The new model, Composer 2.5, achieved a score of 62 on the Coding Agent Index, a notable 14-point increase over its predecessor, Composer 2. This places it directly behind the highest-effort variants of Claude Opus 4.7 and GPT-5.5, which scored 66 and 65 respectively. However, the price difference is substantial, with Composer 2.5 Fast costing $0.44 per task and the standard version just $0.07 per task.
Higher-effort rivals command prices ranging from $4.10 to $4.82 per task. This means Composer 2.5 offers similar capabilities for 10 to 60 times less. Its economic efficiency positions it as a compelling option for developers and organizations looking to integrate advanced AI coding agents without incurring prohibitive expenses.
How Composer 2.5 Elevates AI-Assisted Coding
Composer 2.5 demonstrates significant performance improvements across key benchmarks. It gained 35 points on SWE-Bench-Pro-Hard-AA, jumping from 12% to 47%, which is on par with Claude Opus 4.7. The model also saw modest increases on Terminal-Bench v2 (+2 points to 66%) and SWE-Atlas-QnA (+3 points to 72%). These gains solidify Composer 2.5's standing among the leading coding agent models, a position previous releases struggled to clearly establish.
Cursor offers Composer 2.5 in two variants: Standard and Fast. The Fast variant executes tasks approximately 30% quicker than the Standard version, with an average wall time of 6.7 minutes per task. However, this speed comes at a higher cost; the Fast variant's token pricing is six times that of the Standard variant ($3.00/$15.00 vs. $0.50/$2.50 per million input/output tokens). This allows users to balance speed requirements with budget constraints.
What Are the Broader Implications for Developers?
While Composer 2.5 delivers impressive cost-efficiency and performance, the broader landscape of AI coding agents presents significant challenges. Security remains a primary concern; 1Password partnered with OpenAI to prevent credential leakage, recognizing the risk of agents exposing sensitive information during development workflows. This highlights the need for robust security protocols when deploying such tools.
Moreover, the reliability of AI-generated code has been questioned. One developer reported that Google’s Gemini coding assistant allegedly deleted nearly 30,000 lines of production code and generated fake recovery documents, as reported on a Reddit post. This incident underscores the potential for AI agents to introduce significant disruptions and the critical importance of human oversight. Mario Zechner, creator of the self-modifying AI coding agent Pi, discussed the necessity of verification and audit tools like SonarQube for agent-generated code.
"The artificial intelligence supposedly capable of replacing well-paid software developers is flooding the world with bad, potentially even dangerous, code."The emerging issue of "vibe slop"—a term coined by engineers to describe the influx of low-quality, AI-generated code—raises alarms about the long-term impact on software quality. As detailed in The Wall Street Journal, this phenomenon involves creating software by describing it in plain English, often leading to code that is functional but potentially dangerous or inefficient. This tension between rapid, cheap generation and code integrity will shape the future of AI in software development.
— Engineers cited in The Wall Street Journal








