Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.3 vs Kimi Chat 1.5 benchmark

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

This page compares GLM-5.3 (Zhipu) and Kimi Chat 1.5 (Moonshot) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 138 points (1,558 vs 1,420; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 51% (seed-bootstrap (Aug 16, 2026)). Kimi Chat 1.5 streams faster (95 tok/s) while Kimi Chat 1.5 is the cheaper output token ($0.8/1M). Kimi Chat 1.5 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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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

Moonshot

Kimi Chat 1.5

Moonshot ultra-long context model supporting up to 2 million tokens per request.

Elo 1,420$0.8/1M 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.

GLM-5.3 (5)vsKimi Chat 1.5 (5)

GLM-5.3: 5W (50%)Overall: Even matchupKimi Chat 1.5: 5W (50%)

← GLM-5.3Kimi Chat 1.5 →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,420

Coding Elo

1,586vs1,390

SWE-bench

76.4%vs51%

LiveBench

70.8%vs58%

GPQA Diamond

83.2%vs71.5%

GLM-5.3: 5WKimi Chat 1.5: 0W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs95 tok/s

Time to first token

255 msvs210 ms

GLM-5.3: 0WKimi Chat 1.5: 2W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$0.8/1M

Input price

$0.5/1Mvs$0.2/1M

Context window

200kvs2M

GLM-5.3: 0WKimi Chat 1.5: 3W

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

GLM-5.3

138 pts advantage

Throughput Speed

Kimi Chat 1.5

5 tok/s faster

Price Efficiency

Kimi Chat 1.5

$1.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+10%1,558

EloHigh is better

1,420

Code Elo

High is better

+14%1,586

Code EloHigh is better

1,390

LiveBench

High is better

+22%70.8%

LiveBenchHigh is better

58%

SWE-bench

High is better

+50%76.4%

SWE-benchHigh is better

51%

GPQA

High is better

+16%83.2%

GPQAHigh is better

71.5%

TTFT

Low is better

255 ms

TTFTLow is better

210 ms+21%

Speed

High is better

90 tok/s

SpeedHigh is better

95 tok/s+6%

In $

Low is better

$0.5/1M

In $Low is better

$0.2/1M+150%

Out $

Low is better

$1.8/1M

Out $Low is better

$0.8/1M+125%

Context

High is better

200k

ContextHigh is better

2M+900%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 57% with Kimi Chat 1.5

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

Kimi Chat 1.5$19.00 / mo

In: $7.00Out: $12.00

Estimated Cost Delta

Kimi Chat 1.5 is estimated to save $25.50/month ($306/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
Kimi Chat 1.5:Pick Kimi Chat 1.5 when you are optimizing output cost.
3
Kimi Chat 1.5:Pick Kimi Chat 1.5 when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatKimi Chat 1.5

Lower output list price ($0.8/1M).

Voice / low-latency UIKimi Chat 1.5

Lower TTFT (210 ms).

Long-document RAGKimi Chat 1.5

Larger window (2M).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	Kimi Chat 1.5	Lower output list price ($0.8/1M).
Voice / low-latency UI	Kimi Chat 1.5	Lower TTFT (210 ms).
Long-document RAG	Kimi Chat 1.5	Larger window (2M).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

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.

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

Moonshot

Kimi Chat 1.5

Elo 1,420

LiveBench58%

SWE-bench51%

Speed95 tok/s

Output cost$0.8/1M

Frequently Asked Questions

Which is better overall, GLM-5.3 or Kimi Chat 1.5?: 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 Kimi Chat 1.5?: GLM-5.3 leads SWE-bench at 76.4% vs 51%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Kimi Chat 1.5 output tokens are $0.8/1M versus $1.8/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.3 or Kimi Chat 1.5?: Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Kimi Chat 1.5 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. GLM-5.3 and Kimi Chat 1.5 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 Kimi Chat 1.5 matchups →

GLM-5.3 vs GLM-5.2 (previous glm)Kimi Chat 1.5 vs MoonshotAI: Kimi K2 0711 (next kimi)Kimi K3 vs GLM-5.3 MoonshotAI: Kimi K2.5 vs GLM-5.3 GPT-5 vs GLM-5.3 Claude Sonnet 5 vs GLM-5.3

GLM-5.3 vs Kimi Chat 1.5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (5)vsKimi Chat 1.5 (5)

GLM-5.3: 5W (50%)Overall: Even matchupKimi Chat 1.5: 5W (50%)

← GLM-5.3Kimi Chat 1.5 →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,420

Coding Elo

1,586vs1,390

SWE-bench

76.4%vs51%

LiveBench

70.8%vs58%

GPQA Diamond

83.2%vs71.5%

GLM-5.3: 5WKimi Chat 1.5: 0W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs95 tok/s

Time to first token

255 msvs210 ms

GLM-5.3: 0WKimi Chat 1.5: 2W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$0.8/1M

Input price

$0.5/1Mvs$0.2/1M

Context window

200kvs2M

GLM-5.3: 0WKimi Chat 1.5: 3W

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatKimi Chat 1.5

Lower output list price ($0.8/1M).

Voice / low-latency UIKimi Chat 1.5

Lower TTFT (210 ms).

Long-document RAGKimi Chat 1.5

Larger window (2M).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	Kimi Chat 1.5	Lower output list price ($0.8/1M).
Voice / low-latency UI	Kimi Chat 1.5	Lower TTFT (210 ms).
Long-document RAG	Kimi Chat 1.5	Larger window (2M).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GLM-5.3 or Kimi Chat 1.5?

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 Kimi Chat 1.5?

GLM-5.3 leads SWE-bench at 76.4% vs 51%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Kimi Chat 1.5 output tokens are $0.8/1M versus $1.8/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.3 or Kimi Chat 1.5?

Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Kimi Chat 1.5 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. GLM-5.3 and Kimi Chat 1.5 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

GLM-5.3

Kimi Chat 1.5

Kimi Chat 1.5

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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GLM-5.3

Kimi Chat 1.5

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

GLM-5.3

Kimi Chat 1.5

Kimi Chat 1.5

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

GLM-5.3

Kimi Chat 1.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons