GLM 5.2 vs Opus 4.8 vs GPT 5.5
Real-World Agentic Coding Test
Rapid Research Brief · June 22, 2026
AI ModelsBenchmarksLLM Comparison
Provenance Labs · Generated by Gambit (Hermes Agent)
Medium-High Confidence
Executive Summary
Despite lower per-token pricing, GLM 5.2 burns 10x more tokens to produce inferior results vs Opus 4.8 and GPT 5.5. Subsidized subscription plans make frontier models ~10x cheaper in practice.
GLM 5.2
Open-weight ~1T param model. Cheaper per-token but 10x less efficient. Consistent bottom rank in all tasks.
Opus 4.8
Best for coding tasks. Won the browser game test with smooth physics, better visuals, 100K tokens.
GPT 5.5
Best for design tasks. Won the landing page test with Three.js integration, animated UI, 100K tokens.
DeepSWE Bench
113 task benchmark across 5 languages. GLM 44% @ $3.92 vs Opus 49% @ $3.44 and GPT 54% @ $2.75.
GLM 5.2's Token Inefficiency
Lower per-token cost · Higher total cost
| Metric | GLM 5.2 | Opus 4.8 | GPT 5.5 |
|---|
| Input / 1M tokens | $1.40 | ~$8.00 (5.7x) | ~$9.50 (6.8x) |
| Output / 1M tokens | $4.40 | ~$25.00 | ~$30.00 |
| Game task tokens | 1.35M | 100K | ~100K |
| Game task cost | $1.21 | ~$0.20 | ~$0.20 |
Key insight: Outcomes per task matter more than per-token pricing. GLM's efficiency problem negates its cost advantage entirely.
Source 1: Chase AI video benchmark data
Opus 4.8 Dominates Game Dev
3D racing game · Browser-based · Two passes
- Opus 4.8: Finished in 13 min. Smooth drift physics, sound, dynamic lighting, shadows. Second pass added AAA-quality car model. Winner.
- GLM 5.2: Janky controls, glitchy collision, no track/field differentiation. Jumpy physics. Second pass added glare that made it worse.
- GPT 5.5: "Foundry Circuit" — broken wheels spinning wrong way, annoying sounds, confusing track boundaries. Second pass fixed wheels but little else.
Verdict: Opus clearly ahead. GLM and GPT both produced janky results. Opus used ~100K tokens for both passes vs GLM's 2.5M+.
Source 1: Chase AI video Test 1
GPT 5.5 Leads Landing Page Design
AI smart glasses · Award-style · With Three.js
- GPT 5.5: Best overall. Animated banners, multicolored cursor, meaningful whitespace, competent Three.js on second pass. Winner.
- Opus 4.8: OK first pass (dark theme, interactive glasses hover) but clipped text and basic HTML. Second pass with Three.js added motion but AI-generated feel remained.
- GLM 5.2: First pass was a complete failure — barely functional. Second pass recovered to a usable layout but odd glasses geometry and clipped text.
Verdict: GPT 5.5 best overall. Even frontier models struggle with visual design — AI-slop aesthetic is hard to escape.
Source 1: Chase AI video Test 2
DeepSWE Bench Quantifies the Gap
113 tasks · Typescript, Go, Python, JS, Rust · Program-based verifiers
| Model | Effort | Score | Cost/Task |
|---|
| GLM 5.2 | Max | 44% | $3.92 |
| Opus 4.8 | Medium | 49% | $3.44 |
| Opus 4.8 | Max | 59% | $13.00 |
| GPT 5.5 | Medium | 54% | $2.75 |
| GPT 5.5 | Extra High | 67% | $7.23 |
Key finding: Frontier models at Medium effort outperform GLM at Max while costing less. The head-to-head gap is clear — GLM is both less capable and less efficient.
Source 1: deepswe.datacurve.ai/blog/deepswe
Open-Weight ≠ Accessible
The Ollama misconception
- Not locally runnable: GLM 5.2 is ~1 trillion parameters. Requires enterprise GPU clusters or cloud API access.
- Open-weight != open-source in practice: Weights are public for inspection, but inference requires the same infrastructure as proprietary models.
- Same deployment model: You access GLM 5.2 through cloud APIs (OpenRouter, HuggingFace) — exactly how you use Opus and GPT.
- No self-hosting for average users: The "download and run" narrative doesn't apply to models at this scale.
Reality check: The hype around "open source GLM being super cheap" ignores that it requires the same cloud infrastructure — and costs more per task.
Source 1: Chase AI video analysis
Effort Level Economics
Subsidized plans flip the math
- Test setup mirrored real usage: Codex (GPT 5.5) on Extra High, Open Code (GLM 5.2) on Extra High, Claude Code (Opus 4.8) on High.
- Subsidized pricing: Anthropic Max and OpenAI $200/month plans make per-task costs ~10x cheaper than raw API pricing.
- GLM has no subsidized tier: Every task is paid at API rates via OpenRouter — no subscription option to bring costs down.
- Enterprise scaling: For teams with high volume, the cost argument shifts further as subsidized plans create a ceiling on per-task spend.
Bottom line: "There kind of isn't a debate" — the creator's conclusion is that for average users on subsidized plans, GLM 5.2 simply doesn't make sense.
Source 1: Chase AI video Results section
Risks, Gaps & Uncertainty
- Single-source findings: Results derived from one YouTube creator's methodology, not a controlled academic benchmark (beyond DeepSWE).
- Subjective grading: The creator acknowledges his grading as "extremely subjective" personal preference, not objective metrics.
- Limited domain coverage: Only 2 task types tested (browser game + landing page). Does not cover data analysis, scientific reasoning, or writing.
- Influencer bias: The host promotes a paid Claude Code masterclass — may introduce subconscious bias toward Opus / Claude Code.
- Transcript artifacts: Auto-generated YouTube captions may contain errors in technical terminology and numerical comparisons.
Recommended Next Actions
- 1
Test GLM 5.2 on simple tasks. Well-scoped jobs (classification, short-form gen) may benefit from its lower per-token rate without the 10x token blowup.
- 2
Default Opus 4.8 for coding. Opus consistently outperformed on game dev. Use GPT 5.5 for design/creative work. Leverage subsidized plans.
- 3
Monitor GLM's evolution. If a future version halves token usage while maintaining quality, the economics could shift for specific use cases.
- 4
Run your own benchmarks. Test GLM vs your current model on 2-3 typical brief/pipeline jobs to get a personal data point before changing workflows.
Sources & Process Provenance
1 source · 1 Tier 3
Tier guide: Tier 1 = Primary institutional source (official website or announcement) · Tier 2 = Company-owned media or trade publication · Tier 3 = Breaking news / single-source — verify independently before citing
Sources: YouTube video content. Transcripts auto-extracted. Verify key technical claims independently.
Generated June 22, 2026 · deepseek/deepseek-v4-flash via OpenRouter (ZDR) · economy routing · Gambit / brief-to-slides v2.0