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OpenAI: GPT-5.4 Mini vs Meta: Llama 4 Scout: Coding Performance with 10 Evaluators

In our latest Coding Performance with 10 Evaluators benchmark, we compare OpenAI: GPT-5.4 Mini and Meta: Llama 4 Scout to determine the top performer for developer tasks.

OpenAI: GPT-5.4 Mini

7.6

preference score

vs

Meta: Llama 4 Scout

2.4

preference score

Judges ranked the responses in this Run against each other; the rank is mapped onto a 0–10 scale. It shows which response was preferred, not how good either one is — and it is not a percentage, a pass rate, or a check that the output was correct.

Sample size for this comparison was not recorded. Treat it as directional.

Evidence clarification: this article predates recorded sample provenance. Treat its conclusions as claims about the displayed examples; they do not establish general model superiority, verified correctness, or production suitability.

Key Findings

Top PerformerOpenAI: GPT-5.4 Mini

Achieved a significantly higher overall score of 7.57 in coding tasks.

Cost-EfficiencyMeta: Llama 4 Scout

Offers a much lower cost per output token at $0.000421 compared to the competition.

Instruction FollowingOpenAI: GPT-5.4 Mini

Demonstrated superior capability in executing complex developer instructions.

Specifications

SpecOpenAI: GPT-5.4 MiniMeta: Llama 4 Scout
Provideropenaimeta-llama
Context Length400K1.3M
Input Price (per 1M tokens)$0.75$0.10
Output Price (per 1M tokens)$4.50$0.30
Max Output Tokens128,00016,384
Tieradvancedstandard

Our Verdict

OpenAI: GPT-5.4 Mini is the clear winner for coding tasks requiring high accuracy and strict instruction adherence. While Meta: Llama 4 Scout is significantly more cost-effective, it currently lacks the precision required for complex coding benchmarks. Developers prioritizing quality output should opt for GPT-5.4 Mini, while cost-sensitive projects might find value in Llama 4 Scout.

Overview

As the landscape of LLMs evolves, selecting the right model for coding tasks requires rigorous, empirical data. In this PeerLM evaluation, we examine the OpenAI: GPT-5.4 Mini vs Meta: Llama 4 Scout in a specialized suite focused on Coding Performance with 10 Evaluators. This comparative analysis highlights how these models handle complex instruction following and code accuracy under professional review.

Benchmark Results

The evaluation was conducted using a rigorous comparative ranking methodology. Each model was tested across identical prompts to ensure a fair assessment of their coding capabilities.

ModelOverall ScoreAccuracyInstruction Following
OpenAI: GPT-5.4 Mini7.577.577.57
Meta: Llama 4 Scout2.432.432.43

Criteria Breakdown

Our assessment focused on two primary pillars: Accuracy and Instruction Following. The OpenAI: GPT-5.4 Mini demonstrated a significant lead in both categories, consistently producing code that adhered to requested patterns and functional specifications. Meta: Llama 4 Scout, while highly efficient, struggled to meet the high bar set by the evaluators for complex coding requirements within this specific test suite.

Cost & Latency

Understanding the economic and performance trade-offs is vital for production deployments. Below is a breakdown of the cost and latency metrics recorded during the execution of this suite.

  • OpenAI: GPT-5.4 Mini: Cost per output token is $0.005501, with a total cost of $0.003548 for the run.
  • Meta: Llama 4 Scout: Significantly more economical at $0.000421 per output token, with a total run cost of $0.000246 and an average latency of 231ms.

Use Cases

The OpenAI: GPT-5.4 Mini is currently the preferred choice for high-stakes coding environments where accuracy is paramount and error-correction costs are high. Conversely, the Meta: Llama 4 Scout offers a compelling value proposition for lightweight, high-volume tasks where latency and cost-efficiency are prioritized over peak reasoning capabilities.

Verdict

The OpenAI: GPT-5.4 Mini vs Meta: Llama 4 Scout comparison reveals a clear performance gap in coding tasks. While Meta: Llama 4 Scout provides superior cost-efficiency, OpenAI: GPT-5.4 Mini delivers the reliability and accuracy required for professional software development workflows.

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.

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Methodology

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