Model comparison
GLM 5.3 vs GPT-5.5 Pro
Z.ai against OpenAI, compared on context, price, and verified benchmark results.
Catalog record checked September 3, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
Z.ai
GLM 5.3
Frontier · Open weights
OpenAI
GPT-5.5 Pro
Frontier
| Specification | GLM 5.3 | GPT-5.5 Pro |
|---|---|---|
| Provider | Z.ai | OpenAI |
| Tier | Frontier | Frontier |
| Context window | 1M | Winner: 1.05M |
| Max output | 128K | 128K |
| Input / 1M tokens | Winner: $1.40 | $30 |
| Output / 1M tokens | Winner: $4.40 | $180 |
| Weights | Open | Closed |
| Parameters | 753B total (MoE; active count unpublished) | Not disclosedUnverified |
| Reasoning levels | low, high, max | none, low, medium, high, xhigh, max |
| Modalities | text | text, image |
| License | glm-5.3 (custom) | Not disclosedUnverified |
| API model id | glm-5.3 | gpt-5.5-pro |
| Released | August 14, 2026 | April 23, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-14) | 60 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [xhigh] (2026-08-14) | Not verifiedUnverified | 60 |
| Terminal-Bench 2.1 (2026-08-14) | 88.2 | Not verifiedUnverified |
| DeepSWE 1.1 (2026-08-14) | 66.9 | Not verifiedUnverified |
| Humanity's Last Exam (2026-08-14) | 62.5 | Not verifiedUnverified |
| SWE-bench Verified (2026-09-01) | 95.4 | Not verifiedUnverified |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); Zhipu docs list 77.8 inherited from GLM-5 — attribution contested, both notedReal GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Z.ai GLM-5.3 blog + HF model card (vendor, with tools, full set)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
Pricing tiers: GLM 5.3: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). · GPT-5.5 Pro: Standard $30/$180 per MTok; no cached-input discount. Batch $15/$90.
GLM 5.3
GLM 5.3 is Zhipu's flagship (~753B MoE), near the top of the leaderboards, and the current GLM Coding Plan default.
Best for
- Coding Plan subscribers
- Long-horizon coding
- Chinese + English
Watch out
Open weights dropped 2026-08-28 under Z.ai's custom glm-5.3 licence (not a standard open-source licence — review before commercial use; secondary coverage says >$10B-revenue providers need a security review). 5.2/5.1 Coding Plan requests route to 5.3.
GPT-5.5 Pro
GPT-5.5 Pro is OpenAI's highest-effort tier for maximal accuracy on the hardest tasks.
Best for
- Maximum-accuracy reasoning
- Research
- Hard enterprise workloads
Watch out
At $30/$180 per MTok it is the most expensive OpenAI tier; reserve for work that needs it.
When the cheaper one wins
GLM 5.3 is cheaper on output at $4.40 per million tokens against $180 for GPT-5.5 Pro — about 41×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- 1 shared named benchmark (scores match) — Measured on: Artificial Analysis Intelligence Index — scores are equivalent, so the benchmark does not separate the pair.
- Verified within the last 90 days — Newest catalog check was 8 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- GLM 5.3: Z.ai GLM-5.3 announcement (accessed 2026-08-29)
- GLM 5.3: Hugging Face — zai-org/GLM-5.3 (weights, 2026-08-28) (accessed 2026-08-30)
- GPT-5.5 Pro: OpenAI — Introducing GPT-5.5 (accessed 2026-08-29)
- GPT-5.5 Pro: OpenAI API pricing (gpt-5.5-pro $30/$180) (accessed 2026-08-29)
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Diving deeper on one model? GLM 5.3 · GPT-5.5 Pro
Common questions
GLM 5.3 vs GPT-5.5 Pro
Answered from the verified figures on this page rather than general guidance.
Is GLM 5.3 or GPT-5.5 Pro cheaper for input?
GLM 5.3 is cheaper at $1.40 per million input tokens, against $30 for GPT-5.5 Pro — roughly 21× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 has tiered pricing: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). GPT-5.5 Pro has tiered pricing: Standard $30/$180 per MTok; no cached-input discount. Batch $15/$90.
Is GLM 5.3 or GPT-5.5 Pro cheaper for output?
GLM 5.3 is cheaper at $4.40 per million output tokens, against $180 for GPT-5.5 Pro — roughly 41× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 has tiered pricing: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). GPT-5.5 Pro has tiered pricing: Standard $30/$180 per MTok; no cached-input discount. Batch $15/$90.
Which has the larger context window, GLM 5.3 or GPT-5.5 Pro?
GPT-5.5 Pro accepts 1.05M tokens against 1M for GLM 5.3. This only matters if you routinely send very long documents or large codebases.
Do GLM 5.3 and GPT-5.5 Pro support the same reasoning levels?
GLM 5.3 exposes low, high, max, while GPT-5.5 Pro exposes none, low, medium, high, xhigh, max.
Should I use GLM 5.3 or GPT-5.5 Pro?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.3 suits coding plan subscribers; GPT-5.5 Pro suits maximum-accuracy reasoning.
Can I self-host GLM 5.3 or GPT-5.5 Pro?
GLM 5.3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.5 Pro is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: GLM 5.3 costs $1.40 per 1M tokens versus $30 for GPT-5.5 Pro — a 21.4x difference at the headline tier.
- Context: GPT-5.5 Pro takes 1.05M against 1M for GLM 5.3 — only decisive if your prompts approach the smaller window.
- Deployment: GLM 5.3 publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.