Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

GLM-5.2 vs MoonshotAI: Kimi K2.7 Code benchmark

Name: GLM-5.2 vs MoonshotAI: Kimi K2.7 Code AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.2 (Zhipu) and MoonshotAI: Kimi K2.7 Code (Moonshot) using the latest snapshots we have as of Aug 17, 2026. MoonshotAI: Kimi K2.7 Code wins on output price at $3.5/1M. 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$3.74/1M out

Moonshot

MoonshotAI: Kimi K2.7 Code

Auto-discovered from OpenRouter (moonshotai/kimi-k2.7-code). Preview until a second source matches.

$3.5/1M 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-5.2 (8)vsMoonshotAI: Kimi K2.7 Code (2)

GLM-5.2: 8W (80%)Overall: GLM-5.2MoonshotAI: Kimi K2.7 Code: 2W (20%)

← GLM-5.2MoonshotAI: Kimi K2.7 Code →

GLM-5.2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs—

Coding Elo

1,504vs—

SWE-bench

69.3%vs—

LiveBench

65.2%vs—

GPQA Diamond

79.5%vs—

GLM-5.2: 5WMoonshotAI: Kimi K2.7 Code: 0W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs—

Time to first token

270 msvs—

GLM-5.2: 2WMoonshotAI: Kimi K2.7 Code: 0W

MoonshotAI: Kimi K2.7 Code (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.74/1Mvs$3.5/1M

Input price

$1.19/1Mvs$0.71/1M

Context window

1Mvs262k

GLM-5.2: 1WMoonshotAI: Kimi K2.7 Code: 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-5.2 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

MoonshotAI: Kimi K2.7 Code

$0.24/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,492

EloHigh is better

—

Code Elo

High is better

1,504

Code EloHigh is better

—

LiveBench

High is better

65.2%

LiveBenchHigh is better

—

SWE-bench

High is better

69.3%

SWE-benchHigh is better

—

GPQA

High is better

79.5%

GPQAHigh is better

—

TTFT

Low is better

270 ms

TTFTLow is better

—

Speed

High is better

84 tok/s

SpeedHigh is better

—

In $

Low is better

$1.19/1M

In $Low is better

$0.71/1M+68%

Out $

Low is better

$3.74/1M

Out $Low is better

$3.5/1M+7%

Context

High is better

+300%1M

ContextHigh is better

262k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 21% with MoonshotAI: Kimi K2.7 Code

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$97.75 / mo

In: $41.65Out: $56.10

MoonshotAI: Kimi K2.7 Code$77.35 / mo

In: $24.85Out: $52.50

Estimated Cost Delta

MoonshotAI: Kimi K2.7 Code is estimated to save $20.40/month ($245/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
MoonshotAI: Kimi K2.7 Code:Pick MoonshotAI: Kimi K2.7 Code when you are optimizing output cost.
2
GLM-5.2:Pick GLM-5.2 for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatMoonshotAI: Kimi K2.7 Code

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	MoonshotAI: Kimi K2.7 Code	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

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$3.74/1M

Moonshot

MoonshotAI: Kimi K2.7 Code

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$3.5/1M

Frequently Asked Questions

Which is better overall, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: MoonshotAI: Kimi K2.7 Code output tokens are $3.5/1M versus $3.74/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?: We do not have TTFT for both models.
Which has the larger context window?: GLM-5.2 accepts 1M tokens versus 262k.
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 K2.7 Code 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 K2.7 Code matchups →

GLM-5.2 vs Z.ai: GLM 5V Turbo (next glm)Kimi K3 vs GLM-5.2 GLM-5.3 vs GLM-5.2 MoonshotAI: Kimi K2.5 vs GLM-5.2 MoonshotAI: Kimi K2 0711 vs GLM-5.2 Kimi Chat 1.5 vs GLM-5.2

GLM-5.2 vs MoonshotAI: Kimi K2.7 Code 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 K2.7 Code (2)

GLM-5.2: 8W (80%)Overall: GLM-5.2MoonshotAI: Kimi K2.7 Code: 2W (20%)

← GLM-5.2MoonshotAI: Kimi K2.7 Code →

GLM-5.2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,492vs—

Coding Elo

1,504vs—

SWE-bench

69.3%vs—

LiveBench

65.2%vs—

GPQA Diamond

79.5%vs—

GLM-5.2: 5WMoonshotAI: Kimi K2.7 Code: 0W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

84 tok/svs—

Time to first token

270 msvs—

GLM-5.2: 2WMoonshotAI: Kimi K2.7 Code: 0W

MoonshotAI: Kimi K2.7 Code (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.74/1Mvs$3.5/1M

Input price

$1.19/1Mvs$0.71/1M

Context window

1Mvs262k

GLM-5.2: 1WMoonshotAI: Kimi K2.7 Code: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatMoonshotAI: Kimi K2.7 Code

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	MoonshotAI: Kimi K2.7 Code	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

MoonshotAI: Kimi K2.7 Code output tokens are $3.5/1M versus $3.74/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.2 or MoonshotAI: Kimi K2.7 Code?

We do not have TTFT for both models.

Which has the larger context window?

GLM-5.2 accepts 1M tokens versus 262k.

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 K2.7 Code 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

No shared data

No shared data

MoonshotAI: Kimi K2.7 Code

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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CompareLLM Matrix

GLM-5.2

MoonshotAI: Kimi K2.7 Code

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

No shared data

No shared data

MoonshotAI: Kimi K2.7 Code

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 K2.7 Code

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons