GLM-5.3 vs Kimi K2.7 Code

GLM-5.3 leads the LLM Stats Score 54.2 to 39.6. Kimi K2.7 Code is 1.5x cheaper per token.

Zhipu AI · Moonshot AI · Updated for 2026

Which is better?

GLM-5.3 leads the overall LLM Stats Score 54.2 to 39.6, ranking #6 overall.

The models split the 2 individual benchmarks reported for both models evenly.

On price, Kimi K2.7 Code is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5.3 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-5.3

overall performance matters — it scores 54.2 and ranks #6 on LLM Stats
your work emphasizes reasoning and coding — it leads those capability indexes
you process long inputs — it offers a 1,048,576 token context window
you want the most recent training data — it shipped Aug 2026

Choose Kimi K2.7 Code

cost matters — it's about 1.5x cheaper per token
you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Better token value: Kimi K2.7 Code

Core performance indexes

LLM Stats Score

54.2

#6 · 16 evals

Best

39.6

#49 · 7 evals

Reasoning

53.4

#5 · 8 evals

Best

35.2

#74 · 4 evals

Coding

45.4

#6 · 11 evals

Best

32.2

#42 · 5 evals

Agents

41.2

#5 · 15 evals

Best

28.0

#37 · 6 evals

Cost, coverage & limits

Benchmark wins

1 of 2

Input price

$1.40 / M

$0.74 / M

Best

Output price

$4.40 / M

$3.50 / M

Best

Context window

1,048,576

Best

262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared

Index

GLM-5.3

Kimi K2.7 Code

Tool use

35.4#2 · 5 evals

25.6#26 · 2 evals

Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for GLM-5.3 · 9 for Kimi K2.7 Code

2 shared

GLM-5.3 outperforms in 1 benchmarks (DeepSWE 1.1), while Kimi K2.7 Code is better at 1 benchmark (Program Bench).

Both models are evenly matched across the benchmarks.

Fri Aug 28 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Kimi K2.7 Code costs less

For input processing, GLM-5.3 ($1.40/1M tokens) is 1.9x more expensive than Kimi K2.7 Code ($0.74/1M tokens).

For output processing, GLM-5.3 ($4.40/1M tokens) is 1.3x more expensive than Kimi K2.7 Code ($3.50/1M tokens).

In conclusion, GLM-5.3 is more expensive than Kimi K2.7 Code.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Fri Aug 28 2026 • llm-stats.com

GLM-5.3

Input tokens$1.40

Output tokens$4.40

Best providerNovita

Kimi K2.7 Code

Input tokens$0.74

Output tokens$3.50

Best providerDeepinfra

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

247.0B diff

Kimi K2.7 Code has 247.0B more parameters than GLM-5.3, making it 32.8% larger.

GLM-5.3

753.0Bparameters

Kimi K2.7 Code

1.0Tparameters

753.0B

GLM-5.3

1000.0B

Kimi K2.7 Code

Context Window

Maximum input and output token capacity

GLM-5.3 accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. Both models can generate responses up to 131,072 tokens.

GLM-5.3

Input1,048,576 tokens

Output131,072 tokens

Kimi K2.7 Code

Input262,144 tokens

Output131,072 tokens

Fri Aug 28 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Kimi K2.7 Code supports multimodal inputs, whereas GLM-5.3 does not.

Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5.3

Text

Images

Audio

Video

Kimi K2.7 Code

Text

Images

Audio

Video

Release Timeline

When each model was launched

GLM-5.3 was released on 2026-08-14, while Kimi K2.7 Code was released on 2026-06-12.

GLM-5.3 is 2 months newer than Kimi K2.7 Code.

GLM-5.3

Aug 14, 2026

2 weeks ago

2mo newer

Kimi K2.7 Code

Jun 12, 2026

2 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-5.3 is available from Novita, ZAI. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.

GLM-5.3

Novita

Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M

Unknown Organization

Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M

Kimi K2.7 Code

Deepinfra

Input Price:Input: $0.74/1MOutput Price:Output: $3.50/1M

Fireworks

Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M

Unknown Organization

Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M

Novita

Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M

Together

Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-5.3 and Kimi K2.7 Code side-by-side, then vote on the output you prefer.

GLM-5.3

✓ Preferred

Kimi K2.7 Code

Open in Playground

FAQ

Common questions about GLM-5.3 vs Kimi K2.7 Code.

Which is better, GLM-5.3 or Kimi K2.7 Code?

GLM-5.3 leads the LLM Stats Score 54.2 to 39.6. GLM-5.3 is made by Zhipu AI and Kimi K2.7 Code is made by Moonshot AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-5.3 compare to Kimi K2.7 Code in benchmarks?

GLM-5.3 scores Terminal-Bench 2.1: 88.2%, CyberGym: 84.5%, FrontierSWE: 78.1%, Toolathlon: 73.0%, DeepSWE 1.1: 66.9%. Kimi K2.7 Code scores MCP-Mark: 81.1%, MCP Atlas: 76.0%, LiveBench: 71.9%, Kimi Code Bench v2: 62.0%, Program Bench: 53.6%.

Is GLM-5.3 cheaper than Kimi K2.7 Code?

Kimi K2.7 Code is 1.9x cheaper for input tokens. GLM-5.3 costs $1.40/M input and $4.40/M output via novita. Kimi K2.7 Code costs $0.74/M input and $3.50/M output via deepinfra.

What are the context window sizes for GLM-5.3 and Kimi K2.7 Code?

GLM-5.3 supports 1.0M tokens and Kimi K2.7 Code supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5.3 and Kimi K2.7 Code?

Key differences include LLM Stats Score (54.2 vs 39.6), context window (1.0M vs 262K), input pricing ($1.40 vs $0.74/M), multimodal support (no vs yes), licensing (Unknown vs Modified MIT License). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3 and Kimi K2.7 Code?

GLM-5.3 is developed by Zhipu AI and Kimi K2.7 Code is developed by Moonshot AI.

GLM-5.3 vs Kimi K2.7 Code