Model comparison · accepted catalogue facts 2026-10-21

GPT-5.5 vs GLM 5.3 Flash

GLM 5.3 Flash has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from GPT-5.5 loses capability or fails a requirement. Review the migration checks. The reported score intervals overlap; this is not an established quality improvement.

Editorial comparison: Direct benchmark rivals: GLM 5.3 Flash (Z.ai) vs GPT-5.5 (OpenAI) with overlapping confidence intervals Inclusion does not recommend either model.

GPT-5.5

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1466.9

Balanced effective USD / 1M tokens · lower is better

$11.25/M

GLM 5.3 Flash

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1471.0

Balanced effective USD / 1M tokens · lower is better

$0.238/M

Price under confirmation since Oct 12, 2026

In-Depth Analysis

GPT-5.5 vs GLM 5.3 Flash: Measured Technical Breakdown

Comprehensive comparison across measured human-preference evaluation, effective production token costs, architectural limits, and migration trade-offs.

Measured Capability (LMArena)

GLM 5.3 Flash leads by 4.1 Elo

GPT-5.5: 1466.9 ±3.65 · GLM 5.3 Flash: 1471 ±4.9 Intervals overlap — not a guaranteed quality advantage

Cost Comparison Across Canonical Production Workloads

Rates represent effective cost per 1M tokens under standard token blends. Cached prompt reads are factored according to workload context reuse.

Workload & Ratio	GPT-5.5	GLM 5.3 Flash	Cost Difference	Advantage
Balanced 3 tokens in per 1 out	$11.25 /1M tokens	$0.238 /1M tokens	$11.01 (98% diff)	GLM 5.3 Flash costs less
Summarise Huge input, tiny output	$4.97 /1M tokens	$0.133 /1M tokens	$4.83 (97% diff)	GLM 5.3 Flash costs less
Chat Long cached system prompt	$13.65 /1M tokens	$0.254 /1M tokens	$13.40 (98% diff)	GLM 5.3 Flash costs less
Code gen Output dominates	$19.64 /1M tokens	$0.350 /1M tokens	$19.29 (98% diff)	GLM 5.3 Flash costs less
Agentic Context replayed each turn	$6.07 /1M tokens	$0.131 /1M tokens	$5.94 (98% diff)	GLM 5.3 Flash costs less

Architectural & Operational Specifications

Specification	GPT-5.5	GLM 5.3 Flash	Operational Note
Context window	1,050,000 tokens	1,048,576 tokens	—
Max output generation	128,000 tokens	943,717 tokens	GLM 5.3 Flash allows longer single-turn completions
Base input price	$5.00 / 1M tokens	$0.150 / 1M tokens	GLM 5.3 Flash input rate is cheaper
Base output price	$30.00 / 1M tokens	$0.500 / 1M tokens	GLM 5.3 Flash output rate is cheaper
Cached input price	$0.500 / 1M tokens	$0.030 / 1M tokens	GLM 5.3 Flash offers lower cached prompt cost
Reasoning & thinking tokens	Supported; standard rates	Supported; standard rates	—
Input modalities	file, image, text	text, image, video	—
Tool calling / function use	Supported	Supported	—
Open weights & licence	Proprietary (proprietary)	Proprietary (API-only)	GLM 5.3 Flash allows independent hosting or local deployment
Provider & reseller market	OpenAI · 7 provider offers (2.2× spread)	Z.ai · 33 provider offers (12.59× spread)	—

When to Choose GPT-5.5

Larger context window (1,050,000 tokens) for long-document or repo-level context.
Direct integration with OpenAI tooling and API features.

Risks when moving from GLM 5.3 Flash → GPT-5.5:

Output limit: 943,717 → 128,000 tokens.
video input is not supported by the candidate.

When to Choose GLM 5.3 Flash

Higher measured general capability (1471 Elo on human evaluation).
Higher maximum completion limit (943,717 tokens).
Direct integration with Z.ai tooling and API features.

Risks when moving from GPT-5.5 → GLM 5.3 Flash:

Context window: 1,050,000 → 1,048,576 tokens.
file input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

GPT-5.5 Full model card, pricing ladder & provider offers → GLM 5.3 Flash Full model card, pricing ladder & provider offers → Alternatives to GPT-5.5 Better-and-cheaper dominance audit → Alternatives to GLM 5.3 Flash Better-and-cheaper dominance audit →

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-21

A · Current or starting modelB · Comparison modelC · Optional third model

2 / 3 selected · 0 requirements

Open public comparison link

Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

Workload Public prompt length (tokens) Required context (tokens) Required output limit (tokens) Required input modality

Tool useReasoningOpen weights

A ↔ B

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

GPT-5.5 · Meets the stated model requirements
GLM 5.3 Flash · Meets the stated model requirements

Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured fact	A GPT-5.5	B GLM 5.3 Flash
LMArena general score (Elo)	1,466.9	1,471Δ vs A: +4.1 Elo
Score interval half-width (± Elo)	3.65	4.9Δ vs A: +1.25 Elo
LMArena document score (Elo)	1,485.7	Unknown
Estimated effective USD / 1M tokens	$11.25	$0.2375Δ vs A: -11.0125 USD/M
Base input USD / 1M tokens	$5	$0.15Δ vs A: -4.85 USD/M
Base output USD / 1M tokens	$30	$0.5Δ vs A: -29.5 USD/M
Base cached-input USD / 1M tokens	$0.5	$0.03Δ vs A: -0.47 USD/M
Price status	As of the catalogue date	Under confirmation since 2026-10-12
Context window (tokens)	1,050,000	1,048,576Δ vs A: -1,424 tokens
Output limit (tokens)	128,000	943,717Δ vs A: +815,717 tokens
Input modalities	file, image, text	text, image, video
Open weights	Not supported	Unknown
Recorded licence	proprietary	Unknown
Model provider	OpenAI	Z.ai
Distinct sellers in the catalogue	3	30Δ vs A: +27
Serving precision differs across offers	Not supported	SupportedΔ vs A: +1
Recorded retirement date	Unknown	Unknown

