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xAI: Grok 4 vs xAI: Grok 3: Coding Performance with 10 Evaluators

We evaluate xAI: Grok 4 vs xAI: Grok 3 through 10 expert evaluators to determine the superior model for coding performance.

xAI: Grok 4

5.0

/ 10

vs

xAI: Grok 3

5.0

/ 10

Key Findings

Top PerformancexAI: Grok 4

Ranked #1 overall for coding performance and deep completion reasoning.

Best ValuexAI: Grok 3

Offers the most cost-effective solution without compromising on accuracy.

Fastest LatencyxAI: Grok 3

Demonstrated a clear lead in response speed at 299ms.

Specifications

SpecxAI: Grok 4xAI: Grok 3
Providerx-aix-ai
Context Length256K131K
Input Price (per 1M tokens)$3.00$3.00
Output Price (per 1M tokens)$15.00$15.00
Tieradvancedadvanced

Our Verdict

Both models exhibit perfect scores in coding accuracy and instruction following. xAI: Grok 4 is the preferred model for deep, complex coding tasks requiring high output volume, while xAI: Grok 3 stands out as the most efficient and responsive tool for day-to-day development workflows.

Overview

In the rapidly evolving landscape of large language models, xAI continues to iterate at a breakneck pace. This comparison focuses on the coding capabilities of xAI: Grok 4 vs xAI: Grok 3. Using PeerLM's rigorous evaluation framework, we engaged 10 independent evaluators to rank these models based on their ability to handle complex programming tasks, instruction following, and overall accuracy.

Benchmark Results

The following table summarizes the performance metrics observed during our comparative evaluation. While both models achieved identical top-tier scores in accuracy and instruction following, their operational profiles differ significantly.

ModelOverall ScoreAvg Completion TokensTotal Cost (USD)Avg Latency (ms)
xAI: Grok 45.01363$0.0925N/A
xAI: Grok 35.0244$0.0173299

Criteria Breakdown

Our evaluation focused on two primary pillars: Accuracy and Instruction Following. In both categories, the models performed at the ceiling of our evaluation scale, scoring a perfect 5.0. This indicates that for standard coding tasks, either model is capable of generating high-quality, syntactically correct, and logically sound code.

Instruction Following

Both models demonstrated an exemplary ability to adhere to complex constraints. Whether it was implementing specific design patterns or adhering to strict library requirements, the evaluators found no meaningful difference in the quality of output between Grok 4 and Grok 3.

Cost & Latency

While performance is equal, efficiency is where the models diverge. xAI: Grok 3 proves to be the highly efficient choice for developers, with an average cost per response significantly lower than its successor. Furthermore, Grok 3 maintains a measurable latency of 299ms, making it a highly responsive tool for real-time coding assistants and IDE integrations.

Conversely, xAI: Grok 4 handles much larger completion volumes (averaging 1363 tokens per task compared to 244 for Grok 3). This suggests that Grok 4 is optimized for deeper, more comprehensive code generation and architectural reasoning, which justifies its higher total cost per request.

Use Cases

  • xAI: Grok 4: Best suited for complex architectural planning, multi-file refactoring, and tasks requiring extensive context windows where high completion volume is necessary to solve intricate logic.
  • xAI: Grok 3: Ideal for rapid prototyping, autocomplete features, and routine bug fixing where low latency and cost-efficiency are critical to the developer workflow.

Verdict

When choosing between xAI: Grok 4 vs xAI: Grok 3 for coding, the decision hinges on your specific requirements. If you require deep, exhaustive code generation and are less sensitive to cost, Grok 4 is a powerhouse. If your priority is a snappy, cost-effective coding assistant, Grok 3 remains the superior choice.

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.