Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.3 vs GPT-4o benchmark

Name: GLM-5.3 vs GPT-4o AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.3 (Zhipu) and GPT-4o (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 223 points (1,558 vs 1,335; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 54.8% (seed-bootstrap (Aug 16, 2026)). GPT-4o streams faster (110 tok/s) while GLM-5.3 is the cheaper output token ($1.8/1M). GLM-5.3 has the larger context window (200k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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Zhipu

GLM-5.3

Z.ai Aug 14 2026 post-train of the GLM-5.2 744B base. Coding-plan live; open weights promised after a two-week safety review (z.ai/blog/glm-5.3).

Elo 1,558$1.8/1M out

OpenAI

GPT-4o

Previous OpenAI flagship. Still a common compare baseline on legacy pages.

Elo 1,335$10/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (8)vsGPT-4o (2)

GLM-5.3: 8W (80%)Overall: GLM-5.3GPT-4o: 2W (20%)

← GLM-5.3GPT-4o →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,558vs1,335

Coding Elo

1,586vs—

SWE-bench

76.4%vs54.8%

LiveBench

70.8%vs54.2%

GPQA Diamond

83.2%vs73.2%

GLM-5.3: 5WGPT-4o: 0W

GPT-4o (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs110 tok/s

Time to first token

255 msvs180 ms

GLM-5.3: 0WGPT-4o: 2W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$10/1M

Input price

$0.5/1Mvs$2.5/1M

Context window

200kvs128k

GLM-5.3: 3WGPT-4o: 0W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGLM-5.3 5/6

Preference Elo

GLM-5.3

223 pts advantage

Throughput Speed

GPT-4o

20 tok/s faster

Price Efficiency

GLM-5.3

$8.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+17%1,558

EloHigh is better

1,335

Code Elo

High is better

1,586

Code EloHigh is better

—

LiveBench

High is better

+31%70.8%

LiveBenchHigh is better

54.2%

SWE-bench

High is better

+39%76.4%

SWE-benchHigh is better

54.8%

GPQA

High is better

+14%83.2%

GPQAHigh is better

73.2%

TTFT

Low is better

255 ms

TTFTLow is better

180 ms+42%

Speed

High is better

90 tok/s

SpeedHigh is better

110 tok/s+22%

In $

Low is better

+400%$0.5/1M

In $Low is better

$2.5/1M

Out $

Low is better

+456%$1.8/1M

Out $Low is better

$10/1M

Context

High is better

+56%200k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 81% with GLM-5.3

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

GLM-5.3$44.50 / mo

In: $17.50Out: $27.00

GPT-4o$237.50 / mo

In: $87.50Out: $150.00

Estimated Cost Delta

GLM-5.3 is estimated to save $193.00/month ($2,316/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.3:Pick GLM-5.3 when repo-level coding accuracy is the constraint.
2
GPT-4o:Pick GPT-4o when time-to-first-token matters more than peak Elo.
3
GLM-5.3:Pick GLM-5.3 as the default general assistant.

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIGPT-4o

Lower TTFT (180 ms).

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

Both accept images. Defaulting to the higher-Elo side (GLM-5.3).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	GPT-4o	Lower TTFT (180 ms).
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	Both accept images. Defaulting to the higher-Elo side (GLM-5.3).

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 14, 2026

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

Coding-plan live; open weights promised after a two-week safety review. Newest Zhipu row.

launchJun 18, 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Strong Chinese/English coding. 5.3 is the new row; 5.2 stays so the delta is visible.

newsMay 13, 2024

GPT-4o stays as the 2024 compare anchor people still search

Deprecated for buying. Kept because “4o vs sonnet” is still a typed query.

Community Sentiment

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LIVE

Zhipu

GLM-5.3

Elo 1,558

LiveBench70.8%

SWE-bench76.4%

Speed90 tok/s

Output cost$1.8/1M

OpenAI

GPT-4o

Elo 1,335

LiveBench54.2%

SWE-bench54.8%

Speed110 tok/s

Output cost$10/1M

Frequently Asked Questions

Which is better overall, GLM-5.3 or GPT-4o?: GLM-5.3 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, GLM-5.3 or GPT-4o?: GLM-5.3 leads SWE-bench at 76.4% vs 54.8%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: GLM-5.3 output tokens are $1.8/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.3 or GPT-4o?: GPT-4o has the lower time-to-first-token (180 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GLM-5.3 accepts 200k tokens versus 128k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.3 and GPT-4o Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GLM-5.3 matchups →|All GPT-4o matchups →

GLM-5.3 vs GLM-5.2 (previous glm)GPT-4o vs GPT-4 Turbo (previous gpt-flagship)GPT-4o vs OpenAI: o1 (next gpt-flagship)GPT-5 vs GLM-5.3 GPT-5.6 Terra vs GLM-5.3 GPT-4.5 Orion vs GLM-5.3

GLM-5.3 vs GPT-4o benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (8)vsGPT-4o (2)

GLM-5.3: 8W (80%)Overall: GLM-5.3GPT-4o: 2W (20%)

← GLM-5.3GPT-4o →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,558vs1,335

Coding Elo

1,586vs—

SWE-bench

76.4%vs54.8%

LiveBench

70.8%vs54.2%

GPQA Diamond

83.2%vs73.2%

GLM-5.3: 5WGPT-4o: 0W

GPT-4o (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs110 tok/s

Time to first token

255 msvs180 ms

GLM-5.3: 0WGPT-4o: 2W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$10/1M

Input price

$0.5/1Mvs$2.5/1M

Context window

200kvs128k

GLM-5.3: 3WGPT-4o: 0W

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIGPT-4o

Lower TTFT (180 ms).

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

Both accept images. Defaulting to the higher-Elo side (GLM-5.3).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	GPT-4o	Lower TTFT (180 ms).
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	Both accept images. Defaulting to the higher-Elo side (GLM-5.3).

Frequently Asked Questions

Which is better overall, GLM-5.3 or GPT-4o?

GLM-5.3 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GLM-5.3 or GPT-4o?

GLM-5.3 leads SWE-bench at 76.4% vs 54.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

GLM-5.3 output tokens are $1.8/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.3 or GPT-4o?

GPT-4o has the lower time-to-first-token (180 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.3 accepts 200k tokens versus 128k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. GLM-5.3 and GPT-4o Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.3

GPT-4o

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

GPT-4o stays as the 2024 compare anchor people still search

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

GLM-5.3

GPT-4o

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.3

GPT-4o

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

GPT-4o stays as the 2024 compare anchor people still search

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.3

GPT-4o

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons