GLM 5.2 vs Qwen 3: Chinese Open-Weight AI Models Compared

You need a capable open-weight model for a production workload, and you have narrowed the field to two Chinese flagship systems: GLM 5.2 from Zhipu AI and Qwen 3 235B-A22B from Alibaba Cloud. Both compete at the frontier on raw intelligence — the decision comes down to cost per token, context length, inference speed, and how much your workload depends on agentic tasks.

The short version: GLM 5.2 scores 51 on the Artificial Analysis Intelligence Index; Qwen 3 235B-A22B sits in the same ~50–52 range, making them essentially tied on general reasoning ability. What separates them is everything else. GLM 5.2 runs at 158 tokens per second — one of the three fastest frontier models globally per Artificial Analysis — handles a 1M-token context window, and posts 62.1% on SWE-bench Pro and 78% on Terminal-Bench v2.1, among the strongest publicly reported agentic numbers available. Qwen 3 costs 73% less per output token ($1.20/M vs $4.40/M), which at large scale adds up fast.

Choose GLM 5.2 if you need to process long documents or entire codebases in a single pass, run autonomous software agents, or need near-real-time response speeds in a latency-sensitive product.

Choose Qwen 3 235B-A22B if you are running high-volume generation pipelines where output token costs dominate your budget, your content fits comfortably within 128K tokens, and you can trade some agentic capability for significant cost savings.

Quick Comparison

Both models are open-weight and available on HuggingFace. GLM 5.2 is at THUDM/GLM-5.2; Qwen 3 is at Qwen/Qwen3-235B-A22B. Neither requires a commercial license fee for self-hosting.

Benchmark Performance

On standard academic benchmarks the two models converge. GPQA Diamond — a graduate-level science reasoning test used to proxy expert-level reasoning — comes in at 89% for GLM 5.2 and approximately 85% for Qwen 3. HumanEval, which measures Python code generation, shows both models above 90%.

The more meaningful gap appears on agentic evaluations. SWE-bench Pro tests a model's ability to resolve real GitHub issues autonomously across entire codebases. GLM 5.2's 62.1% is among the highest publicly reported results on this benchmark. Terminal-Bench v2.1, which measures multi-step CLI task completion, shows GLM 5.2 at 78%. Comparable public numbers for Qwen 3 on these two agentic benchmarks are not available, which suggests either that they have not been run or that results were not published — a meaningful signal if agentic reliability is your primary concern.

Both models support extended thinking (chain-of-thought reasoning before output), which boosts performance on complex multi-step problems. This feature is available via API on both Z.ai (GLM 5.2) and Aliyun or OpenRouter (Qwen 3).

Pricing Breakdown

For most workloads, output tokens dominate cost. At $4.40/M for GLM 5.2 versus $1.20/M for Qwen 3, you pay roughly 3.7x more per output token with GLM 5.2. On a workload generating 100M output tokens per month, that is $440,000 versus $120,000 — a $320,000 monthly difference.

GLM 5.2 partially offsets this with prompt caching at $0.26/M for cache hits. If your application reuses a large system prompt or document prefix — a common pattern in RAG pipelines and agentic systems — effective input cost can drop substantially. No equivalent published cache pricing is listed for Qwen 3.

The cost calculation shifts when you factor in context length requirements. If you need to process a 500K-token legal document or an entire open-source repository in one shot, Qwen 3 cannot do it. Its 128K context limit means you must chunk the document, run multiple calls, and merge results. That engineering overhead, plus any accuracy lost at chunk boundaries, is a real cost that does not appear on the pricing page.

For latency-sensitive applications — chatbots, real-time coding assistants, interactive agents — GLM 5.2's speed advantage also translates to a better user experience without horizontal scaling. At roughly 3x the throughput of Qwen 3, you can serve three times the simultaneous users with the same inference infrastructure. For a deeper look at GLM 5.2's pricing tiers and cache behavior, see the GLM 5.2 pricing breakdown.

Speed and Latency

According to Artificial Analysis benchmarks, GLM 5.2 ranks third among all frontier models on throughput — sitting just behind the fastest systems and well ahead of most. At 158 tokens per second, a 2,000-word (~2,500 token) response completes in roughly 16 seconds. At 55 t/s (midpoint estimate for Qwen 3), the same response takes about 45 seconds.

