GLM-5.2 vs Claude Opus for AI Coding Agents: Same Results at 46% Cost

Jun 25, 2026

Problem

I run Claude Code daily. Opus works, but it’s expensive. A single coding session can burn $10–30. If I’m building a pipeline that runs dozens of agent tasks a day, Opus pricing breaks the budget. So I need to know: does a cheaper open-weights model hold up on real coding agent work, or does it trip over itself after a few tool calls?

The GLM-5.2 release caught my eye because early reports hinted it keeps up with frontier closed models. But I wanted a controlled comparison, not marketing numbers.

Setup

I ran both models through Claude Code as the agent harness, using terminal-bench — a suite of 45 real shell-level coding tasks. Each task has hidden tests that give a binary pass/fail. The agent gets a shell, a filesystem, and standard tools (read, write, edit, bash). No hand-holding.

Harness:   Claude Code (identical for both runs)
Test set:  terminal-bench, 45 tasks
Models:    GLM-5.2 (open-weights) vs Claude Opus (closed, frontier)
Metric:    Binary pass/fail via hidden tests
Cost:      Recorded per-task token consumption

Both models ran every task once. No retries, no cherry-picking.

What Happened

Both models solved 25 out of 45 tasks. Identical capability.

                Pass    Fail    Total
GLM-5.2          25      20      45
Claude Opus       25      20      45
Agreement         —       —       43/45

On 43 of 45 tasks, the models agreed — 24 both-pass and 19 both-fail. Only 2 tasks split (one model passed while the other failed). That’s a 95.6% agreement rate.

Cost

GLM-5.2:        ~$15.00
Claude Opus:     $32.67
GLM is 46% of Opus cost

Turns

GLM-5.2 used more tool-calling turns: 760 vs Opus’s 554. That’s 37% more back-and-forth. The model solves the same problems but takes more steps to get there.

Cumulative token usage across 10 rounds of agent tool calls for GLM-5.2 vs Claude Opus

More turns means higher latency even if token cost stays low. If your workflow is latency-sensitive (CI pipeline, real-time pair programming), the extra round trips matter. If you’re running batch jobs overnight, the cost savings win.

Confident-Wrong Failures

Both models share a failure mode: they confidently declare success when hidden tests say otherwise. They don’t know they failed.

1. Agent reads task, writes code
2. Agent runs a quick smoke test (passes)
3. Agent declares "done"
4. Hidden test runs → FAIL
5. Agent never knows

Flow diagram showing how one wrong reasoning token propagates errors through the chain

The root cause: a single wrong assumption early in the reasoning chain cascades. The model writes code that matches its wrong understanding, tests that match its wrong code, and concludes everything is fine. Hidden tests catch what self-review misses.

Early GLM Rate-Limit Issues

GLM-5.2 hit upstream 502 and 429 errors in early runs. Rate limiting from the inference provider caused flaky starts. After stabilizing, the model performed fine. Worth noting if you self-host or use a provider with tight limits.

Why This Matters

Bar chart comparing per-million-token cost of Chinese LLMs vs Western models

The cost gap between Chinese open-weights models and Western closed models is real. GLM-5.2 at 46% of Opus cost means you can run 2x the agent tasks for the same budget. For a pipeline doing daily blog generation, code review, or test generation, that doubles throughput without doubling cost.

The near-perfect agreement (95.6%) tells me these models share a similar capability ceiling on structured coding tasks. Where they succeed, they succeed together. Where they fail, the failure patterns match too.

The cost-latency trade-off is the real decision point:

                          Opus        GLM-5.2
Cost per 45 tasks         $32.67      ~$15.00
Tool turns (latency)      554         760 (+37%)
Confident-wrong rate      same        same
Pass rate                 25/45       25/45

Choose Opus when latency matters. Choose GLM-5.2 when cost matters. For batch agent pipelines, GLM-5.2 is the clear call.

Summary

In this post, I compared GLM-5.2 and Claude Opus on a 45-task coding agent benchmark. Both solved 25/45 tasks with 95.6% agreement. GLM-5.2 cost 46% of Opus but used 37% more turns. Both share the same confident-wrong failure mode where self-review misses errors hidden tests catch. For budget-sensitive agent pipelines, an open-weights model now matches the frontier — the gap is gone.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: GLM-5.2 vs Claude Opus Coding Agent Benchmark

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!