Choose Claude Opus 4.8 for…
- ✓Production coding work where wall-clock time matters
- ✓A more predictable reasoning envelope
- ✓Teams already using Claude-based coding tools
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Head-to-head · updated 2026-07-28
Claude Opus 4.8 vs GLM 5.2
Price, speed and real outputs from the same PromptFrenzy test—not an aggregate of vendor benchmark claims.
Bottom line
Claude Opus 4.8 is the more practical production choice in our shared coding test; GLM 5.2 is the open-weights, low-token-price choice when latency and a larger reasoning budget are acceptable. Opus finished the identical Three.js brief in about 197 seconds inside the shared 64k-token envelope. GLM produced a larger code artifact, but took about 1,209 seconds and only completed after we raised its ceiling to roughly 120k tokens.
| At a glance | Claude Opus 4.8 | GLM 5.2 |
|---|---|---|
| Maker | Anthropic | Z.ai |
| Status | Generally available | Generally available |
| Released | 2026 | 2026 |
| Input price | $5 / 1M | $1 / 1M |
| Output price | $25 / 1M | $3.2 / 1M |
| Context window | Not verified | Not verified |
Which model is best for what?
Choose GLM 5.2 for…
- ✓Open-weights or self-hosted deployments
- ✓Low API token prices
- ✓Long-horizon generation where a 20-minute run is acceptable
Same brief, side by side
Both models received the same input and first-shot rule. Every artifact below is the unedited output we actually received.
Cyberpunk Alley in Three.js
We gave eight AI models one prompt and one shot: write a complete Three.js scene from scratch — a neon-lit cyberpunk alley at night in the rain — with a locked-down 10-second camera dolly so every render lines up frame for frame. No tools, no retries, no edits. Every scene below is the model’s own code running in real time, from a polished neon corridor to one that never compiles. Which one nails the brief? Your call.
Claude Opus 4.8
Anthropic
- Latency
- 197s
- Output tokens
- 14,127
- Code size
- 7,411 chars
- Est. cost at current rates
- $0.36
The top tier of Anthropic’s Claude 4 family, and the company’s flagship coding model until Fable 5 arrived. The Opus line is the one most big AI coding tools default to. Ran with adaptive thinking at high effort.
GLM 5.2
Z.ai
- Latency
- 1209s
- Output tokens
- 82,044
- Code size
- 23,149 chars
- Est. cost at current rates
- $0.26
Z.ai’s open-weights flagship (Zhipu) — strong long-horizon coding at a fraction of frontier cost, and the only open-weights model on the page. Run direct on the z.ai API with thinking enabled. It is the one stated exception to the shared 64,000-token ceiling: at that budget it spends every token reasoning and returns no code at all, so it is shown here on the larger ~120k budget where it does finish — the single lane running on more room than the rest, called out rather than hidden. The longest run on the page, at twenty minutes.
Methodology: identical prompt, one shot, no retries or human edits.
Read the full methodology and fairness disclosure
Every model received the identical prompt in a single turn: a complete single-file Three.js scene with no external assets, plus an exact camera path (eye-level start, constant-speed 10-second dolly down the alley centerline with a ±4° sinusoidal sway) so the renders line up frame for frame. No tools, no retries, no human edits — the first complete HTML file returned is what you see, recorded in headless Chromium under software WebGL (SwiftShader) on a virtualized clock for constant frame pacing. The fair comparison rests on one shared control: every model runs under the same 64,000-token total-output ceiling. Reasoning bills as output on every provider, so that single number bounds cost and thinking-room identically — and it is why each model’s billed tokens exceed the size of the code it returned. We deliberately do NOT force one "effort" label across vendors, because the tiers are not calibrated to each other: OpenAI’s GPT-5 family runs at medium reasoning effort here, which on this brief already spends more reasoning than the Claude models do at high (GPT-5.5 spent 53k tokens against Sonnet 5’s 31k). Matching the label would equalise nothing — and when we tested forcing high effort, it only pushed the OpenAI models to write more ambitious code that failed to compile. So we match the envelope, not the label, and disclose each model’s setting: the three Claude entries ran with adaptive thinking at high effort; the three GPT-5 entries at medium reasoning effort; Gemini 3.1 Pro with an explicit 32,768-token thinking budget (raised from an earlier 8,192 after a reader rightly flagged the smaller cap as unfair). One model is a stated exception: GLM 5.2 cannot finish inside the 64k ceiling — at high effort it spends the whole budget reasoning and emits no code — so it is shown on the larger ~120k budget where it does complete, the single lane on more room than the rest, called out here rather than hidden. Two honest failures stayed in unedited: GPT-5 mini returned code that throws on load and renders a black frame; that black frame is its real output. GPT-5.5 Pro is excluded entirely because its reasoning spend cannot be capped.
Caveats
- !This is one shared creative-coding brief, not a universal coding benchmark.
- !The token ceilings were not equivalent: GLM 5.2 returned no code at 64k and is shown on a larger ~120k allowance.
- !Artifact size is not a quality score. Watch both outputs and inspect the methodology before choosing a winner.