Model Comparison

Kimi K2.5 vs GLM-4.7

Kimi K2.5 significantly outperforms across most benchmarks. GLM-4.7 is 1.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

10 benchmarks

Kimi K2.5 outperforms in 9 benchmarks (AIME 2025, BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench v6, MMLU-Pro, SWE-bench Multilingual, SWE-Bench Verified, Terminal-Bench 2.0), while GLM-4.7 is better at 1 benchmark (IMO-AnswerBench).

Kimi K2.5 significantly outperforms across most benchmarks.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.7 costs less

For input processing, Kimi K2.5 ($0.60/1M tokens) costs the same as GLM-4.7 ($0.60/1M tokens).

For output processing, Kimi K2.5 ($2.50/1M tokens) is 1.1x more expensive than GLM-4.7 ($2.20/1M tokens).

In conclusion, Kimi K2.5 is more expensive than GLM-4.7.*

* 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 K2.5
Input tokens$0.60
Output tokens$2.50
Best providerFireworks
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
Best providerFireworks
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

642.0B diff

Kimi K2.5 has 642.0B more parameters than GLM-4.7, making it 179.3% larger.

Moonshot AI
Kimi K2.5
1000.0Bparameters
Zhipu AI
GLM-4.7
358.0Bparameters
1000.0B
Kimi K2.5
358.0B
GLM-4.7

Context Window

Maximum input and output token capacity

Kimi K2.5 accepts 262,100 input tokens compared to GLM-4.7's 202,800 tokens. Kimi K2.5 can generate longer responses up to 262,100 tokens, while GLM-4.7 is limited to 131,072 tokens.

Moonshot AI
Kimi K2.5
Input262,100 tokens
Output262,100 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

Both Kimi K2.5 and GLM-4.7 support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Kimi K2.5

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 K2.5

MIT

Open weights

GLM-4.7

MIT

Open weights

Release Timeline

When each model was launched

Kimi K2.5 was released on 2026-01-27, while GLM-4.7 was released on 2025-12-22.

Kimi K2.5 is 1 month newer than GLM-4.7.

Kimi K2.5

Jan 27, 2026

2 months ago

1mo newer
GLM-4.7

Dec 22, 2025

3 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

Kimi K2.5 is available from Fireworks. GLM-4.7 is available from Fireworks, Novita.

Kimi K2.5

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 (262,100 tokens)
Higher AIME 2025 score (96.1% vs 95.7%)
Higher BrowseComp score (74.9% vs 52.0%)
Higher GPQA score (87.6% vs 85.7%)
Higher Humanity's Last Exam score (50.2% vs 42.8%)
Higher LiveCodeBench v6 score (85.0% vs 84.9%)
Higher MMLU-Pro score (87.1% vs 84.3%)
Higher SWE-bench Multilingual score (73.0% vs 66.7%)
Higher SWE-Bench Verified score (76.8% vs 73.8%)
Higher Terminal-Bench 2.0 score (50.8% vs 41.0%)
Zhipu AI
Less expensive output tokens
Higher IMO-AnswerBench score (82.0% vs 81.8%)

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2.5
Zhipu AI
GLM-4.7

FAQ

Common questions about Kimi K2.5 vs GLM-4.7

Kimi K2.5 significantly outperforms across most benchmarks. Kimi K2.5 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 K2.5 scores AIME 2025: 96.1%, HMMT 2025: 95.4%, InfoVQAtest: 92.6%, OCRBench: 92.3%, MathVista-Mini: 90.1%. GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%.
Both models cost $0.60 per million input tokens.
Kimi K2.5 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). See the full comparison above for benchmark-by-benchmark results.
Kimi K2.5 is developed by Moonshot AI and GLM-4.7 is developed by Zhipu AI.