4 shared measured facts

Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured fact	A GPT-5.5	B GLM 5.3 Flash
Benchmark reasoning effort	default	default
Tool use	Supported	SupportedΔ vs A: 0
Reasoning	Supported	SupportedΔ vs A: 0
Retirement evidence	None announced; not a guarantee	None announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

GPT-5.5

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token threshold	Input	Output	Cached input
Base, up to and including first threshold	$5	$30	$0.5
> 272,000	$10	$45	$1

GLM 5.3 Flash

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token threshold	Input	Output	Cached input
Base, up to and including first threshold	$0.15	$0.5	$0.03

Migration preflight · compare each candidate with model A

Keeping GPT-5.5 is a valid decision. No pairwise comparison certifies a drop-in replacement.

GPT-5.5 → GLM 5.3 Flash

Known loss or failed requirement

Context window: 1,050,000 → 1,048,576 tokens.
file input is not supported by the candidate.
Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.

Check alternatives to GPT-5.5

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

Requests Input tokens per request Output tokens per request Cached share of input (%) Extra attempts per request

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

GPT-5.5Estimated total: $25 1,000 full attempts · base rates
GLM 5.3 FlashEstimated total: $0.55 1,000 full attempts · base rates

Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokens	GPT-5.5	GLM 5.3 Flash	Coverage
2,000	$25	$0.55Lowest known	All computed
4,096	$35.48	$0.8644Lowest known	All computed
32,768	$178.84	$5.1652Lowest known	All computed
131,072	$670.36	$19.9108Lowest known	All computed
271,999	$1,374.995	$41.04985Lowest known	All computed
272,000	$1,375	$41.05Lowest known	All computed
272,001	$2,742.51	$41.05015Lowest known	All computed

Vary output; hold input, requests, cache and retries fixed.
Output tokens	GPT-5.5	GLM 5.3 Flash
128	$13.84	$0.364
2,048	$71.44	$1.324
8,192	$255.76	$4.396

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Import private evaluation JSON

Expected format

[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Owner (private) Review date (private) Recorded choice (private) Rationale (private)

Include private review fields, scenario and observations in downloaded files

Store private decision records on this device

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Import a v2 decision JSON file

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is GPT-5.5 a better-and-cheaper replacement for GLM 5.3 Flash?

Which model is higher rated: GPT-5.5 or GLM 5.3 Flash?

GLM 5.3 Flash scores higher on LMArena general (1471.0 ±4.9) compared to GPT-5.5 (1466.9 ±3.7), a margin of 4.1 Elo points. However, because the 95% confidence intervals overlap, this does not establish an unconditional superiority across all tasks.

Which model is more cost-effective: GPT-5.5 or GLM 5.3 Flash?

On a standard balanced workload (3 prompt tokens per 1 generated token), GLM 5.3 Flash costs $0.238 (price under confirmation since 2026-10-12) per 1M tokens versus $11.25 for GPT-5.5. GPT-5.5 is approximately 47.4× the price of GLM 5.3 Flash. Base input rates are $5.00 for GPT-5.5 and $0.150 for GLM 5.3 Flash; base output rates are $30.00 for GPT-5.5 and $0.500 for GLM 5.3 Flash.

How do their context windows and generation limits compare?

GPT-5.5 provides a 1,050,000-token context window with a maximum output limit of 128,000 tokens. GLM 5.3 Flash provides a 1,048,576-token context window with a maximum output of 943,717 tokens.

What capabilities are lost when switching between GPT-5.5 and GLM 5.3 Flash?

Switching from GPT-5.5 to GLM 5.3 Flash involves: Context window: 1,050,000 → 1,048,576 tokens; file input is not supported by the candidate. Conversely, moving from GLM 5.3 Flash to GPT-5.5 involves: Output limit: 943,717 → 128,000 tokens; video input is not supported by the candidate. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can GPT-5.5 or GLM 5.3 Flash be self-hosted or deployed privately?

Both GPT-5.5 and GLM 5.3 Flash are proprietary cloud models offered via managed API endpoints. GPT-5.5 is served by OpenAI and 7 catalogue offers, while GLM 5.3 Flash is served by Z.ai and 33 catalogue offers.

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

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GPT-5.5 vs GLM 5.3 Flash

GPT-5.5 vs GLM 5.3 Flash: Measured Technical Breakdown

Cost Comparison Across Canonical Production Workloads

Architectural & Operational Specifications

When to Choose GPT-5.5

When to Choose GLM 5.3 Flash

Detailed Evidence Passports & Independent Audits

Choose two or three models

What changes between these models

GPT-5.5

GLM 5.3 Flash

GPT-5.5 → GLM 5.3 Flash

Estimate a fixed request shape

Bring your observed evaluation results

Keep the decision and its evidence

Saved local decisions · 0

Questions this page answers