GLM-5.2 vs Opus 4.8

Benchmark head-to-head

Only one evaluator scores both of these models under a single published methodology: Artificial Analysis, whose Intelligence Index v4.1 runs the same nine evaluations (GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR) against every model on its board, then reports a Coding Index and an Agentic Index alongside. Those three are the rows below — GLM-5.2 and Opus 4.8 as captured in our 2026-07-21 snapshot (GLM-5.2's Intelligence Index re-confirmed live on the Artificial Analysis model page). We do not pair Z.ai's own headline numbers against Anthropic's, because the two are produced on different harnesses and are not directly comparable.

Artificial Analysis reports these for the max-effort configuration of each model, and the Intelligence Index is a rolled-up composite — a single number over nine evaluations — so it hides where within the suite each model pulls ahead or falls back. Read the three indices as a directional cross-vendor snapshot, not a task-by-task verdict. Both models are reasoning models scored with thinking enabled.

Each vendor also publishes its own suite, and this is exactly why we do not pair them. Z.ai reports GLM-5.2 at 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro — and, on that same page, puts Opus 4.8 at 85.0 on Terminal-Bench 2.1, while Anthropic's own System Card records Opus 4.8 at 74.6 on the identical benchmark. Two different numbers for one model is the whole point: harness, effort setting and scaffold move the score, so setting Z.ai's self-run figures beside Anthropic's would manufacture a precision that isn't there. We show only what a single evaluator produced for both.

LLM Index Score

Separately from Artificial Analysis, both models also carry an LLM Index Score — our single composite built from the LiveBench raw task set, weighted and unmodified. It is a different lens (a broad public benchmark rather than a cross-vendor index) and lands in the same place:

A 5.8-point composite gap — same direction as the three Artificial Analysis indices and, if anything, a touch wider. Two independent evaluators agreeing that Opus 4.8 leads by a real, mid-single-digit margin is a firmer read than either alone; what this page settles is whether that lead outweighs owning the weights. See how the score is built.

The two models, side by side

What the gap costs

Take a steady load — 10M input and 2M output tokens a day, the shape of a sustained agentic-coding workload — against each model's first-party API price:

Prices are Z.ai's own API rate for GLM-5.2 ($1.40 / $4.40) and Anthropic's for Opus 4.8 ($5 / $25); because GLM-5.2 is open-weights, third-party hosts price it differently and self-hosting removes the per-token bill entirely, trading it for compute you run yourself. The token counts assume an identical split on both, a simplification: GLM-5.2 and Opus 4.8 use different tokenizers, so the same text splits into different numbers of tokens on each and the real gap moves a few percent either way. Both discount cache hits — GLM-5.2 to $0.26 per 1M input, Opus to $0.50.

Which should you pick?

Before you commit

• The only cross-vendor benchmark that scores both — Artificial Analysis — puts Opus 4.8 ahead on all three indices, but each is a rolled-up composite. A task that leans hard on one specific capability can diverge from what the index implies.
• The two use different tokenizers, so cost and context comparisons are approximate: '1M tokens' of your text is not the same amount of text on each model.
• The real axis here is the deployment model, not a benchmark point. GLM-5.2 ships open weights (self-host, offline, freeze a version, keep data in-house) under MIT; Opus 4.8 is closed and hosted-only — and that difference decides the pick more often than the scores do.
• Input modalities differ: GLM-5.2 is text-only, while Opus 4.8 accepts text, images and files. Any image or document workflow rules GLM-5.2 out regardless of the benchmarks.
• Fast mode (premium high-throughput) exists only on the Opus tier; GLM-5.2 has no equivalent hosted tier, though self-hosting lets you provision your own throughput.

The full read

This comparison is not about which trim to buy or which vendor's generalist to bet on — it is about who owns the model. GLM-5.2 ships its weights under an MIT license; Opus 4.8 is closed and hosted-only. That single fact reframes every number below, because a benchmark gap you can measure sits opposite a set of capabilities no leaderboard scores: self-hosting, offline operation, keeping data in-house, and freezing an exact version for as long as you need it.

On the measurable side, the read is clear and — unlike a same-tier pairing — not a near-tie. Opus 4.8 leads all three Artificial Analysis indices: Intelligence 55.7 to 51.1, Coding 74.3 to 68.8, Agentic 47.2 to 43.1, each a 4-to-6-point margin. Our own LiveBench-based LLM Index Score agrees, 79.67 to 73.84. Two independent evaluators, same direction, a mid-single-digit lead. On raw capability Opus is ahead, and it is not close enough to call a draw.

Then the other column. GLM-5.2 is a 753B-parameter Mixture-of-Experts model — 40B active per token — that you can download from Hugging Face and run on your own hardware, under a license that permits commercial use. That buys what a hosted model cannot: your data never leaves your infrastructure, you can run air-gapped, you can pin a version forever, and at volume you trade a per-token bill for compute you control. Priced through Z.ai's own API it is $1.40 / $4.40 per million tokens against Opus's $5 / $25 — about four times cheaper before you even consider self-hosting.

It is worth being explicit about why the benchmark table stops at the three Artificial Analysis indices. Z.ai publishes its own strong figures — Terminal-Bench 2.1 at 81.0, SWE-bench Pro at 62.1 — and on the same page reports Opus 4.8 at 85.0 on Terminal-Bench, while Anthropic's own System Card records Opus at 74.6 on that identical benchmark. That gap between two 'Opus 4.8 Terminal-Bench' numbers is the entire reason we do not pair vendor suites: different harnesses, effort settings and scaffolds yield different scores for the same model. Only Artificial Analysis runs both under one methodology, so that is the only cross-vendor table we show.

The practical rule follows from the axis rather than the scoreboard. If you need to own the model — data control, offline, self-hosting, a frozen version, or simply the lowest cost at scale — GLM-5.2 is the pick, and it is the strongest open-weights option in its class, close enough to the frontier for most text work. If you want the measured ceiling, image and file input, fast mode, or a fully managed hosted frontier model, and the capability lead is worth roughly four times the token price, Opus 4.8 is the escalation. These are vendor-reported and third-party figures, not our own hands-on runs, so read them as a well-sourced starting point and confirm on the workload you actually have — and, for GLM-5.2, the hardware you would run it on.

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