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OpenAI GPT-5.4 vs Anthropic Claude Opus 4.6: Performance and Cost Analysis

A comprehensive comparison of OpenAI's GPT-5.4 and Anthropic's Claude Opus 4.6, revealing significant performance differences and cost trade-offs.

OpenAI: GPT-5.4

2.6

/ 10

vs

Anthropic: Claude Opus 4.6

7.4

/ 10

Key Findings

Overall PerformanceAnthropic: Claude Opus 4.6

Scores 7.44 vs GPT-5.4's 2.56, a significant 4.88-point lead

Response SpeedAnthropic: Claude Opus 4.6

4.4x faster at 1,203ms vs GPT-5.4's 5,270ms latency

Cost EfficiencyOpenAI: GPT-5.4

75% lower cost per response at $0.002514 vs $0.010196

AccuracyAnthropic: Claude Opus 4.6

Achieves 7.44 accuracy score vs GPT-5.4's 2.56

Instruction FollowingAnthropic: Claude Opus 4.6

Perfect 7.44 score compared to GPT-5.4's 2.56

Specifications

SpecOpenAI: GPT-5.4Anthropic: Claude Opus 4.6
Provideropenaianthropic
Context Length1.1M1.0M
Input Price (per 1M tokens)$2.50$5.00
Output Price (per 1M tokens)$15.00$25.00
Max Output Tokens128,000128,000
Tieradvancedadvanced

Our Verdict

Anthropic's Claude Opus 4.6 dominates this comparison with superior performance across all metrics while delivering significantly faster response times. Despite GPT-5.4's cost advantage, the substantial performance gap and latency difference make Claude Opus 4.6 the better choice for most applications requiring quality and speed. GPT-5.4 remains viable only for budget-constrained scenarios where performance requirements are minimal.

Overview

The OpenAI: GPT-5.4 vs Anthropic: Claude Opus 4.6 comparison reveals a clear performance hierarchy in our PeerLM evaluation. Claude Opus 4.6 dominates the leaderboard with an overall score of 7.44, significantly outperforming GPT-5.4's 2.56 score. This substantial gap of 4.88 points represents one of the larger performance spreads we've observed in recent evaluations.

Both models were evaluated using our comparative ranking methodology across four response samples, focusing on accuracy and instruction following capabilities. The results highlight distinct strengths and weaknesses that potential users should consider when choosing between these flagship language models.

Benchmark Results

Anthropic's Claude Opus 4.6 secured the top position with commanding performance across all evaluation criteria. The model achieved identical scores of 7.44 for both accuracy and instruction following, demonstrating consistent excellence in understanding and executing complex instructions.

OpenAI's GPT-5.4 ranked second with an overall score of 2.56, matching this score across both evaluation criteria. While this represents a significant gap behind Claude Opus 4.6, it's important to note that these scores reflect comparative rankings rather than absolute performance measures.

ModelOverall ScoreAccuracyInstruction FollowingRank
Anthropic: Claude Opus 4.67.447.447.441
OpenAI: GPT-5.42.562.562.562

Criteria Breakdown

Accuracy

Claude Opus 4.6 demonstrated superior accuracy with a score of 7.44, significantly outperforming GPT-5.4's 2.56. This suggests that Claude Opus 4.6 provides more reliable and factually correct responses across various query types and complexity levels.

Instruction Following

In instruction following capabilities, Claude Opus 4.6 again achieved a perfect score of 7.44 compared to GPT-5.4's 2.56. This indicates that Claude Opus 4.6 more consistently interprets and executes user instructions as intended, maintaining better adherence to specific formatting, tone, or content requirements.

Cost and Latency Analysis

The cost and performance trade-offs between these models present interesting considerations for different use cases. Claude Opus 4.6 operates at a higher cost per output token of $0.028303 compared to GPT-5.4's $0.01908, representing approximately a 48% premium.

However, this cost difference becomes more nuanced when examining total costs per response. Claude Opus 4.6's total cost per response averages $0.010196, while GPT-5.4 costs $0.002514 per response. The higher cost for Claude Opus 4.6 partly reflects its tendency to generate longer, more comprehensive responses with an average of 360 completion tokens versus GPT-5.4's 132 tokens.

Latency presents a clear advantage for Claude Opus 4.6, with an average response time of 1,203ms compared to GPT-5.4's significantly slower 5,270ms. This 4.4x speed advantage makes Claude Opus 4.6 more suitable for real-time applications and interactive use cases.

ModelAvg Latency (ms)Cost per Output TokenTotal Cost per ResponseAvg Completion Tokens
Claude Opus 4.61,203$0.028303$0.010196360
GPT-5.45,270$0.01908$0.002514132

Use Cases and Applications

Based on the evaluation results, Claude Opus 4.6 appears better suited for applications requiring high accuracy and reliable instruction following, such as content creation, complex analysis tasks, and professional writing assistance. Its superior performance across both evaluation criteria makes it ideal for scenarios where quality is paramount.

GPT-5.4 might appeal to users prioritizing cost efficiency for high-volume applications where the performance gap is acceptable. Its lower per-response cost could make it viable for bulk processing tasks, basic content generation, or applications with tight budget constraints.

The significant latency difference also influences use case suitability. Claude Opus 4.6's faster response times make it more appropriate for interactive applications, chatbots, and real-time assistance tools, while GPT-5.4's slower responses might be acceptable for batch processing or non-time-sensitive tasks.

Verdict

Claude Opus 4.6 emerges as the clear winner in this comparison, delivering superior performance across all evaluation criteria while maintaining faster response times. Despite its higher cost per token, the model provides better value for applications requiring high-quality, accurate responses with strong instruction adherence. GPT-5.4 offers a more budget-friendly alternative but with significant performance and speed trade-offs that limit its competitiveness in this comparison.

Backed by real data

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Methodology

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