Pairwise benchmark snapshot · Aug 16, 2026

Gemini 1.5 Pro vs GLM-5.3 benchmark

Name: Gemini 1.5 Pro vs GLM-5.3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and GLM-5.3 (Zhipu) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 298 points (1,558 vs 1,260; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 38% (seed-bootstrap (Aug 16, 2026)). GLM-5.3 streams faster (90 tok/s) while GLM-5.3 is the cheaper output token ($1.8/1M). Gemini 1.5 Pro has the larger context window (2M 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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Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M out

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

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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.

Gemini 1.5 Pro (2)vsGLM-5.3 (8)

Gemini 1.5 Pro: 2W (20%)Overall: GLM-5.3GLM-5.3: 8W (80%)

← Gemini 1.5 ProGLM-5.3 →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,558

Coding Elo

—vs1,586

SWE-bench

38%vs76.4%

LiveBench

49.2%vs70.8%

GPQA Diamond

58%vs83.2%

Gemini 1.5 Pro: 0WGLM-5.3: 5W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs90 tok/s

Time to first token

240 msvs255 ms

Gemini 1.5 Pro: 1WGLM-5.3: 1W

GLM-5.3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$1.8/1M

Input price

$1.25/1Mvs$0.5/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 1WGLM-5.3: 2W

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

298 pts advantage

Throughput Speed

GLM-5.3

20 tok/s faster

Price Efficiency

GLM-5.3

$3.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,558+24%

Code Elo

High is better

—

Code EloHigh is better

1,586

LiveBench

High is better

49.2%

LiveBenchHigh is better

70.8%+44%

SWE-bench

High is better

38%

SWE-benchHigh is better

76.4%+101%

GPQA

High is better

58%

GPQAHigh is better

83.2%+43%

TTFT

Low is better

+6%240 ms

TTFTLow is better

255 ms

Speed

High is better

70 tok/s

SpeedHigh is better

90 tok/s+29%

In $

Low is better

$1.25/1M

In $Low is better

$0.5/1M+150%

Out $

Low is better

$5/1M

Out $Low is better

$1.8/1M+178%

Context

High is better

+900%2M

ContextHigh is better

200k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 63% 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)

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75.00

GLM-5.3$44.50 / mo

In: $17.50Out: $27.00

Estimated Cost Delta

GLM-5.3 is estimated to save $74.25/month ($891/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
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

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 UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

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	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
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.

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LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

Zhipu

GLM-5.3

Elo 1,558

LiveBench70.8%

SWE-bench76.4%

Speed90 tok/s

Output cost$1.8/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or GLM-5.3?: 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, Gemini 1.5 Pro or GLM-5.3?: GLM-5.3 leads SWE-bench at 76.4% vs 38%. 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 $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or GLM-5.3?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 200k.
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. Gemini 1.5 Pro and GLM-5.3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 1.5 Pro matchups →|All GLM-5.3 matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)GLM-5.3 vs GLM-5.2 (previous glm)Gemini 3 Pro vs GLM-5.3 Gemini 3 Flash vs GLM-5.3 Gemini 2.0 Flash Thinking vs GLM-5.3 Gemini 3.6 Pro vs GLM-5.3

Gemini 1.5 Pro vs GLM-5.3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (2)vsGLM-5.3 (8)

Gemini 1.5 Pro: 2W (20%)Overall: GLM-5.3GLM-5.3: 8W (80%)

← Gemini 1.5 ProGLM-5.3 →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,558

Coding Elo

—vs1,586

SWE-bench

38%vs76.4%

LiveBench

49.2%vs70.8%

GPQA Diamond

58%vs83.2%

Gemini 1.5 Pro: 0WGLM-5.3: 5W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs90 tok/s

Time to first token

240 msvs255 ms

Gemini 1.5 Pro: 1WGLM-5.3: 1W

GLM-5.3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$1.8/1M

Input price

$1.25/1Mvs$0.5/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 1WGLM-5.3: 2W

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 UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

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	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	GLM-5.3	Both accept images. Defaulting to the higher-Elo side (GLM-5.3).

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or GLM-5.3?

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, Gemini 1.5 Pro or GLM-5.3?

GLM-5.3 leads SWE-bench at 76.4% vs 38%. 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 $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or GLM-5.3?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 200k.

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. Gemini 1.5 Pro and GLM-5.3 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

GLM-5.3

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

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CompareLLM Matrix

Gemini 1.5 Pro

GLM-5.3

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

GLM-5.3

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 1.5 Pro

GLM-5.3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons