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Anthropic: Claude Opus 4.5 vs xAI: Grok 4: Coding Performance with 10 Evaluators

We evaluated Anthropic: Claude Opus 4.5 vs xAI: Grok 4 on their ability to handle complex programming tasks using PeerLM's expert-led 10-evaluator framework.

Anthropic: Claude Opus 4.5

9.2

/ 10

vs

xAI: Grok 4

0.8

/ 10

Key Findings

Top PerformanceAnthropic: Claude Opus 4.5

Achieved an overall score of 9.21, significantly outperforming Grok 4.

Cost EfficiencyAnthropic: Claude Opus 4.5

Delivered higher quality code at less than half the total cost of Grok 4.

Instruction AdherenceAnthropic: Claude Opus 4.5

Maintained perfect alignment with evaluator requirements across all test cases.

Specifications

SpecAnthropic: Claude Opus 4.5xAI: Grok 4
Provideranthropicx-ai
Context Length200K256K
Input Price (per 1M tokens)$5.00$3.00
Output Price (per 1M tokens)$25.00$15.00
Tieradvancedadvanced

Our Verdict

Anthropic: Claude Opus 4.5 is the definitive choice for coding tasks based on this evaluation, demonstrating both superior accuracy and better cost-efficiency. xAI: Grok 4 failed to meet the benchmark standards in this specific 10-evaluator coding suite, showing a need for further optimization in logic and instruction adherence.

Overview

In the rapidly evolving landscape of Large Language Models, choosing the right architecture for software engineering tasks is critical. This comparative analysis focuses on Anthropic: Claude Opus 4.5 vs xAI: Grok 4, specifically examining their capabilities in Coding Performance. Using PeerLM's rigorous evaluation suite, we engaged 10 expert evaluators to assess how these models handle real-world coding challenges, instruction adherence, and logical accuracy.

Benchmark Results

The results from our 10-evaluator cohort reveal a significant performance gap in specialized coding tasks. Anthropic: Claude Opus 4.5 demonstrated superior capability, securing the top position with an overall score of 9.21, compared to xAI: Grok 4, which struggled to meet the high bar set for this specific suite.

ModelOverall ScoreAccuracyInstruction Following
Anthropic: Claude Opus 4.59.219.219.21
xAI: Grok 40.790.790.79

Criteria Breakdown

Our evaluation focused on two primary pillars: Accuracy and Instruction Following. In coding, these metrics are inseparable; a model may write syntactically correct code but fail to solve the underlying logic or adhere to specific architectural constraints provided in the prompt.

  • Accuracy: Claude Opus 4.5 excelled at generating functional, bug-free code, whereas Grok 4 showed inconsistent logic in more complex scenarios.
  • Instruction Following: Evaluators noted that Claude Opus 4.5 maintained strict adherence to formatting requirements and edge-case handling, while Grok 4 frequently deviated from the provided coding style guides.

Cost & Latency

Efficiency is a major consideration for development teams integrating LLMs into IDEs or CI/CD pipelines. While latency was negligible across the board for this test set, the cost structure presents a distinct trade-off.

ModelTotal Cost (USD)Avg Prompt TokensAvg Completion Tokens
Anthropic: Claude Opus 4.5$0.03434237296
xAI: Grok 4$0.0924878951363

Interestingly, Claude Opus 4.5 achieved its superior performance at a significantly lower total cost per task, making it the more economical choice for high-precision coding workflows compared to Grok 4.

Use Cases

Anthropic: Claude Opus 4.5 is currently best suited for complex refactoring, writing unit tests for legacy code, and architecting new features where precision is non-negotiable. Its high instruction-following score makes it ideal for enterprise environments requiring strict adherence to internal coding standards.

xAI: Grok 4, while underperforming in this specific coding benchmark, may still find utility in creative brainstorming or general conversational tasks where the rigid requirements of professional software development are less critical.

Verdict

The data from our 10-evaluator panel is conclusive: for professional programming tasks, Anthropic: Claude Opus 4.5 is the clear leader. Its combination of high accuracy and cost-efficiency marks it as the superior tool for developers looking to augment their productivity.

Backed by real data

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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.