Claude Opus 4.6

$25.00

DeepSeek R1

$2.50

Want to test both models live?

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Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9250.0%vs9080.0%
Claude Opus 4.6
DeepSeek R1
HumanEvalPython coding & logic synthesis
9450.0%vs9280.0%
Claude Opus 4.6
DeepSeek R1
MATHComplex mathematical problem solving
8890.0%vs9310.0%
Claude Opus 4.6
DeepSeek R1
GPQAGraduate-level expert reasoning
7980.0%vs6210.0%
Claude Opus 4.6
DeepSeek R1
HellaSwagCommonsense reasoning and inference
9750.0%vs9050.0%
Claude Opus 4.6
DeepSeek R1
MT-BenchMulti-turn conversation flow quality
965.0%vs935.0%
Claude Opus 4.6
DeepSeek R1

Claude Opus 4.6 Quirks & Gotchas

  • Best for long-context document analysis and legal review
  • Tool calling requires structured prompt — prone to verbose refusal without explicit output schema

DeepSeek R1 Quirks & Gotchas

  • Reasoning model — not designed for high-frequency tool calling
  • Pair with a smaller model (V4 Flash) for routing and use R1 for complex reasoning only