Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.2 vs MoonshotAI: Kimi K2 0711 benchmark

Name: GLM-5.2 vs MoonshotAI: Kimi K2 0711 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.2 (Zhipu) and MoonshotAI: Kimi K2 0711 (Moonshot) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.2 is ahead by 27 points (1,492 vs 1,465; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GLM-5.2 resolves 69.3% versus 65.8% (seed-bootstrap (Aug 1, 2026)). MoonshotAI: Kimi K2 0711 streams faster (85 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

Moonshot

MoonshotAI: Kimi K2 0711

Auto-discovered from OpenRouter (moonshotai/kimi-k2). Preview until a second source matches.

Elo 1,465$2.3/1M out

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.2 (8)vsMoonshotAI: Kimi K2 0711 (2)

GLM-5.2: 8W (80%)Overall: GLM-5.2MoonshotAI: Kimi K2 0711: 2W (20%)

← GLM-5.2MoonshotAI: Kimi K2 0711 →

GLM-5.2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs1,465

Coding Elo

1,504vs1,470

SWE-bench

69.3%vs65.8%

LiveBench

65.2%vs62.8%

GPQA Diamond

79.5%vs76.5%

GLM-5.2: 5WMoonshotAI: Kimi K2 0711: 0W

MoonshotAI: Kimi K2 0711 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs85 tok/s

Time to first token

270 msvs240 ms

GLM-5.2: 0WMoonshotAI: Kimi K2 0711: 2W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$2.3/1M

Input price

$0.31/1Mvs$0.57/1M

Context window

1Mvs131k

GLM-5.2: 3WMoonshotAI: Kimi K2 0711: 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 AdvantageGLM-5.2 5/6

Preference Elo

GLM-5.2

27 pts advantage

Throughput Speed

MoonshotAI: Kimi K2 0711

1 tok/s faster

Price Efficiency

GLM-5.2

$1.33/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+2%1,492

EloHigh is better

1,465

Code Elo

High is better

+2%1,504

Code EloHigh is better

1,470

LiveBench

High is better

+4%65.2%

LiveBenchHigh is better

62.8%

SWE-bench

High is better

+5%69.3%

SWE-benchHigh is better

65.8%

GPQA

High is better

+4%79.5%

GPQAHigh is better

76.5%

TTFT

Low is better

270 ms

TTFTLow is better

240 ms+13%

Speed

High is better

84 tok/s

SpeedHigh is better

85 tok/s+1%

In $

Low is better

+85%$0.31/1M

In $Low is better

$0.57/1M

Out $

Low is better

+138%$0.97/1M

Out $Low is better

$2.3/1M

Context

High is better

+700%1M

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 54% 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

MoonshotAI: Kimi K2 0711$54.45 / mo

In: $19.95Out: $34.50

Estimated Cost Delta

GLM-5.2 is estimated to save $29.15/month ($350/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
MoonshotAI: Kimi K2 0711:Pick MoonshotAI: Kimi K2 0711 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 UIMoonshotAI: Kimi K2 0711

Lower TTFT (240 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

Both accept images. Defaulting to the higher-Elo side (GLM-5.2).

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	MoonshotAI: Kimi K2 0711	Lower TTFT (240 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	Both accept images. Defaulting to the higher-Elo side (GLM-5.2).

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.

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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

Moonshot

MoonshotAI: Kimi K2 0711

Elo 1,465

LiveBench62.8%

SWE-bench65.8%

Speed85 tok/s

Output cost$2.3/1M

Frequently Asked Questions

Which is better overall, GLM-5.2 or MoonshotAI: Kimi K2 0711?: GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, GLM-5.2 or MoonshotAI: Kimi K2 0711?: GLM-5.2 leads SWE-bench at 69.3% vs 65.8%. 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 $2.3/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.2 or MoonshotAI: Kimi K2 0711?: MoonshotAI: Kimi K2 0711 has the lower time-to-first-token (240 ms vs 270 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 131k.
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 MoonshotAI: Kimi K2 0711 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 MoonshotAI: Kimi K2 0711 matchups →

GLM-5.2 vs GLM-5.3 (next glm)MoonshotAI: Kimi K2 0711 vs Kimi Chat 1.5 (previous kimi)MoonshotAI: Kimi K2 0711 vs MoonshotAI: Kimi K2.5 (next kimi)Kimi K3 vs GLM-5.2 MoonshotAI: Kimi K2.5 vs GLM-5.2 Gemini 2.0 Flash Thinking vs GLM-5.2

GLM-5.2 vs MoonshotAI: Kimi K2 0711 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.2 (8)vsMoonshotAI: Kimi K2 0711 (2)

GLM-5.2: 8W (80%)Overall: GLM-5.2MoonshotAI: Kimi K2 0711: 2W (20%)

← GLM-5.2MoonshotAI: Kimi K2 0711 →

GLM-5.2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs1,465

Coding Elo

1,504vs1,470

SWE-bench

69.3%vs65.8%

LiveBench

65.2%vs62.8%

GPQA Diamond

79.5%vs76.5%

GLM-5.2: 5WMoonshotAI: Kimi K2 0711: 0W

MoonshotAI: Kimi K2 0711 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs85 tok/s

Time to first token

270 msvs240 ms

GLM-5.2: 0WMoonshotAI: Kimi K2 0711: 2W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.97/1Mvs$2.3/1M

Input price

$0.31/1Mvs$0.57/1M

Context window

1Mvs131k

GLM-5.2: 3WMoonshotAI: Kimi K2 0711: 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 UIMoonshotAI: Kimi K2 0711

Lower TTFT (240 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

Both accept images. Defaulting to the higher-Elo side (GLM-5.2).

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	MoonshotAI: Kimi K2 0711	Lower TTFT (240 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	Both accept images. Defaulting to the higher-Elo side (GLM-5.2).

Frequently Asked Questions

Which is better overall, GLM-5.2 or MoonshotAI: Kimi K2 0711?

GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GLM-5.2 or MoonshotAI: Kimi K2 0711?

GLM-5.2 leads SWE-bench at 69.3% vs 65.8%. 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 $2.3/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.2 or MoonshotAI: Kimi K2 0711?

MoonshotAI: Kimi K2 0711 has the lower time-to-first-token (240 ms vs 270 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 131k.

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 MoonshotAI: Kimi K2 0711 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-5.2

MoonshotAI: Kimi K2 0711

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

Cast Your Matchup Vote

Community Discussions (0)

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GLM-5.2

MoonshotAI: Kimi K2 0711

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-5.2

MoonshotAI: Kimi K2 0711

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

GLM-5.2

MoonshotAI: Kimi K2 0711

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons