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Model comparison

GLM 5.3 vs GPT-5.5

Z.ai against OpenAI, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Z.ai

GLM 5.3

Frontier · Open weights

vs

OpenAI

GPT-5.5

Frontier

AI model capability comparison
SpecificationGLM 5.3GPT-5.5
ProviderZ.aiOpenAI
TierFrontierFrontier
Context window1MWinner: 1.05M
Max outputWinner: 131K128K
Input / 1M tokensWinner: $1.40$5
Output / 1M tokensWinner: $4.40$30
WeightsOpenClosed
Parameters753B total (MoE; active count not verified)Not verifiedUnverified
Reasoning levelslow, high, maxnone, low, medium, high, xhigh
Modalitiestexttext, image
LicenseGLM-5.3 License (custom)Not verifiedUnverified
API model idglm-5.3gpt-5.5
ReleasedAugust 14, 2026April 23, 2026
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-0145Not verifiedUnverified
Terminal-Bench 2.1 · source date GLM 5.3: 2026-08-14, GPT-5.5: 2026-07-27 (comparability not verified)88.283.4
DeepSWE 1.1 · source date GLM 5.3: 2026-08-1466.9Not verifiedUnverified
Humanity's Last Exam · source date GLM 5.3: 2026-08-14, GPT-5.5: 2026-04-23 (comparability not verified)62.552.2
GPQA Diamond · source date GPT-5.5: 2026-07-27Not verifiedUnverified93.5

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-10-01
    Artificial Analysis (GLM-5.3 (max))

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.

  • Source date2026-08-14
    Z.ai GLM-5.3 blog + HF model card (vendor, with tools, full set)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

  • Source date2026-08-14
    Z.ai GLM-5.3 blog + HF model card (vendor, mini-swe-agent family)

    Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.

    Directly comparable

  • Source date2026-08-14
    Z.ai GLM-5.3 blog + HF model card eval results (vendor, Claude Code harness)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-07-27
    Kimi K3 model card (vendor-reported, xhigh); announcement charts show 93.6 — minor conflict preserved

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-07-27
    Kimi K3 model card (vendor-reported, xhigh, Codex harness; orig OpenAI)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-04-23
    OpenAI — Introducing GPT-5.5 (vendor, with tools; 41.4 no tools)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

Pricing tiers

GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

GPT-5.5: Standard $5/$30 per MTok; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible.

FrontierOpen weightsRecord checked October 2, 2026

GLM 5.3

Z.ai's text-only GLM flagship, with a 753B-total MoE checkpoint and always-enabled reasoning. Published benchmark figures identify particular harnesses and settings.

Best for

  • Coding Plan subscribers
  • Long-horizon coding
  • Chinese + English

Watch out

Custom licence: model-as-a-service operators whose licensee/affiliate aggregate revenue exceeds $10B in any consecutive 12 months require Z.AI security review before commercial use; the definition excludes embedded end-user features/harnesses and mere relays. Keep attribution/permission notices. Reasoning defaults to max; disabling it fails. Accounts that ever subscribed to Coding Plan, including expired subscriptions, currently have Model API access only through the Chat Completions-compatible protocol. Active parameter count and exact initial weight-publication day remain unverified.

FrontierRecord checked October 1, 2026

GPT-5.5

OpenAI’s April 2026 model for reasoning, coding and professional work, with a 1.05M-token context. Retained benchmark scores identify their source and configuration.

Best for

  • Reasoning
  • Agentic coding
  • Long-context work

Watch out

An older model, still listed in the API. GPT-5.5 retires from ChatGPT, Work and Codex on October 14, 2026; that notice does not affect the OpenAI API.

When the cheaper one wins

GLM 5.3 is cheaper on output at $4.40 per million tokens against $30 for GPT-5.5 — about 6.8×. 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. On DeepSWE 1.1, GLM 5.3 is 69% Pass@1 at $3.99/task versus GPT-5.5 at 67% / $7.23/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

Run the model picker

Evidence confidence: Medium

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.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 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.

Related comparisons

Diving deeper on one model? GLM 5.3 · GPT-5.5

Common questions

GLM 5.3 vs GPT-5.5

Answered from the verified figures on this page rather than general guidance.

Is GLM 5.3 or GPT-5.5 cheaper for input?
GLM 5.3 is cheaper at $1.40 per million input tokens, against $5 for GPT-5.5 — roughly 3.6× the price. Estimate both input and output token volume for your workload. These are base rates; GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling. GPT-5.5: Standard $5/$30 per MTok; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible.
Is GLM 5.3 or GPT-5.5 cheaper for output?
GLM 5.3 is cheaper at $4.40 per million output tokens, against $30 for GPT-5.5 — roughly 6.8× the price. Estimate both input and output token volume for your workload. These are base rates; GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling. GPT-5.5: Standard $5/$30 per MTok; prompts above 272K bill 2x input and 1.5x output for the full session in Standard/Batch/Flex. Cached input $0.50; short-context Batch $2.50/$15. Regional/FedRAMP processing adds 10% where eligible.
Which has the larger context window, GLM 5.3 or GPT-5.5?
GPT-5.5 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 support the same reasoning levels?
GLM 5.3 exposes low, high, max, while GPT-5.5 exposes none, low, medium, high, xhigh.
Should I use GLM 5.3 or GPT-5.5?
Both are classified in our frontier tier; this editorial grouping does not imply equal capability. GLM 5.3 is a candidate for coding plan subscribers; GPT-5.5 is a candidate for reasoning. Compare accuracy, tool reliability, latency and cost on representative tasks.
Can I self-host GLM 5.3 or GPT-5.5?
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 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 $5 for GPT-5.5 — a 3.6x difference at the headline tier.
  • Context: GPT-5.5 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.
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