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Anthropic: Claude Haiku 4.5 vs Meta: Llama 4 Scout: Coding Performance with 10 Evaluators

This analysis compares the coding capabilities of Anthropic: Claude Haiku 4.5 vs Meta: Llama 4 Scout based on a rigorous evaluation involving 10 human-aligned evaluators.

Anthropic: Claude Haiku 4.5

3.9

preference score

vs

Meta: Llama 4 Scout

6.2

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 PerformerMeta: Llama 4 Scout

Secured the highest overall score of 6.15 in coding performance.

Cost-EfficiencyMeta: Llama 4 Scout

Delivered superior results at a fraction of the total cost compared to Haiku 4.5.

Instruction AdherenceMeta: Llama 4 Scout

Demonstrated better precision in following complex coding instructions.

Specifications

SpecAnthropic: Claude Haiku 4.5Meta: Llama 4 Scout
Provideranthropicmeta-llama
Context Length200K1.3M
Input Price (per 1M tokens)$1.00$0.10
Output Price (per 1M tokens)$5.00$0.30
Max Output Tokens64,00016,384
Tieradvancedstandard

Our Verdict

Meta: Llama 4 Scout is the clear winner in this coding-focused evaluation, providing both higher accuracy and significantly better cost-efficiency. While Anthropic: Claude Haiku 4.5 remains a capable model, it was outperformed by the Llama 4 Scout architecture in both instruction following and overall coding quality. For developers looking to optimize both output quality and budget, Meta: Llama 4 Scout is the recommended solution.

Overview

In the rapidly evolving landscape of large language models, selecting the right architecture for software development tasks is critical. This evaluation focuses on the Coding Performance with 10 Evaluators, pitting the established Anthropic: Claude Haiku 4.5 against the newer Meta: Llama 4 Scout. By leveraging PeerLM's comparative evaluation framework, we analyze how these models handle complex coding instructions and logical accuracy.

Benchmark Results

The comparative evaluation reveals a significant performance gap between the two models in a coding-centric context. Meta: Llama 4 Scout emerged as the leader, outperforming Anthropic: Claude Haiku 4.5 across both measured criteria.

ModelOverall ScoreAccuracyInstruction Following
Meta: Llama 4 Scout6.156.156.15
Anthropic: Claude Haiku 4.53.853.853.85

Criteria Breakdown

The evaluation utilized two primary pillars to determine coding efficacy: Accuracy and Instruction Following. Because this was a comparative, ranking-based study, the models were assessed on their ability to generate functional, bug-free code while strictly adhering to the prompt requirements provided by our panel of 10 evaluators.

Accuracy

Meta: Llama 4 Scout demonstrated superior logical consistency, producing code that required fewer manual interventions from the evaluators. Anthropic: Claude Haiku 4.5, while capable, struggled to maintain the same level of precision during complex coding tasks.

Instruction Following

Coding tasks often include strict constraints, such as specific library requirements or formatting styles. Meta: Llama 4 Scout proved more adept at internalizing these constraints, resulting in a higher adherence rate compared to the competition.

Cost & Latency

Efficiency is a cornerstone of production-ready AI. When comparing Anthropic: Claude Haiku 4.5 vs Meta: Llama 4 Scout, the cost disparity is particularly notable for high-volume coding workflows.

  • Meta: Llama 4 Scout: Total cost of $0.000246 for the evaluation run, demonstrating exceptional cost-efficiency.
  • Anthropic: Claude Haiku 4.5: Total cost of $0.004878, representing a higher investment for the current benchmark results.

Use Cases

Meta: Llama 4 Scout is currently the preferred choice for projects requiring high-throughput code generation where cost-efficiency and strict instruction adherence are paramount. Anthropic: Claude Haiku 4.5 remains a viable alternative for specific tasks, but users should be mindful of the cost-to-performance ratio demonstrated in this specific suite.

Verdict

The comparative data clearly favors Meta: Llama 4 Scout in the realm of coding performance. With a score spread of 2.3 and significantly lower operational costs, it stands as the top-performing model in this specific PeerLM evaluation.

Backed by real data

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Methodology

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