Pairwise benchmark snapshot · Aug 16, 2026

Gemini 3.6 Pro vs GLM-5.3 benchmark

Name: Gemini 3.6 Pro vs GLM-5.3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 3.6 Pro (Google) and GLM-5.3 (Zhipu) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Gemini 3.6 Pro is ahead by 12 points (1,570 vs 1,558; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 74.6% (seed-bootstrap (Aug 16, 2026)). Gemini 3.6 Pro streams faster (102 tok/s) while GLM-5.3 is the cheaper output token ($1.8/1M). Gemini 3.6 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 3.6 Pro

Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.

Elo 1,570$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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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 3.6 Pro (6)vsGLM-5.3 (4)

Gemini 3.6 Pro: 6W (60%)Overall: Gemini 3.6 ProGLM-5.3: 4W (40%)

← Gemini 3.6 ProGLM-5.3 →

Gemini 3.6 Pro (3/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs1,558

Coding Elo

1,566vs1,586

SWE-bench

74.6%vs76.4%

LiveBench

71.8%vs70.8%

GPQA Diamond

84.4%vs83.2%

Gemini 3.6 Pro: 3WGLM-5.3: 2W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs90 tok/s

Time to first token

205 msvs255 ms

Gemini 3.6 Pro: 2WGLM-5.3: 0W

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 3.6 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 AdvantageGemini 3.6 Pro 4/6

Preference Elo

Gemini 3.6 Pro

12 pts advantage

Throughput Speed

Gemini 3.6 Pro

12 tok/s faster

Price Efficiency

GLM-5.3

$3.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+1%1,570

EloHigh is better

1,558

Code Elo

High is better

1,566

Code EloHigh is better

1,586+1%

LiveBench

High is better

+1%71.8%

LiveBenchHigh is better

70.8%

SWE-bench

High is better

74.6%

SWE-benchHigh is better

76.4%+2%

GPQA

High is better

+1%84.4%

GPQAHigh is better

83.2%

TTFT

Low is better

+24%205 ms

TTFTLow is better

255 ms

Speed

High is better

+13%102 tok/s

SpeedHigh is better

90 tok/s

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 3.6 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 3.6 Pro:Pick Gemini 3.6 Pro when time-to-first-token matters more than peak Elo.
3
Gemini 3.6 Pro:Pick Gemini 3.6 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 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

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 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

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.

compareAug 13, 2026

Gemini Flash vs Pro: when Flash is the whole product

3.7 Flash vs 3.6 Pro is the current Google volume-versus-upgrade pair.

compareJul 24, 2026

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Top Anthropic vs top Google. Usually Opus 5 vs 3.6 Pro. Two different invoices.

compareJul 16, 2026

Gemini vs GPT: long context versus the OpenAI flagship invoice

Hub remaps to top Google vs top OpenAI Elo. Usually 3.6 Pro vs Sol. Window vs price is the plot.

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LIVE

Google

Gemini 3.6 Pro

Elo 1,570

LiveBench71.8%

SWE-bench74.6%

Speed102 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 3.6 Pro or GLM-5.3?: Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 3.6 Pro or GLM-5.3?: GLM-5.3 leads SWE-bench at 76.4% vs 74.6%. 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 3.6 Pro or GLM-5.3?: Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 3.6 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 3.6 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 3.6 Pro matchups →|All GLM-5.3 matchups →

Gemini 3.6 Pro vs Gemini 3 Pro (previous gemini-pro)GLM-5.3 vs GLM-5.2 (previous glm)Gemini 3 Pro vs GLM-5.3 Gemini 3.7 Flash vs GLM-5.3 Gemini 3.6 Flash vs GLM-5.3 Gemini 3 Flash vs GLM-5.3

Gemini 3.6 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 3.6 Pro (6)vsGLM-5.3 (4)

Gemini 3.6 Pro: 6W (60%)Overall: Gemini 3.6 ProGLM-5.3: 4W (40%)

← Gemini 3.6 ProGLM-5.3 →

Gemini 3.6 Pro (3/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs1,558

Coding Elo

1,566vs1,586

SWE-bench

74.6%vs76.4%

LiveBench

71.8%vs70.8%

GPQA Diamond

84.4%vs83.2%

Gemini 3.6 Pro: 3WGLM-5.3: 2W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs90 tok/s

Time to first token

205 msvs255 ms

Gemini 3.6 Pro: 2WGLM-5.3: 0W

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 3.6 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 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

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 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or GLM-5.3?

Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 3.6 Pro or GLM-5.3?

GLM-5.3 leads SWE-bench at 76.4% vs 74.6%. 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 3.6 Pro or GLM-5.3?

Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 3.6 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 3.6 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

Gemini 3.6 Pro

Gemini 3.6 Pro

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

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Gemini 3.6 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

Gemini 3.6 Pro

Gemini 3.6 Pro

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

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

GLM-5.3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons