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GLM-4.5 vs Kimi K2-Instruct-0905

GLM-4.5 significantly outperforms across most benchmarks.

Zhipu AI · Moonshot AI · Updated for 2026

Which is better?

GLM-4.5 outperforms in 7 benchmarks (AIME 2024, GPQA, Humanity's Last Exam, LiveCodeBench, MATH-500, MMLU-Pro, Terminal-Bench), while Kimi K2-Instruct-0905 is better at 1 benchmark (SWE-Bench Verified). GLM-4.5 significantly outperforms across most benchmarks.

Based on current benchmark, pricing, and model metadata for 2026.

Choose GLM-4.5

  • you want the strongest raw capability — it leads on 7 of 8 shared benchmarks

Choose Kimi K2-Instruct-0905

  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

GLM-4.5
Zhipu AI
Kimi K2-Instruct-0905
Moonshot AI
Benchmark wins
7 of 8
1 of 8
Input price
$0.40 / M
— / M
Output price
$1.60 / M
— / M
Context window
131,072
—
Released
Jul 2025
Sep 2025
License
MIT
MIT

Performance Benchmarks

Comparative analysis across standard metrics

8 benchmarks

GLM-4.5 outperforms in 7 benchmarks (AIME 2024, GPQA, Humanity's Last Exam, LiveCodeBench, MATH-500, MMLU-Pro, Terminal-Bench), while Kimi K2-Instruct-0905 is better at 1 benchmark (SWE-Bench Verified).

GLM-4.5 significantly outperforms across most benchmarks.

Mon Aug 24 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

645.0B diff

Kimi K2-Instruct-0905 has 645.0B more parameters than GLM-4.5, making it 181.7% larger.

Zhipu AI
GLM-4.5
355.0Bparameters
Moonshot AI
Kimi K2-Instruct-0905
1.0Tparameters
355.0B
GLM-4.5
1000.0B
Kimi K2-Instruct-0905

Context Window

Maximum input and output token capacity

Only GLM-4.5 specifies input context (131,072 tokens). Only GLM-4.5 specifies output context (131,072 tokens).

Zhipu AI
GLM-4.5
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2-Instruct-0905
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.5

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.5 was released on 2025-07-28, while Kimi K2-Instruct-0905 was released on 2025-09-05.

Kimi K2-Instruct-0905 is 1 month newer than GLM-4.5.

GLM-4.5

Jul 28, 2025

1.1 years ago

Kimi K2-Instruct-0905

Sep 5, 2025

11 months ago

1mo newer

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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-4.5 and Kimi K2-Instruct-0905 side-by-side, then vote on the output you prefer.

GLM-4.5
✓ Preferred
Kimi K2-Instruct-0905
Open in Playground

FAQ

Common questions about GLM-4.5 vs Kimi K2-Instruct-0905.

Which is better, GLM-4.5 or Kimi K2-Instruct-0905?

GLM-4.5 significantly outperforms across most benchmarks. GLM-4.5 is made by Zhipu AI and Kimi K2-Instruct-0905 is made by Moonshot AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.5 compare to Kimi K2-Instruct-0905 in benchmarks?

GLM-4.5 scores MATH-500: 98.2%, AIME 2024: 91.0%, MMLU-Pro: 84.6%, TAU-bench Retail: 79.7%, GPQA: 79.1%. Kimi K2-Instruct-0905 scores MATH-500: 97.4%, MMLU-Redux: 92.7%, IFEval: 89.8%, AutoLogi: 89.5%, MMLU: 89.5%.

What are the context window sizes for GLM-4.5 and Kimi K2-Instruct-0905?

GLM-4.5 supports 131K tokens and Kimi K2-Instruct-0905 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes GLM-4.5 and Kimi K2-Instruct-0905?

GLM-4.5 is developed by Zhipu AI and Kimi K2-Instruct-0905 is developed by Moonshot AI.

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