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OpenAI: GPT-5.2 vs DeepSeek: DeepSeek V3.2: Coding Performance with 10 Evaluators

We evaluate OpenAI: GPT-5.2 vs DeepSeek: DeepSeek V3.2 through the lens of Coding Performance with 10 Evaluators to determine the current leader in code generation.

OpenAI: GPT-5.2

4.6

/ 10

vs

DeepSeek: DeepSeek V3.2

5.4

/ 10

Key Findings

Top PerformerDeepSeek: DeepSeek V3.2

Ranked #1 in the Coding Performance with 10 Evaluators suite.

Cost EfficiencyDeepSeek: DeepSeek V3.2

Achieved significantly lower cost per output token compared to GPT-5.2.

Overall ScoreDeepSeek: DeepSeek V3.2

Outscored GPT-5.2 by a margin of 0.76 in comparative rankings.

Specifications

SpecOpenAI: GPT-5.2DeepSeek: DeepSeek V3.2
Provideropenaideepseek
Context Length400K164K
Input Price (per 1M tokens)$1.75$0.26
Output Price (per 1M tokens)$14.00$0.38
Tieradvancedstandard

Our Verdict

DeepSeek: DeepSeek V3.2 is the clear winner in this evaluation, providing superior instruction following and accuracy for coding tasks while maintaining a much lower price point. While OpenAI: GPT-5.2 provides reliable results, the cost-to-performance ratio currently favors the DeepSeek architecture for development workflows.

Overview

In the rapidly evolving landscape of large language models, selecting the right architecture for software development tasks is critical. This analysis compares OpenAI: GPT-5.2 vs DeepSeek: DeepSeek V3.2, focusing specifically on their Coding Performance with 10 Evaluators. By utilizing PeerLM's comparative evaluation framework, we move beyond static benchmarks to understand how these models perform when scrutinized by human-in-the-loop expert reviewers.

Benchmark Results

The comparative evaluation highlights a distinct leader in coding tasks. DeepSeek: DeepSeek V3.2 secured the top rank, outperforming the competition across both Accuracy and Instruction Following criteria.

Model Overall Score Accuracy Instruction Following
DeepSeek: DeepSeek V3.2 5.38 5.38 5.38
OpenAI: GPT-5.2 4.62 4.62 4.62

Criteria Breakdown

The evaluation centered on two primary pillars: Accuracy and Instruction Following. In coding contexts, accuracy refers to the syntactical and logical correctness of the generated code blocks, while instruction following measures how well the model adheres to specific constraints, such as using a particular library or maintaining a specific project structure.

  • DeepSeek: DeepSeek V3.2 demonstrated superior alignment with evaluator expectations, achieving an overall score of 5.38.
  • OpenAI: GPT-5.2 followed with an overall score of 4.62, showing consistent performance but falling behind the leader in this specific comparative set.

Cost & Latency

Beyond raw performance, cost-efficiency is a major factor for enterprise-scale deployments. The data reveals a significant disparity in resource consumption between the two models.

Model Total Cost (USD) Cost/Output Token
DeepSeek: DeepSeek V3.2 $0.000447 $0.000764
OpenAI: GPT-5.2 $0.010465 $0.016352

DeepSeek: DeepSeek V3.2 offers a significantly more cost-effective profile, making it an attractive choice for high-volume coding automation tasks.

Use Cases

Given the results of this Coding Performance with 10 Evaluators review, DeepSeek: DeepSeek V3.2 is ideally suited for high-throughput coding environments where cost-efficiency and high-accuracy code generation are paramount. OpenAI: GPT-5.2 remains a robust option, though users should weigh its performance metrics against the higher operational costs per token.

Verdict

The comparative analysis between OpenAI: GPT-5.2 vs DeepSeek: DeepSeek V3.2 confirms that DeepSeek currently holds the edge in coding-specific tasks. With a higher overall score and significantly lower cost, it represents the superior choice for developers prioritizing efficiency and performance.

Backed by real data

View the Full Evaluation Report

See every response, score, and evaluator judgment behind this comparison. All data from PeerLM's blind evaluation pipeline.

View Report

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Methodology

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