Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.3 vs Kimi K3 benchmark

Name: GLM-5.3 vs Kimi K3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.3 (Zhipu) and Kimi K3 (Moonshot) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 30 points (1,558 vs 1,528; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 73.8% (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). Kimi K3 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-5.3

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 K3

Moonshot AI frontier flagship reasoning and agent swarm model with 256k context and top-tier SWE-bench coding capability.

Elo 1,528$15/1M 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)vsKimi K3 (2)

GLM-5.3: 8W (80%)Overall: GLM-5.3Kimi K3: 2W (20%)

← GLM-5.3Kimi K3 →

GLM-5.3 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,528

Coding Elo

1,586vs1,535

SWE-bench

76.4%vs73.8%

LiveBench

70.8%vs69.5%

GPQA Diamond

83.2%vs88.4%

GLM-5.3: 4WKimi K3: 1W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs68 tok/s

Time to first token

255 msvs290 ms

GLM-5.3: 2WKimi K3: 0W

GLM-5.3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$15/1M

Input price

$0.5/1Mvs$3/1M

Context window

200kvs1M

GLM-5.3: 2WKimi K3: 1W

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

30 pts advantage

Throughput Speed

GLM-5.3

22 tok/s faster

Price Efficiency

GLM-5.3

$13.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+2%1,558

EloHigh is better

1,528

Code Elo

High is better

+3%1,586

Code EloHigh is better

1,535

LiveBench

High is better

+2%70.8%

LiveBenchHigh is better

69.5%

SWE-bench

High is better

+4%76.4%

SWE-benchHigh is better

73.8%

GPQA

High is better

83.2%

GPQAHigh is better

88.4%+6%

TTFT

Low is better

+14%255 ms

TTFTLow is better

290 ms

Speed

High is better

+32%90 tok/s

SpeedHigh is better

68 tok/s

In $

Low is better

+500%$0.5/1M

In $Low is better

$3/1M

Out $

Low is better

+733%$1.8/1M

Out $Low is better

$15/1M

Context

High is better

200k

ContextHigh is better

1M+424%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 87% 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

Kimi K3$330.00 / mo

In: $105.00Out: $225.00

Estimated Cost Delta

GLM-5.3 is estimated to save $285.50/month ($3,426/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
GLM-5.3:Pick GLM-5.3 when time-to-first-token matters more than peak Elo.
3
Kimi K3:Pick Kimi K3 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 UIGLM-5.3

Lower TTFT (255 ms).

Long-document RAGKimi K3

Larger window (1M).

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	GLM-5.3	Lower TTFT (255 ms).
Long-document RAG	Kimi K3	Larger window (1M).
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.

launchJun 8, 2026

Kimi K3: Moonshot’s long-context open-weight competitive row

Jun 2026. The Kimi vs GPT hub remaps to the top Moonshot Elo vs top OpenAI Elo.

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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 K3

Elo 1,528

LiveBench69.5%

SWE-bench73.8%

Speed68 tok/s

Output cost$15/1M

Frequently Asked Questions

Which is better overall, GLM-5.3 or Kimi K3?: 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 K3?: GLM-5.3 leads SWE-bench at 76.4% vs 73.8%. 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 $15/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.3 or Kimi K3?: GLM-5.3 has the lower time-to-first-token (255 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Kimi K3 accepts 1M 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 K3 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 K3 matchups →

GLM-5.3 vs GLM-5.2 (previous glm)Kimi K3 vs MoonshotAI: Kimi K2.5 (previous kimi)GLM-5.2 vs Kimi K3 GPT-5 vs GLM-5.3 Claude Sonnet 5 vs GLM-5.3 Gemini 3 Pro vs GLM-5.3

GLM-5.3 vs Kimi K3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (8)vsKimi K3 (2)

GLM-5.3: 8W (80%)Overall: GLM-5.3Kimi K3: 2W (20%)

← GLM-5.3Kimi K3 →

GLM-5.3 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,528

Coding Elo

1,586vs1,535

SWE-bench

76.4%vs73.8%

LiveBench

70.8%vs69.5%

GPQA Diamond

83.2%vs88.4%

GLM-5.3: 4WKimi K3: 1W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs68 tok/s

Time to first token

255 msvs290 ms

GLM-5.3: 2WKimi K3: 0W

GLM-5.3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$15/1M

Input price

$0.5/1Mvs$3/1M

Context window

200kvs1M

GLM-5.3: 2WKimi K3: 1W

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 UIGLM-5.3

Lower TTFT (255 ms).

Long-document RAGKimi K3

Larger window (1M).

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	GLM-5.3	Lower TTFT (255 ms).
Long-document RAG	Kimi K3	Larger window (1M).
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 Kimi K3?

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 K3?

GLM-5.3 leads SWE-bench at 76.4% vs 73.8%. 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 $15/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.3 or Kimi K3?

GLM-5.3 has the lower time-to-first-token (255 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Kimi K3 accepts 1M 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 K3 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

Kimi K3: Moonshot’s long-context open-weight competitive row

Cast Your Matchup Vote

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CompareLLM Matrix

GLM-5.3

Kimi K3

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

Kimi K3: Moonshot’s long-context open-weight competitive row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.3

Kimi K3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons