GLM 5.3 vs Claude Opus 5.5
Claude Opus 5.5 scores higher on 3 of the 3 benchmarks both models publish. The widest gap is on Terminal-Bench 4.0, where Claude Opus 5.5 leads by 24.5 points. GLM 5.3 has open weights, so unlike Claude Opus 5.5 you can run it on your own GPU with no per-token fees.
Shared benchmarks
Raw published scores; the higher score on each row is highlighted. LMArena Elo is a human-preference vote and is not counted as a win.
- Humanity's Last Exam42.364.4
no tools, text-only subset (2,158 q); competitor comparison columnHumanity's Last Exam, no tools; max effort
- Humanity's Last Exam (with tools)62.567.7
with toolsHumanity's Last Exam, with tools (web search/fetch, code execution); max effort
- Terminal-Bench 4.041.966.4
Terminal-Bench 4, Max, mini-swe-agent harnessTerminal-Bench 4.0; xhigh effort (66.36% in text, safeguards enabled)
- LMArena Text Elo · not counted14811509
GLM-5.3 (Max), ±5, board dated 2026-10-27LMArena Text, Overall; snapshot Oct 27, 2026; Claude Opus 5.5 (High); +/-7
Should you pick GLM 5.3 or Claude Opus 5.5?
Pick GLM 5.3 if…
- You need to self-host: keep prompts and data on your own GPU, avoid per-token fees, or fine-tune the weights.
- Cost matters: $4.40 per million output tokens against $20.00.
Pick Claude Opus 5.5 if…
- You care most about Humanity's Last Exam, Humanity's Last Exam (with tools), Terminal-Bench 4.0, where it scores at least 2 points higher.
- You want a managed API and would rather not run or pay for GPUs yourself.
- People prefer its answers in blind chats: LMArena Elo 1509 against 1481.
Hardware and cost to run
GLM 5.3
Even at 4-bit it needs about 520 GB (4× H200 (564 GB)), well beyond a home workstation, so most people run it on rented multi-GPU servers. Full BF16 precision needs about 1.7 TB (Multiple nodes (over 1.1 TB)).
Claude Opus 5.5
Claude Opus 5.5 is only available through Anthropic's API, so it cannot be self-hosted; it costs $4 per million input tokens and $20 per million output tokens.
Category profile
Category index uses every test each model reports, not only the shared ones.
Specs side by side
| GLM 5.3 | Claude Opus 5.5 | |
|---|---|---|
| Lab | Z.ai (Zhipu) | Anthropic |
| Weights | Open | API only |
| Released | 2026-08-14 | 2026-09-22 |
| Parameters | 753B total, 40B active (MoE) | — |
| Context | 1M | 1M |
| License | GLM-5.3 License (custom; arena.ai lists MIT) | — |
| 4-bit memory | 520 GB · 4× H200 (564 GB) | — |
| API price | $1.40 in / $4.40 out per 1M tokens | $4.00 in / $20.00 out per 1M tokens |
| API speed | 88 tok/s | 96 tok/s |
Related comparisons
Sources
| Model | Test | Score | Setting | Source |
|---|---|---|---|---|
| GLM 5.3 | Humanity's Last Exam | 42.3 | no tools, text-only subset (2,158 q); competitor comparison column | MiniMax M3.1 Flash model card (comparison column) |
| Claude Opus 5.5 | Humanity's Last Exam | 64.4 | Humanity's Last Exam, no tools; max effort | Anthropic Claude Opus 5.5 System Card (Table 8.1.A) |
| GLM 5.3 | Humanity's Last Exam (with tools) | 62.5 | with tools | Hugging Face model card (GLM-5.3) |
| Claude Opus 5.5 | Humanity's Last Exam (with tools) | 67.7 | Humanity's Last Exam, with tools (web search/fetch, code execution); max effort | Anthropic Claude Opus 5.5 System Card (Table 8.1.A); also in launch post |
| GLM 5.3 | Terminal-Bench 4.0 | 41.9 | Terminal-Bench 4, Max, mini-swe-agent harness | Hugging Face model card (GLM-5.3), evaluation results |
| Claude Opus 5.5 | Terminal-Bench 4.0 | 66.4 | Terminal-Bench 4.0; xhigh effort (66.36% in text, safeguards enabled) | Anthropic Claude Opus 5.5 System Card (Table 8.1.A / sec 8.5) |
| GLM 5.3 | LMArena Text Elo | 1481 | GLM-5.3 (Max), ±5, board dated 2026-10-27 | LMArena (arena.ai) text leaderboard |
| Claude Opus 5.5 | LMArena Text Elo | 1509 | LMArena Text, Overall; snapshot Oct 27, 2026; Claude Opus 5.5 (High); +/-7 | LMArena (arena.ai) Text leaderboard |
Scores are published results from model cards, lab reports, and public leaderboards, each linked to its source. Settings such as thinking mode or test version differ between sources and are shown next to each score. OpenLLM Buddy catalog models are uncensored fine-tunes; their scores are those of the official base model. Data collected 2026-10-30. How the index works