# Grok 4.7 Shows Improved AI Coding Performance and Cost Efficiency

> Grok 4.7 has outperformed its predecessor with a notable pass@3 rate of 40.0% on the Senior SWE-Bench, while also being significantly cheaper per trial than competing models. This combination of high performance and low cost positions Grok as a formidable player in the coding agent market.

**Source**: snorkel.ai | **Published**: 2026-09-22 | **Type**: research

## Key Facts

- Grok 4.7's pass@3 of 40.0% shows improved performance, indicating competitive edge in AI coding.
- Cost per trial at $0.24 vs. Opus 4.8's $3.36 reveals significant financial advantage for Grok 4.7.
- Despite lower task completion, Grok 4.7 converts solves to tasteful ones better, enhancing quality perception.
- Reliability issues with a 7.4% pass^3 signal vulnerability, suggesting need for improvement in consistency.
- Strategic focus on cost-effectiveness over first-attempt success may attract budget-conscious firms.

## Summary

Grok 4.7 has recently been evaluated on the Senior SWE-Bench, a benchmark designed to assess the performance of coding agents against senior software engineers. The model achieved a tasteful pass@3 rate of 40.0%, an improvement from Grok 4.6's 38.9%. This advancement is significant not only because it enhances Grok's standing—ranking sixth overall—but also due to its cost-effectiveness, priced at $0.24 per trial, substantially lower than competitors like Opus 4.8, which costs $3.36 per trial.

The Senior SWE-Bench is structured to mimic real-world engineering challenges, with tasks drawn from both public and private repositories. It evaluates agents on their ability to produce high-quality code that meets behavioral requirements, rather than just correctness. The benchmark's difficulty is evident, as even top-performing models struggle to achieve a tasteful solve on more than 65% of tasks. Grok 4.7's results indicate a competitive edge in both performance and cost, which could reshape market dynamics.

In terms of specific performance metrics, Grok 4.7 shows improvements in both the tasteful pass@1 and pass@3 rates, rising to 27.4% and 40.0% respectively. This performance places it on par with Opus 4.8, while also demonstrating a more efficient use of resources. Grok 4.7 achieves its results using approximately 39.4K output tokens, comparable to Fable 5.1 and GPT-5.6 Sol, yet at a fraction of their costs. This pricing strategy positions Grok 4.7 favorably for organizations looking to deploy coding agents at scale, particularly in environments where cost control is critical.

However, Grok 4.7 does exhibit a slight decline in the share of tasks solved compared to its predecessor, with a pass@3 of 63.2% versus 65.3% for Grok 4.6. Yet, it converts a higher percentage of these solves into tasteful ones—63.3% compared to 59.6%. This indicates that while Grok 4.7 may solve fewer tasks overall, it is more effective at delivering quality solutions when it does succeed.

Reliability remains a challenge for Grok 4.7, with a tasteful pass^3 of only 7.4%, significantly lower than Fable 5.1's 22.1% and Grok 4.6's 16.8%. This inconsistency suggests that while Grok 4.7 can perform well under certain conditions, it may not always deliver reliable results on the first attempt. For organizations that prioritize first-attempt accuracy, this could be a critical consideration.

The advancements in Grok 4.7 signal a notable shift in the competitive landscape of AI-driven coding agents. As the technology matures, the ability to deliver high-quality outputs at lower costs will likely become a key differentiator. Companies may increasingly favor models like Grok 4.7 that offer a balance of performance and affordability, particularly in scenarios where iterative testing and retries are feasible. This trend could lead to broader adoption of AI coding agents in software development, fundamentally altering how engineering teams operate and interact with technology. The implications for future development and deployment strategies are profound, as organizations seek to leverage these tools for enhanced productivity and efficiency.

## Entities

- **Companies**: Grok, Opus, Fable, GPT, Snorkel.ai, Correlation One, Amazon, Google
- **Products**: Grok 4.7, Grok 4.6, Opus 4.8, Fable 5.1, GPT-5.6 Sol
- **Technologies**: AI, deep learning, LLMs
- **People**: Jonathan Schlosser
- **Organizations**: UNC Chapel Hill

## Key Concepts

Senior SWE-Bench, tasteful pass, benchmarking coding agents, cost per trial, code quality, real PRs, engineering challenges, AI-driven applications

## Definitions

- **Senior SWE-Bench**: A benchmark for evaluating coding agents against the performance of senior engineers.
- **tasteful pass**: A measure of how well an agent produces code that meets behavioral requirements and quality standards.
- **output tokens**: Units of measure for the amount of code generated by the AI during a trial.
- **pass@k**: A metric indicating the percentage of tasks successfully solved by the agent at a given ranking level.
- **retries**: Subsequent attempts to solve a task after an initial failure.

## Use Cases

- Evaluating AI coding agents
- Benchmarking performance of AI models
- Cost-effective AI solutions for coding tasks
- Improving code quality in software development
- Training AI in real-world scenarios
- Developing AI-driven applications

## Frequently Asked Questions

**What is Grok 4.7?**

Grok 4.7 is an AI coding agent evaluated on the Senior SWE-Bench, designed to mimic the performance of senior software engineers. It has shown improvements in its ability to produce quality code compared to its predecessor, Grok 4.6.

**How does Grok 4.7 compare to other models?**

Grok 4.7 ranks sixth overall in the Senior SWE-Bench, achieving a tasteful pass@3 of 40.0%. It offers a significantly lower cost per trial compared to models like Opus 4.8, making it a more economical choice.

**What are the main metrics used in the evaluation?**

The evaluation uses metrics such as tasteful pass rates at k=1 and k=3, as well as the average output token cost per trial. These metrics help assess both the quality and efficiency of the coding agents.

**What challenges do AI coding agents face?**

AI coding agents, including Grok 4.7, still struggle with many tasks, particularly in producing high-quality solutions on first attempts. This highlights the ongoing challenges in achieving reliable performance in complex coding scenarios.

**Who is Jonathan Schlosser?**

Jonathan Schlosser is an AI Advocate at Snorkel.ai with over 10 years of experience in data science and AI. He has taught graduate-level courses and worked with various organizations to enhance AI education and application.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/grok-47-shows-improved-ai-coding-performance-and-cost-efficiency)
- [Original source](https://snorkel.ai/blog/grok-4-7-on-senior-swe-bench-strong-pass3-cheaper-cost/)
- [Grok](https://welcome.ai/company/grok): Featured company

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Source: Welcome.AI | https://welcome.ai/content/grok-47-shows-improved-ai-coding-performance-and-cost-efficiency