Model Comparison

Kimi K2-Thinking-0905 vs GLM-4.7

GLM-4.7 has a slight edge in benchmark performance. Kimi K2-Thinking-0905 is 1.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

11 benchmarks

Kimi K2-Thinking-0905 outperforms in 5 benchmarks (AIME 2025, BrowseComp, Humanity's Last Exam, MMLU-Pro, Terminal-Bench), while GLM-4.7 is better at 6 benchmarks (BrowseComp-zh, GPQA, IMO-AnswerBench, LiveCodeBench v6, SWE-bench Multilingual, SWE-Bench Verified).

GLM-4.7 has a slight edge in benchmark performance.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Kimi K2-Thinking-0905 costs less

For input processing, Kimi K2-Thinking-0905 ($0.47/1M tokens) is 1.3x cheaper than GLM-4.7 ($0.60/1M tokens).

For output processing, Kimi K2-Thinking-0905 ($2.00/1M tokens) is 1.1x cheaper than GLM-4.7 ($2.20/1M tokens).

In conclusion, GLM-4.7 is more expensive than Kimi K2-Thinking-0905.*

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

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
Moonshot AI
Kimi K2-Thinking-0905
Input tokens$0.47
Output tokens$2.00
Best providerDeepinfra
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
Best providerFireworks
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Model Size

Parameter count comparison

642.0B diff

Kimi K2-Thinking-0905 has 642.0B more parameters than GLM-4.7, making it 179.3% larger.

Moonshot AI
Kimi K2-Thinking-0905
1000.0Bparameters
Zhipu AI
GLM-4.7
358.0Bparameters
1000.0B
Kimi K2-Thinking-0905
358.0B
GLM-4.7

Context Window

Maximum input and output token capacity

Kimi K2-Thinking-0905 accepts 262,144 input tokens compared to GLM-4.7's 202,800 tokens. Kimi K2-Thinking-0905 can generate longer responses up to 262,144 tokens, while GLM-4.7 is limited to 131,072 tokens.

Moonshot AI
Kimi K2-Thinking-0905
Input262,144 tokens
Output262,144 tokens
Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
Fri Apr 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.7 supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.

GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2-Thinking-0905

Text
Images
Audio
Video

GLM-4.7

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.

Kimi K2-Thinking-0905

MIT

Open weights

GLM-4.7

MIT

Open weights

Release Timeline

When each model was launched

Kimi K2-Thinking-0905 was released on 2025-09-05, while GLM-4.7 was released on 2025-12-22.

GLM-4.7 is 4 months newer than Kimi K2-Thinking-0905.

Kimi K2-Thinking-0905

Sep 5, 2025

7 months ago

GLM-4.7

Dec 22, 2025

3 months ago

3mo 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

Provider Availability

Kimi K2-Thinking-0905 is available from DeepInfra, Novita, Fireworks. GLM-4.7 is available from Fireworks, Novita.

Kimi K2-Thinking-0905

deepinfra logo
Deepinfra
Input Price:Input: $0.47/1MOutput Price:Output: $2.00/1M
novita logo
Novita
Input Price:Input: $0.48/1MOutput Price:Output: $2.00/1M
fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $2.50/1M

GLM-4.7

fireworks logo
Fireworks
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Moonshot AI
Larger context window (262,144 tokens)
Less expensive input tokens
Less expensive output tokens
Higher AIME 2025 score (100.0% vs 95.7%)
Higher BrowseComp score (60.2% vs 52.0%)
Higher Humanity's Last Exam score (51.0% vs 42.8%)
Higher MMLU-Pro score (84.6% vs 84.3%)
Higher Terminal-Bench score (47.1% vs 33.3%)
Zhipu AI
Supports multimodal inputs
Higher BrowseComp-zh score (66.6% vs 62.3%)
Higher GPQA score (85.7% vs 84.5%)
Higher IMO-AnswerBench score (82.0% vs 78.6%)
Higher LiveCodeBench v6 score (84.9% vs 83.1%)
Higher SWE-bench Multilingual score (66.7% vs 61.1%)
Higher SWE-Bench Verified score (73.8% vs 71.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2-Thinking-0905
Zhipu AI
GLM-4.7

FAQ

Common questions about Kimi K2-Thinking-0905 vs GLM-4.7

GLM-4.7 has a slight edge in benchmark performance. Kimi K2-Thinking-0905 is made by Moonshot AI and GLM-4.7 is made by Zhipu AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Kimi K2-Thinking-0905 scores AIME 2025: 100.0%, HMMT 2025: 97.5%, MMLU-Redux: 94.4%, FRAMES: 87.0%, MMLU-Pro: 84.6%. GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%.
Kimi K2-Thinking-0905 is 1.3x cheaper for input tokens. Kimi K2-Thinking-0905 costs $0.47/M input and $2.00/M output via deepinfra. GLM-4.7 costs $0.60/M input and $2.20/M output via fireworks.
Kimi K2-Thinking-0905 supports 262K tokens and GLM-4.7 supports 203K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (262K vs 203K), input pricing ($0.47 vs $0.60/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.
Kimi K2-Thinking-0905 is developed by Moonshot AI and GLM-4.7 is developed by Zhipu AI.