Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.2 vs GPT-5 benchmark

Name: GLM-5.2 vs GPT-5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.2 (Zhipu) and GPT-5 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GPT-5 is ahead by 66 points (1,558 vs 1,492; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GLM-5.2 resolves 69.3% versus 68.4% (seed-bootstrap (Aug 1, 2026)). GLM-5.2 streams faster (84 tok/s) while GLM-5.2 is the cheaper output token ($0.97/1M). GLM-5.2 has the larger context window (1M 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-5.2

Zhipu flagship. Strong Chinese/English coding and agents.

Elo 1,492$0.9680000000000001/1M out

OpenAI

GPT-5

OpenAI flagship reasoning model for 2025–26. Strong general preference Elo and multimodal coverage.

Elo 1,558$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-5.2 (6)vsGPT-5 (4)

GLM-5.2: 6W (60%)Overall: GLM-5.2GPT-5: 4W (40%)

← GLM-5.2GPT-5 →

GPT-5 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs1,558

Coding Elo

1,504vs1,540

SWE-bench

69.3%vs68.4%

LiveBench

65.2%vs69.8%

GPQA Diamond

79.5%vs85.2%

GLM-5.2: 1WGPT-5: 4W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs78 tok/s

Time to first token

270 msvs290 ms

GLM-5.2: 2WGPT-5: 0W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$10/1M

Input price

$0.31/1Mvs$1.25/1M

Context window

1Mvs400k

GLM-5.2: 3WGPT-5: 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 AdvantageEven 3–3

Preference Elo

GPT-5

66 pts advantage

Throughput Speed

GLM-5.2

6 tok/s faster

Price Efficiency

GLM-5.2

$9.03/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,492

EloHigh is better

1,558+4%

Code Elo

High is better

1,504

Code EloHigh is better

1,540+2%

LiveBench

High is better

65.2%

LiveBenchHigh is better

69.8%+7%

SWE-bench

High is better

+1%69.3%

SWE-benchHigh is better

68.4%

GPQA

High is better

79.5%

GPQAHigh is better

85.2%+7%

TTFT

Low is better

+7%270 ms

TTFTLow is better

290 ms

Speed

High is better

+8%84 tok/s

SpeedHigh is better

78 tok/s

In $

Low is better

+306%$0.31/1M

In $Low is better

$1.25/1M

Out $

Low is better

+933%$0.97/1M

Out $Low is better

$10/1M

Context

High is better

+162%1M

ContextHigh is better

400k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 87% with GLM-5.2

Monthly Volume50M tokens/mo

Quick Presets:

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

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

GLM-5.2$25.30 / mo

In: $10.78Out: $14.52

GPT-5$193.75 / mo

In: $43.75Out: $150.00

Estimated Cost Delta

GLM-5.2 is estimated to save $168.45/month ($2,021/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.2:Pick GLM-5.2 when repo-level coding accuracy is the constraint.
2
GLM-5.2:Pick GLM-5.2 when time-to-first-token matters more than peak Elo.
3
GLM-5.2:Pick GLM-5.2 for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatGLM-5.2

Lower output list price ($0.97/1M).

Voice / low-latency UIGLM-5.2

Lower TTFT (270 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGPT-5

Both accept images. Defaulting to the higher-Elo side (GPT-5).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	GLM-5.2	Lower output list price ($0.97/1M).
Voice / low-latency UI	GLM-5.2	Lower TTFT (270 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GPT-5	Both accept images. Defaulting to the higher-Elo side (GPT-5).

Related Editorial Dispatches & Benchmark Notes

View all news

newsAug 16, 2026

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 undercuts GLM 5V Turbo by 74.2% on input at $0.31/1M, with 1,492 Elo and 69.3% SWE-bench in dated 2026 snapshots.

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.

launchAug 8, 2025

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Still a common compare target. 5.6 Sol replaced it as the buy; GPT-5 stays for old URLs.

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LIVE

Zhipu

GLM-5.2

Elo 1,492

LiveBench65.2%

SWE-bench69.3%

Speed84 tok/s

Output cost$0.97/1M

OpenAI

GPT-5

Elo 1,558

LiveBench69.8%

SWE-bench68.4%

Speed78 tok/s

Output cost$10/1M

Frequently Asked Questions

Which is better overall, GLM-5.2 or GPT-5?: GPT-5 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-5.2 or GPT-5?: GLM-5.2 leads SWE-bench at 69.3% vs 68.4%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: GLM-5.2 output tokens are $0.97/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.2 or GPT-5?: GLM-5.2 has the lower time-to-first-token (270 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GLM-5.2 accepts 1M tokens versus 400k.
Can I self-host either model?: GLM-5.2 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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-5.2 and GPT-5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GLM-5.2 matchups →|All GPT-5 matchups →

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

GLM-5.2 vs GPT-5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.2 (6)vsGPT-5 (4)

GLM-5.2: 6W (60%)Overall: GLM-5.2GPT-5: 4W (40%)

← GLM-5.2GPT-5 →

GPT-5 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs1,558

Coding Elo

1,504vs1,540

SWE-bench

69.3%vs68.4%

LiveBench

65.2%vs69.8%

GPQA Diamond

79.5%vs85.2%

GLM-5.2: 1WGPT-5: 4W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs78 tok/s

Time to first token

270 msvs290 ms

GLM-5.2: 2WGPT-5: 0W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$10/1M

Input price

$0.31/1Mvs$1.25/1M

Context window

1Mvs400k

GLM-5.2: 3WGPT-5: 0W

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatGLM-5.2

Lower output list price ($0.97/1M).

Voice / low-latency UIGLM-5.2

Lower TTFT (270 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGPT-5

Both accept images. Defaulting to the higher-Elo side (GPT-5).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	GLM-5.2	Lower output list price ($0.97/1M).
Voice / low-latency UI	GLM-5.2	Lower TTFT (270 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GPT-5	Both accept images. Defaulting to the higher-Elo side (GPT-5).

Frequently Asked Questions

Which is better overall, GLM-5.2 or GPT-5?

GPT-5 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-5.2 or GPT-5?

GLM-5.2 leads SWE-bench at 69.3% vs 68.4%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

GLM-5.2 output tokens are $0.97/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.2 or GPT-5?

GLM-5.2 has the lower time-to-first-token (270 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.2 accepts 1M tokens versus 400k.

Can I self-host either model?

GLM-5.2 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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-5.2 and GPT-5 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

GPT-5

GLM-5.2

GLM-5.2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Cast Your Matchup Vote

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CompareLLM Matrix

GLM-5.2

GPT-5

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

GPT-5

GLM-5.2

GLM-5.2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.2

GPT-5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons