Model Comparison

GLM-4.6 vs Kimi K2-Instruct-0905

GLM-4.6 significantly outperforms across most benchmarks.

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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.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
Zhipu AI
GLM-4.6
Input tokens$0.55
Output tokens$2.00
Best providerFireworks
Moonshot AI
Kimi K2-Instruct-0905
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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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
1000.0Bparameters
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
Fri Apr 17 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

6 months ago

3w newer
Kimi K2-Instruct-0905

Sep 5, 2025

7 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

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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

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

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.
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%.
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.
Key differences include multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.
GLM-4.6 is developed by Zhipu AI and Kimi K2-Instruct-0905 is developed by Moonshot AI.