Model Comparison

GLM-5 vs Qwen3.6-27B

Both models are evenly matched across the benchmarks. Qwen3.6-27B is 1.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while Qwen3.6-27B is better at 1 benchmark (Terminal-Bench 2.0).

Both models are evenly matched across the benchmarks.

Wed May 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3.6-27B costs less

For input processing, GLM-5 ($1.00/1M tokens) is 1.7x more expensive than Qwen3.6-27B ($0.60/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 1.1x cheaper than Qwen3.6-27B ($3.60/1M tokens).

In conclusion, GLM-5 is more expensive than Qwen3.6-27B.*

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

Lowest available price from all providers

Wed May 13 2026 • llm-stats.com

GLM-5

Input tokens$1.00

Output tokens$3.20

Best providerUnknown Organization

Qwen3.6-27B

Input tokens$0.60

Output tokens$3.60

Best providerNovita

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

716.2B diff

GLM-5 has 716.2B more parameters than Qwen3.6-27B, making it 2578.0% larger.

GLM-5

744.0Bparameters

Qwen3.6-27B

27.8Bparameters

744.0B

GLM-5

27.8B

Qwen3.6-27B

Context Window

Maximum input and output token capacity

Qwen3.6-27B accepts 262,144 input tokens compared to GLM-5's 200,000 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while Qwen3.6-27B is limited to 65,536 tokens.

GLM-5

Input200,000 tokens

Output128,000 tokens

Qwen3.6-27B

Input262,144 tokens

Output65,536 tokens

Wed May 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.6-27B supports multimodal inputs, whereas GLM-5 does not.

Qwen3.6-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text

Images

Audio

Video

Qwen3.6-27B

Text

Images

Audio

Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Qwen3.6-27B uses Apache 2.0.

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

GLM-5

MIT

Open weights

Qwen3.6-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Qwen3.6-27B was released on 2026-04-21.

Qwen3.6-27B is 2 months newer than GLM-5.

GLM-5

Feb 11, 2026

3 months ago

Qwen3.6-27B

Apr 21, 2026

3 weeks ago

2mo 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

GLM-5 is available from ZAI. Qwen3.6-27B is available from Novita.

GLM-5

Unknown Organization

Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Qwen3.6-27B

Novita

Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

GLM-5

View details

Zhipu AI

Less expensive output tokens

Higher SWE-Bench Verified score (77.8% vs 77.2%)

Qwen3.6-27B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Less expensive input tokens

Higher Terminal-Bench 2.0 score (59.3% vs 56.2%)

Detailed Comparison

AI Model Comparison Table
Feature	GLM-5	Qwen3.6-27B

FAQ

Common questions about GLM-5 vs Qwen3.6-27B.

Which is better, GLM-5 or Qwen3.6-27B?

Both models are evenly matched across the benchmarks. GLM-5 is made by Zhipu AI and Qwen3.6-27B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-5 compare to Qwen3.6-27B in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Qwen3.6-27B scores CountBench: 97.8%, VLMsAreBlind: 97.0%, V*: 94.7%, AIME 2026: 94.1%, HMMT 2025: 93.8%.

Is GLM-5 cheaper than Qwen3.6-27B?

Qwen3.6-27B is 1.7x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. Qwen3.6-27B costs $0.60/M input and $3.60/M output via novita.

What are the context window sizes for GLM-5 and Qwen3.6-27B?

GLM-5 supports 200K tokens and Qwen3.6-27B supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5 and Qwen3.6-27B?

Key differences include context window (200K vs 262K), input pricing ($1.00 vs $0.60/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Qwen3.6-27B?

GLM-5 is developed by Zhipu AI and Qwen3.6-27B is developed by Alibaba Cloud / Qwen Team.