GPT-5.5 and GLM-5.2 are two of the strongest coding models available today, but they take very different approaches. GPT-5.5 is designed for high-end reasoning and coding performance, while GLM-5.2 has gained attention for delivering competitive results at a significantly lower cost.

To compare them in a real-world coding workflow, we evaluated both models using the same coding agent on Terminal-Bench. The benchmark measures coding performance, agent efficiency, and inference cost across a shared set of software engineering tasks.

Benchmark Setup

All evaluations were conducted using Terminal-Bench, a consistent framework for measuring agentic coding performance. To ensure a fair comparison, we kept every variable identical between models:

• Benchmark: Terminal-Bench (45 tasks)

• Coding Agent: Claude Code

• GPT Model: GPT-5.5 (Medium reasoning)

• GLM Model: GLM-5.2

• Same prompts

• Same tool access

• Same hidden evaluation tests

• Same turn budget

• Same execution environment

Evaluation Tasks

All models were evaluated on the exact same 45-task subset of Terminal-Bench under identical conditions. The benchmark covers a broad range of real-world software engineering workflows, including debugging, repository navigation, multi-file code changes, testing, package management, command-line tooling, asynchronous programming, data processing, parsing, backend development, and algorithmic reasoning.

Representative tasks include broken-python (Python debugging), csv-to-parquet (data transformation), cancel-async-tasks (async programming), classifier-debug (multi-step debugging), fix-pandas-version (dependency management), fibonacci-server (backend implementation), cprofiling-python (performance profiling), dna-assembly (algorithmic reasoning), crack-7z-hash (cryptanalysis), and hydra-debug-slurm-mode (infrastructure debugging).

Using the exact same task set, coding agent, prompts, hidden evaluation tests, execution environment, and turn budget across every model ensures the comparison isolates model capability and efficiency rather than differences in workload.

Benchmark Results

GPT-5.5 (Medium) passed 29/45 tasks with 726 model calls and a cost of $25.42, while GLM-5.2 passed 25/45 tasks with 760 model calls and an approximate cost of $15 using prompt caching.

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Cost Comparison

While GPT-5.5 delivered stronger overall coding performance, GLM-5.2's primary advantage is cost, especially with prompt caching (~$15 vs GPT-5.5's $25.42). For teams running coding agents at scale, this can translate into substantial savings without a proportional drop in benchmark performance.

Agent Efficiency

Task completion only tells part of the story. We also compared how efficiently each model navigated the benchmark by measuring agent turns and token usage.

GPT-5.5 generally completed tasks in fewer iterations, resulting in shorter execution paths and lower overall agent activity. GLM-5.2 required more agent iterations (760 total turns, 16.9 per task) to arrive at its results, maintaining competitive task completion rates while leveraging a significant cost advantage.

Key Takeaways

• GPT-5.5 achieved the highest overall task completion rate in our Terminal-Bench evaluation.

• GLM-5.2 remained highly competitive while delivering significantly lower inference costs.

• GPT-5.5 generally required fewer agent iterations to complete coding tasks, resulting in more efficient execution.

• GLM-5.2 offers one of the strongest price-to-performance ratios for autonomous coding workflows.

• The right choice depends on whether your priority is maximum coding capability or cost-efficient deployment at scale.

Final Verdict

GPT-5.5 delivered the strongest overall performance in our benchmark, consistently solving more coding tasks while requiring fewer agent iterations. For teams prioritizing reliability and the highest possible task completion rate, it remains the stronger choice.

GLM-5.2, however, continues to impress with its exceptional price-to-performance ratio. While it may require more iterations to complete certain tasks, its significantly lower inference cost makes it a compelling option for large-scale coding agents and cost-sensitive deployments.

Overall, GPT-5.5 remains the stronger coding model in this evaluation, while GLM-5.2 offers exceptional value for teams optimizing for inference cost. The right choice ultimately depends on whether your priority is maximum task success or cost-efficient deployment at scale.