Head to Head

zai-org/GLM-5.1 vs z-lab/Qwen3.6-35B-A3B-DFlash

Pricing, experience, and what the community actually says.

zai-org/GLM-5.1

zai-org/GLM-5.1

Starting at

$1.40 / 1M input tokens

Refund

Pay-as-you-go model; no refunds on consumed tokens. Unused credits may expire per provider terms.

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★ Our Pick

z-lab/Qwen3.6-35B-A3B-DFlash

z-lab/Qwen3.6-35B-A3B-DFlash

Starting at

0

Refund

Open-weight model; no refunds applicable.

Try Free →

Our Take

zai-org/GLM-5.1zai-org/GLM-5.1

“Worth it for developers and enterprises needing a highly capable, commercially permissive model for software engineering and complex multi-step agents, provided latency and token costs fit the budget.”

GLM-5.1 delivers frontier-level reasoning and coding performance under an open MIT license, but its high token cost and slower inference speed make it best suited for specialized, high-value tasks rather than high-volume, low-latency applications.

z-lab/Qwen3.6-35B-A3B-DFlashz-lab/Qwen3.6-35B-A3B-DFlash

“Yes for developers and researchers with adequate GPU resources who prioritize open licensing, local deployment, and agentic coding workflows.”

A highly capable open-weight MoE model that delivers strong coding and reasoning performance with efficient inference, though it requires substantial local hardware and technical setup.

Pros & Cons

zai-org/GLM-5.1

✓Strong multi-step reasoning and coding performance
✓Commercially permissive MIT license
✓Large 200k context window
✓Open-weight with transparent architecture
✓High benchmark scores (Intelligence Index: 51)
✗Higher token pricing compared to many open models
✗Slower inference speed (~44 t/s)
✗High verbosity increases output costs
✗Text-only input/output requires separate vision models
✗Heavy hardware requirements for self-hosting

z-lab/Qwen3.6-35B-A3B-DFlash

✓Strong coding and repository-level reasoning
✓Efficient MoE architecture reduces active compute
✓Thinking preservation improves iterative workflows
✓Permissive Apache 2.0 licensing
✓Compatible with major open-source inference frameworks
✗Requires ~24GB VRAM for full deployment
✗Setup and optimization require technical expertise
✗No official enterprise support or SLA
✗Raw inference speed depends heavily on backend configuration

Full Breakdown

Category
zai-org/GLM-5.1zai-org/GLM-5.1
z-lab/Qwen3.6-35B-A3B-DFlashz-lab/Qwen3.6-35B-A3B-DFlash

Overall Rating

4.2 / 5
★4.3 / 5

Starting Price

$1.40 / 1M input tokens
0

Learning Curve

Moderate. Requires familiarity with OpenAI-compatible SDKs, prompt engineering for reasoning modes, and token budget management due to verbosity.
Moderate to high; requires familiarity with LLM inference frameworks (vLLM, SGLang, Transformers) and hardware optimization.

Best Suited For

Software engineering teams, AI agent developers, and researchers requiring strong multi-step reasoning and open-weight deployment flexibility.
Software engineers, AI researchers, and developers building local or self-hosted AI agents, code assistants, and long-context applications.

Support Quality

Standard developer documentation and community support via GitHub and Hugging Face. No dedicated enterprise SLA is publicly advertised for the open-weight version.
Community-driven support via Hugging Face discussions, GitHub issues, and developer forums. No official enterprise SLA.

Hidden Costs

High verbosity can significantly increase output token consumption. Self-hosting requires substantial GPU infrastructure due to the 754B parameter size.
Hardware requirements (24GB+ VRAM) and potential cloud GPU rental fees for inference hosting.

Refund Policy

Pay-as-you-go model; no refunds on consumed tokens. Unused credits may expire per provider terms.
Open-weight model; no refunds applicable.

Platforms

Cloud API, Self-hosted (GPU), Hugging Face, ModelScope
Linux, macOS, Windows, Cloud GPU Instances

Features

Watermark on Free Plan

✗ No
✗ No

Mobile App

✗ No
✗ No

API Access

✓ Yes
✓ Yes
zai-org/GLM-5.1 Review →Try Free →
z-lab/Qwen3.6-35B-A3B-DFlash Review →Try Free →