Model Comparison

GLM-4.6 vs Qwen2.5 7B Instruct

GLM-4.6 significantly outperforms across most benchmarks. Qwen2.5 7B Instruct is 3.0x cheaper per token.

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-4.6 outperforms in 1 benchmarks (GPQA), while Qwen2.5 7B Instruct is better at 0 benchmarks.

GLM-4.6 significantly outperforms across most benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen2.5 7B Instruct costs less

For input processing, GLM-4.6 ($0.55/1M tokens) is 1.8x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

For output processing, GLM-4.6 ($2.00/1M tokens) is 6.7x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

In conclusion, GLM-4.6 is more expensive than Qwen2.5 7B Instruct.*

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

Lowest available price from all providers
Sat May 02 2026 • llm-stats.com
Zhipu AI
GLM-4.6
Input tokens$0.55
Output tokens$2.00
Best providerFireworks
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input tokens$0.30
Output tokens$0.30
Best providerTogether
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Model Size

Parameter count comparison

349.4B diff

GLM-4.6 has 349.4B more parameters than Qwen2.5 7B Instruct, making it 4591.2% larger.

Zhipu AI
GLM-4.6
357.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
7.6Bparameters
357.0B
GLM-4.6
7.6B
Qwen2.5 7B Instruct

Context Window

Maximum input and output token capacity

Both models have the same input context window of 131,072 tokens. GLM-4.6 can generate longer responses up to 131,072 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.

Zhipu AI
GLM-4.6
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input131,072 tokens
Output8,192 tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.6 supports multimodal inputs, whereas Qwen2.5 7B Instruct 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

Qwen2.5 7B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.6 is licensed under MIT, while Qwen2.5 7B Instruct uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-4.6

MIT

Open weights

Qwen2.5 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.6 was released on 2025-09-30, while Qwen2.5 7B Instruct was released on 2024-09-19.

GLM-4.6 is 13 months newer than Qwen2.5 7B Instruct.

GLM-4.6

Sep 30, 2025

7 months ago

1.0yr newer
Qwen2.5 7B Instruct

Sep 19, 2024

1.6 years 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

GLM-4.6 is available from Fireworks, DeepInfra. Qwen2.5 7B Instruct is available from Together.

GLM-4.6

fireworks logo
Fireworks
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.60/1MOutput Price:Output: $2.00/1M

Qwen2.5 7B Instruct

together logo
Together
Input Price:Input: $0.30/1MOutput Price:Output: $0.30/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Supports multimodal inputs
Higher GPQA score (81.0% vs 36.4%)
Alibaba Cloud / Qwen Team

Qwen2.5 7B Instruct

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.6
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct

FAQ

Common questions about GLM-4.6 vs Qwen2.5 7B Instruct

GLM-4.6 significantly outperforms across most benchmarks. GLM-4.6 is made by Zhipu AI and Qwen2.5 7B Instruct is made by Alibaba Cloud / Qwen Team. 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%. Qwen2.5 7B Instruct scores GSM8k: 91.6%, MT-Bench: 87.5%, HumanEval: 84.8%, MBPP: 79.2%, MATH: 75.5%.
Qwen2.5 7B Instruct is 1.8x cheaper for input tokens. GLM-4.6 costs $0.55/M input and $2.00/M output via fireworks. Qwen2.5 7B Instruct costs $0.30/M input and $0.30/M output via together.
GLM-4.6 supports 131K tokens and Qwen2.5 7B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include input pricing ($0.55 vs $0.30/M), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-4.6 is developed by Zhipu AI and Qwen2.5 7B Instruct is developed by Alibaba Cloud / Qwen Team.