Model Comparison

GLM-4.6 vs Kimi K2-Instruct-0905Which is better in 2026?

GLM-4.6 significantly outperforms across most benchmarks.

Verdict: GLM-4.6 vs Kimi K2-Instruct-0905 — which is better?

GLM-4.6 (by Zhipu AI) and Kimi K2-Instruct-0905 (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

GLM-4.6 outperforms in 5 benchmarks (AIME 2025, GPQA, Humanity's Last Exam, SWE-Bench Verified, Terminal-Bench), while Kimi K2-Instruct-0905 is better at 0 benchmarks. GLM-4.6 significantly outperforms across most benchmarks.

Choose GLM-4.6 if…

  • you want the strongest raw capability — it leads on 5 of 5 shared benchmarks
  • you want the most recent training data — it shipped Sep 2025

Choose Kimi K2-Instruct-0905 if…

  • you are already invested in the Moonshot AI ecosystem
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

GLM-4.6 outperforms in 5 benchmarks (AIME 2025, GPQA, Humanity's Last Exam, SWE-Bench Verified, Terminal-Bench), while Kimi K2-Instruct-0905 is better at 0 benchmarks.

GLM-4.6 significantly outperforms across most benchmarks.

Wed Jun 24 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

643.0B diff

Kimi K2-Instruct-0905 has 643.0B more parameters than GLM-4.6, making it 180.1% larger.

Zhipu AI
GLM-4.6
357.0Bparameters
Moonshot AI
Kimi K2-Instruct-0905
1.0Tparameters
357.0B
GLM-4.6
1000.0B
Kimi K2-Instruct-0905

Context Window

Maximum input and output token capacity

Only GLM-4.6 specifies input context (131,072 tokens). Only GLM-4.6 specifies output context (131,072 tokens).

Zhipu AI
GLM-4.6
Input131,072 tokens
Output131,072 tokens
Moonshot AI
Kimi K2-Instruct-0905
Input- tokens
Output- tokens
Wed Jun 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.6 supports multimodal inputs, whereas Kimi K2-Instruct-0905 does not.

GLM-4.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.6

Text
Images
Audio
Video

Kimi K2-Instruct-0905

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

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

GLM-4.6

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.6 was released on 2025-09-30, while Kimi K2-Instruct-0905 was released on 2025-09-05.

GLM-4.6 is 1 month newer than Kimi K2-Instruct-0905.

GLM-4.6

Sep 30, 2025

8 months ago

3w newer
Kimi K2-Instruct-0905

Sep 5, 2025

9 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (131,072 tokens)
Supports multimodal inputs
Higher AIME 2025 score (93.9% vs 49.5%)
Higher GPQA score (81.0% vs 75.1%)
Higher Humanity's Last Exam score (17.2% vs 4.7%)
Higher SWE-Bench Verified score (68.0% vs 65.8%)
Higher Terminal-Bench score (40.5% vs 25.0%)
Moonshot AI

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.6
Moonshot AI
Kimi K2-Instruct-0905

FAQ

Common questions about GLM-4.6 vs Kimi K2-Instruct-0905.

Which is better, GLM-4.6 or Kimi K2-Instruct-0905?

GLM-4.6 significantly outperforms across most benchmarks. GLM-4.6 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-4.6 compare to Kimi K2-Instruct-0905 in benchmarks?

GLM-4.6 scores AIME 2025: 93.9%, LiveCodeBench v6: 82.8%, GPQA: 81.0%, SWE-Bench Verified: 68.0%, BrowseComp: 45.1%. 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-4.6 and Kimi K2-Instruct-0905?

GLM-4.6 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.

What are the main differences between GLM-4.6 and Kimi K2-Instruct-0905?

Key differences include multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.6 and Kimi K2-Instruct-0905?

GLM-4.6 is developed by Zhipu AI and Kimi K2-Instruct-0905 is developed by Moonshot AI.

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