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DeepSeek: R1 vs OpenAI: o3 vs Anthropic: Claude Opus 4.6: Coding Performance with 10 Evaluators

This comparison analyzes the Coding Performance with 10 Evaluators for DeepSeek: R1, OpenAI: o3, and Anthropic: Claude Opus 4.6 to determine the top performer.

DeepSeek: R1

1.7

/ 10

vs

OpenAI: o3

5.3

/ 10

Key Findings

Top PerformanceAnthropic: Claude Opus 4.6

Ranked #1 with an overall score of 8.08 in coding accuracy.

ConsistencyOpenAI: o3

Maintained a strong second place with 5.26 in overall coding score.

Output VolumeDeepSeek: R1

Generated the highest average completion tokens at 2,712.

Specifications

SpecDeepSeek: R1OpenAI: o3
Providerdeepseekopenai
Context Length64K200K
Input Price (per 1M tokens)$0.70$2.00
Output Price (per 1M tokens)$2.50$8.00
Max Output Tokens16,000100,000
Tierstandardadvanced

Our Verdict

Anthropic: Claude Opus 4.6 is the definitive winner for coding accuracy, significantly outperforming competitors in this specific PeerLM evaluation. OpenAI: o3 offers a balanced, reliable performance, while DeepSeek: R1 serves as a distinct alternative for high-volume output tasks.

Overview

In this technical breakdown, we evaluate the coding capabilities of three industry-leading models: DeepSeek: R1, OpenAI: o3, and Anthropic: Claude Opus 4.6. PeerLM conducted a rigorous assessment focusing on Coding Performance with 10 Evaluators to determine how these models handle complex programming tasks, instruction adherence, and overall accuracy.

Benchmark Results

The evaluation utilized a comparative ranking methodology, where 10 independent evaluators assessed the models based on their ability to execute code and follow instructions. The results reveal a clear hierarchy in model performance for this specific coding suite.

ModelOverall ScoreRank
Anthropic: Claude Opus 4.68.081
OpenAI: o35.262
DeepSeek: R11.673

Side-by-side Results

When looking at the DeepSeek: R1 vs OpenAI: o3 vs Anthropic: Claude Opus 4.6 comparison, the performance spread is significant. Anthropic: Claude Opus 4.6 emerged as the clear leader, securing an overall score of 8.08. OpenAI: o3 maintained a solid middle ground with a score of 5.26, while DeepSeek: R1 trailed in this specific coding evaluation with a score of 1.67.

Performance Breakdown

  • Anthropic: Claude Opus 4.6: Demonstrated superior logical reasoning and precision in code generation, consistently outperforming the cohort in accuracy and instruction following.
  • OpenAI: o3: Showed strong reliability, serving as a balanced option for high-level coding tasks, though it fell short of the top-tier performance exhibited by Opus 4.6 in this specific suite.
  • DeepSeek: R1: While highly efficient in terms of token usage, the model struggled to match the accuracy benchmarks set by its competitors in this specific evaluator set.

Cost & Latency

Efficiency is a critical factor for developers integrating LLMs into automated pipelines. The following table breaks down the cost profile for the models tested:

ModelAvg Completion TokensTotal Cost (USD)
Anthropic: Claude Opus 4.6360$0.040785
OpenAI: o3772$0.026432
DeepSeek: R12712$0.027719

It is noteworthy that DeepSeek: R1 generates significantly longer completion outputs compared to the other models, which impacts its cost-per-token profile despite its lower rank in the accuracy metrics for this coding suite.

Use Cases

For developers requiring the absolute highest level of coding accuracy, Anthropic: Claude Opus 4.6 is the recommended choice. OpenAI: o3 provides a highly capable middle-ground for production environments requiring a balance of cost and performance. DeepSeek: R1, with its high output volume, may find utility in tasks where verbose reasoning or explanatory documentation generation is prioritized over strict coding accuracy.

Verdict

The analysis of DeepSeek: R1 vs OpenAI: o3 vs Anthropic: Claude Opus 4.6 confirms that Anthropic: Claude Opus 4.6 is the current leader for high-stakes coding tasks. While OpenAI: o3 remains a formidable competitor, users must weigh the specific accuracy requirements of their project against the cost and output characteristics of these three models.

Backed by real data

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Methodology

Evaluated using PeerLM's blind evaluation pipeline with 4 responses per model across 2 criteria.