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OpenAI: GPT-5.5 vs DeepSeek: DeepSeek V4 Pro: Coding Performance with 10 Evaluators

In our latest Coding Performance with 10 Evaluators benchmark, we put OpenAI: GPT-5.5 and DeepSeek: DeepSeek V4 Pro head-to-head to determine the superior coding assistant.

OpenAI: GPT-5.5

6.8

/ 10

vs

DeepSeek: DeepSeek V4 Pro

6.3

/ 10

Key Findings

Top PerformanceOpenAI: GPT-5.5

GPT-5.5 achieved the highest overall score of 6.84 in our coding benchmarks.

Best ValueDeepSeek: DeepSeek V4 Pro

DeepSeek V4 Pro provides a significantly more cost-effective solution for high-volume tasks.

Latency LeaderOpenAI: GPT-5.5

With an average latency of 1215ms, GPT-5.5 is nearly 3.5x faster than DeepSeek V4 Pro.

Specifications

SpecOpenAI: GPT-5.5DeepSeek: DeepSeek V4 Pro
Provideropenaideepseek
Context Length1.1M1.0M
Input Price (per 1M tokens)$5.00$0.44
Output Price (per 1M tokens)$30.00$0.87
Max Output Tokens128,000384,000
Tierfrontierstandard

Our Verdict

OpenAI: GPT-5.5 emerges as the top-performing model for coding tasks, offering superior accuracy and significantly lower latency. Conversely, DeepSeek: DeepSeek V4 Pro stands out as the best value option, providing strong performance for a fraction of the cost. The choice depends on whether your priority is maximum coding precision or operational cost-efficiency.

Overview

As LLMs continue to evolve, selecting the right model for software development tasks requires more than just hype; it requires rigorous, data-driven evaluation. In this PeerLM analysis, we examine the OpenAI: GPT-5.5 vs DeepSeek: DeepSeek V4 Pro comparison through the lens of Coding Performance with 10 Evaluators. By utilizing a panel of 10 human-aligned evaluators, we have ranked these models based on their ability to generate accurate, functional, and instruction-compliant code.

Benchmark Results

The evaluation indicates a clear performance leader in terms of raw coding capability, while the value proposition offers a compelling alternative for cost-conscious developers.

Model Overall Score Accuracy Instruction Following
OpenAI: GPT-5.5 6.84 6.84 6.84
DeepSeek: DeepSeek V4 Pro 6.32 6.32 6.32

Criteria Breakdown

Our evaluation focused on two primary pillars: Accuracy and Instruction Following. In the context of Coding Performance with 10 Evaluators, these metrics represent the model's ability to produce bug-free logic while adhering to complex prompt constraints such as library usage, architectural patterns, and specific coding styles.

  • Accuracy: OpenAI: GPT-5.5 secured the top spot with a score of 6.84, demonstrating a higher rate of syntactically correct and logically sound code generation.
  • Instruction Following: Both models showed alignment in their ability to follow prompts; however, GPT-5.5 consistently outperformed DeepSeek V4 Pro in navigating nuanced constraints typical of professional engineering environments.

Cost & Latency

Beyond raw performance, operational efficiency is critical for scaling AI-driven development tools. The following table highlights the latency and cost footprint observed during our benchmark runs.

Model Avg Latency (ms) Cost per Output Token
OpenAI: GPT-5.5 1215 $0.03487
DeepSeek: DeepSeek V4 Pro 4162 $0.001042

Use Cases

When to choose OpenAI: GPT-5.5

With its superior overall score and significantly lower latency (1215ms), GPT-5.5 is the ideal candidate for real-time coding assistants, IDE plugins, and high-complexity refactoring tasks where speed and accuracy are non-negotiable.

When to choose DeepSeek: DeepSeek V4 Pro

DeepSeek V4 Pro is an exceptional choice for batch processing, documentation generation, and non-latency-sensitive coding tasks. Its cost profile is significantly more efficient, making it a powerful tool for high-volume environments where cost-per-token is a primary factor.

Verdict

The OpenAI: GPT-5.5 vs DeepSeek: DeepSeek V4 Pro comparison reveals a trade-off between premium performance and high-value efficiency. While GPT-5.5 is the definitive winner for coding precision, DeepSeek V4 Pro offers a highly competitive option for those looking to optimize their infrastructure spend.

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.