Model Comparison

Qwen3.5-397B-A17B vs GLM-4.6

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. GLM-4.6 is 1.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Qwen3.5-397B-A17B outperforms in 5 benchmarks (BrowseComp, GPQA, Humanity's Last Exam, LiveCodeBench v6, SWE-Bench Verified), while GLM-4.6 is better at 0 benchmarks.

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.6 costs less

For input processing, Qwen3.5-397B-A17B ($0.60/1M tokens) is 1.1x more expensive than GLM-4.6 ($0.55/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 1.8x more expensive than GLM-4.6 ($2.00/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than GLM-4.6.*

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

Lowest available price from all providers

Thu Apr 16 2026 • llm-stats.com

Qwen3.5-397B-A17B

Input tokens$0.60

Output tokens$3.60

Best providerNovita

GLM-4.6

Input tokens$0.55

Output tokens$2.00

Best providerFireworks

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

40.0B diff

Qwen3.5-397B-A17B has 40.0B more parameters than GLM-4.6, making it 11.2% larger.

Qwen3.5-397B-A17B

397.0Bparameters

GLM-4.6

357.0Bparameters

397.0B

Qwen3.5-397B-A17B

357.0B

GLM-4.6

Context Window

Maximum input and output token capacity

Qwen3.5-397B-A17B accepts 262,144 input tokens compared to GLM-4.6's 131,072 tokens. GLM-4.6 can generate longer responses up to 131,072 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.

Qwen3.5-397B-A17B

Input262,144 tokens

Output64,000 tokens

GLM-4.6

Input131,072 tokens

Output131,072 tokens

Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3.5-397B-A17B and GLM-4.6 support multimodal inputs.

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

Qwen3.5-397B-A17B

Text

Images

Audio

Video

GLM-4.6

Text

Images

Audio

Video

License

Usage and distribution terms

Qwen3.5-397B-A17B is licensed under Apache 2.0, while GLM-4.6 uses MIT.

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

Qwen3.5-397B-A17B

Apache 2.0

Open weights

GLM-4.6

MIT

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while GLM-4.6 was released on 2025-09-30.

Qwen3.5-397B-A17B is 5 months newer than GLM-4.6.

Qwen3.5-397B-A17B

Feb 16, 2026

1 months ago

4mo newer

GLM-4.6

Sep 30, 2025

6 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

Qwen3.5-397B-A17B is available from Novita. GLM-4.6 is available from Fireworks, DeepInfra.

Qwen3.5-397B-A17B

Novita

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

GLM-4.6

Fireworks

Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M

Deepinfra

Input Price:Input: $0.60/1MOutput Price:Output: $2.00/1M

* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher BrowseComp score (69.0% vs 45.1%)

Higher GPQA score (88.4% vs 81.0%)

Higher Humanity's Last Exam score (28.7% vs 17.2%)

Higher LiveCodeBench v6 score (83.6% vs 82.8%)

Higher SWE-Bench Verified score (76.4% vs 68.0%)

GLM-4.6

View details

Zhipu AI

Less expensive input tokens

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3.5-397B-A17B	GLM-4.6

FAQ

Common questions about Qwen3.5-397B-A17B vs GLM-4.6

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3.5-397B-A17B is made by Alibaba Cloud / Qwen Team and GLM-4.6 is made by Zhipu AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

Qwen3.5-397B-A17B scores MMLU-Redux: 94.9%, HMMT 2025: 94.8%, C-Eval: 93.0%, HMMT25: 92.7%, IFEval: 92.6%. GLM-4.6 scores AIME 2025: 93.9%, LiveCodeBench v6: 82.8%, GPQA: 81.0%, SWE-Bench Verified: 68.0%, BrowseComp: 45.1%.

GLM-4.6 is 1.1x cheaper for input tokens. Qwen3.5-397B-A17B costs $0.60/M input and $3.60/M output via novita. GLM-4.6 costs $0.55/M input and $2.00/M output via fireworks.

Qwen3.5-397B-A17B supports 262K tokens and GLM-4.6 supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include context window (262K vs 131K), input pricing ($0.60 vs $0.55/M), licensing (Apache 2.0 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Qwen3.5-397B-A17B is developed by Alibaba Cloud / Qwen Team and GLM-4.6 is developed by Zhipu AI.

Qwen3.5-397B-A17B vs GLM-4.6

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

Input Capabilities

Qwen3.5-397B-A17B

GLM-4.6

License

Release Timeline

Knowledge Cutoff

Provider Availability

Qwen3.5-397B-A17B

GLM-4.6

Outputs Comparison

Key Takeaways

Qwen3.5-397B-A17B

GLM-4.6

Detailed Comparison

FAQ

Which is better, Qwen3.5-397B-A17B or GLM-4.6?

How does Qwen3.5-397B-A17B compare to GLM-4.6 in benchmarks?

Is Qwen3.5-397B-A17B cheaper than GLM-4.6?

What are the context window sizes for Qwen3.5-397B-A17B and GLM-4.6?

What are the main differences between Qwen3.5-397B-A17B and GLM-4.6?

Who makes Qwen3.5-397B-A17B and GLM-4.6?