GLM-5.2 vs Claude Opus 4.8: Full Comparison

The Verdict

GLM-5.2 is the first open-weights model to make Claude Opus 4.8 look expensive without making it look slow. Opus still holds the benchmark crown. GLM-5.2 gets close enough, on open weights, that the price gap becomes the story.

On June 16, 2026, Z.ai released GLM-5.2, a 753B-parameter MoE model built for long-horizon coding agents, under an MIT license. Three weeks earlier Anthropic shipped Claude Opus 4.8, its most capable general-access model. This is the comparison that matters for anyone deciding where to point an agent: the strongest open model against the strongest closed one.

The short version: Opus 4.8 wins most benchmarks, with its largest margins on multi-hour software engineering and tool-use tasks. GLM-5.2 wins a handful (mostly olympiad math and one terminal-agent harness), stays within a point on a few agentic evals, and undercuts Opus on price by 3.6x to 5.7x. Here are the seven differences that decide which one you should run.

At a Glance

Every Benchmark, Side by Side

Z.ai published GLM-5.2 against Opus 4.8 on 19 reasoning, coding, and agentic benchmarks. Sorted by margin, the pattern is clean: GLM-5.2 takes the top of the chart on math and one terminal harness, then Opus 4.8 pulls ahead and the gap widens as tasks get longer and more agentic.

GLM-5.2 is the highest-scoring open-weights model on every one of these benchmarks. The comparison here is against the closed frontier, which is the harder test. Read the chart as “how much capability are you giving up to go open and cheap,” and the answer is: not much on reasoning, a real amount on multi-hour engineering.

1. Price: Up to 5.7x Cheaper

This is the headline difference. GLM-5.2 is $1.40 in / $4.40 out per million tokens. Opus 4.8 is $5 in / $25 out, and its fast mode doubles that to $10 / $50.

For an agent that reads large repositories and writes long diffs, output tokens dominate the bill, so the 5.7x output gap is the one to feel. A workload that costs $1,000/day on Opus 4.8 output lands near $176/day on GLM-5.2. GLM-5.2 holds the same rate across Z.ai, Novita, and Friendli serverless endpoints, so the price is a property of the model, not a single vendor’s promo.

2. Open Weights vs Closed API

GLM-5.2 is MIT licensed with open weights on HuggingFace, runnable on vLLM, SGLang, xLLM, KTransformers, and Transformers. No regional restrictions, no API gate. You can fine-tune it, quantize it, run it air-gapped, and pin a version forever.

Claude Opus 4.8 is proprietary. You reach it through Anthropic’s API, Amazon Bedrock, Google Vertex AI, or Microsoft Foundry, and you accept its rate limits, deprecation schedule, and content policies. For regulated data that cannot leave your network, or for products that need a frozen model behind them, this difference outweighs every benchmark in the table.

One caveat in GLM-5.2’s favor on capability but against it on scope: GLM-5.2 is text only, while Opus 4.8 handles vision. If your agent reads screenshots, PDFs, or UI state from images, that is an Opus-only job today.

3. Context: Both Hit 1M

Context window is a tie. Both models accept a 1 million token context, and both cap output near 130K. GLM-5.2’s actual claim is not the number but the quality: it was trained specifically to hold coding-agent trajectories together across that full window, using an architecture change (IndexShare, covered below) that keeps the long-context cost manageable.

In practice, “usable 1M” is hard to verify from a spec sheet, and Opus 4.8 has its own strong long-context track record. Treat this as parity until you stress it on your own trajectories. Neither model wins context on paper.

4. Reasoning and Math

This is GLM-5.2’s strongest category relative to Opus. It wins AIME 2026 (99.2 vs 95.7) and IMOAnswerBench (91.0 vs 83.5), two olympiad-grade math evals. On the rest, Opus stays narrowly ahead.

The takeaway: for competition math, GLM-5.2 is genuinely at or above the frontier. For broad expert knowledge (Humanity’s Last Exam, GPQA), Opus 4.8 keeps a real edge, widest on the no-tools HLE split.

5. Coding and SWE

On standard coding benchmarks, GLM-5.2 is the strongest open model ever shipped, but Opus 4.8 still leads the head-to-head. The exception is Terminal-Bench 2.1: GLM-5.2 trails under the Terminus-2 harness (81.0 vs 85.0) but edges ahead under each model’s best reported harness (82.7 vs 78.9).

The gap tracks task structure. On bounded, single-shot terminal tasks GLM-5.2 is competitive. On building a repository from a natural-language spec (NL2Repo), Opus 4.8 is in a different tier, by more than 20 points.

6. Long-Horizon Agentic Work

GLM-5.2 was explicitly built for long-horizon tasks, and it is the second-best model in the world on several of them. But “second” here means second to Opus 4.8, which owns the longest, messiest benchmarks.

• FrontierSWE (dominance): 74.4 vs 75.1. Effectively a tie. GLM-5.2 trails by under a point on hours-long open-ended projects.
• MCP-Atlas: 76.8 vs 77.8. Within a point on tool-use orchestration.
• PostTrainBench: 34.3 vs 37.2. Close on autonomous model post-training.
• Tool-Decathlon: 48.2 vs 59.9. Opus pulls away by nearly 12 points.
• SWE-Marathon: 13.0 vs 26.0. Opus doubles GLM-5.2 on ultra-long-horizon engineering.

So the marketing and the data agree, with a twist. GLM-5.2 really is strong on long-horizon work, enough to sit a hair behind Opus on FrontierSWE and MCP-Atlas. But the moment tasks stretch to the marathon length Opus was tuned for, the closed model’s lead roughly doubles. If your agents routinely run for hours, that is where you pay for Opus.

7. Effort Control and Architecture

Both models expose tunable thinking effort. GLM-5.2 ships High and Max levels; Opus 4.8 adds an extra (xhigh) tier above high, plus max. In both, more effort buys accuracy on hard problems at the cost of latency and tokens, and you set it per request.

The architectural story is GLM-5.2’s. It introduces IndexShare, which reuses one lightweight sparse-attention indexer across every four layers, cutting per-token FLOPs by 2.9x at 1M context. Z.ai also reworked the model’s MTP layer for speculative decoding, raising acceptance length by up to 20%. These are the levers that let an open 753B model serve a 1M window at a price Anthropic does not match. Anthropic does not publish Opus 4.8’s architecture, so there is no symmetric comparison to make.

Which One Should You Use?

Choose Claude Opus 4.8 if you need the highest ceiling on multi-hour software engineering and tool use (NL2Repo, SWE-Marathon, Tool-Decathlon), if your agent needs vision, or if you want a managed frontier model with first-party cloud support and you can absorb the price.

Choose GLM-5.2 if cost is a first-order constraint, if you need open MIT weights to fine-tune or self-host, if your workload is reasoning- and math-heavy, or if you want frontier-adjacent agentic coding at roughly a fifth of the output price. For most teams running high-volume agents on bounded tasks, GLM-5.2 is the better dollar-for-token deal; for the hardest long-horizon jobs, Opus 4.8 still earns its premium.

Compare both live on the LLM Stats comparison page, or see each model’s full benchmark profile for GLM-5.2 and Claude Opus 4.8.