In a user-facing chat product, the difference between 16 and 45 seconds is the difference between a usable and an unusable experience. For batch processing jobs that run overnight, the throughput difference matters less — though it still affects your required instance count and infrastructure budget.

GLM 5.2's time to first token of 1.54 seconds is competitive. Users perceive the first token as the start of a response, so a short TTFT makes the model feel snappy even before the full answer arrives. Public TTFT figures for Qwen 3 via third-party APIs are not consistently benchmarked; results vary by provider.

Context Window

GLM 5.2 supports a context window of 1,048,576 tokens — commonly called 1M context. Qwen 3 235B-A22B supports 128,000 tokens. The practical gap is 8x.

The difference matters in several concrete scenarios:

• Legal and financial documents. A large contract or 10-K filing can exceed 200,000 tokens. GLM 5.2 processes the entire document in one call; Qwen 3 requires chunking and merging.
• Codebase analysis. A medium-sized open-source repository often runs 500K–1M tokens of source code. GLM 5.2 can ingest the full repository context; Qwen 3 cannot without significant preprocessing.
• Long-form research synthesis. Multi-document research tasks — academic papers, news archives, earnings call transcripts — benefit heavily from large context. At 1M tokens, GLM 5.2 can hold roughly 750 research articles in context simultaneously.
• Agentic memory. Autonomous agents accumulate long conversation histories across many tool calls. A 1M context means the agent rarely needs to summarize or discard earlier context, reducing hallucination risk from context truncation.

For applications where 128K is genuinely sufficient — most chatbots, short document Q&A, code generation from small functions — the context advantage of GLM 5.2 does not matter, and Qwen 3's lower cost becomes the dominant factor.

License and Self-Hosting

Both models are equally open by practical standards. GLM 5.2 is released under the MIT license; Qwen 3 uses Apache 2.0. Both allow commercial use, fine-tuning, and redistribution without royalties. MIT is marginally less restrictive on attribution requirements, but for most organizations this distinction is immaterial.

Self-hosting the full model weights is feasible for both, though deploying a 753B-parameter system — even with only 40B active at any moment under MoE — requires substantial GPU infrastructure. Most teams opt for API access via Z.ai (GLM 5.2) or Aliyun and OpenRouter (Qwen 3) rather than running the flagship model in-house. Smaller distilled variants are available in both families for local or on-premise inference with more modest hardware requirements.

Neither model currently supports image or audio input in its flagship version. Qwen3-VL (a separate model from Alibaba) adds vision capability; GLM 5.2 is text-only as of this writing. If multimodal input is a hard requirement, evaluate models specifically designed for that modality rather than either flagship compared here.

When to Choose GLM 5.2

• You need to process documents, repositories, or conversation histories longer than 128K tokens
• Your application requires autonomous agents that write and execute code, file issues, or complete multi-step CLI tasks
• Response latency is user-facing and 158 t/s vs ~55 t/s translates to a measurable UX difference
• You want the strongest publicly documented agentic benchmark numbers (62.1% SWE-bench Pro, 78% Terminal-Bench v2.1)
• Your workload benefits from prompt caching — the $0.26/M cache read price makes long system prompts economical at scale
• You prefer MIT licensing for maximum downstream flexibility

When to Choose Qwen 3 235B-A22B

• Output token costs dominate your budget and 73% savings at $1.20/M versus $4.40/M is the decisive factor
• Your documents and conversations reliably fit within 128K tokens
• You are already embedded in the Alibaba Cloud ecosystem (Aliyun, DashScope) and want a single provider relationship
• You want access to the broader Qwen model family — Qwen2.5-Coder for code-specific tasks, Qwen-VL for vision — under a unified vendor
• Your use case is offline batch generation where throughput speed matters less than cost per token

Frequently Asked Questions

Are GLM 5.2 and Qwen 3 comparable in overall intelligence?

Yes — both score in the 50–52 range on the Artificial Analysis Intelligence Index, which aggregates performance across dozens of standardized benchmarks. They are peer-level models on general reasoning. The differentiation between them comes from speed, context length, agentic capability, and price rather than raw intelligence score.

Which model is better for coding tasks?

Both score above 90% on HumanEval. GLM 5.2 separates itself on real-world software engineering: 62.1% on SWE-bench Pro (which tests actual GitHub issue resolution on real repositories) and 78% on Terminal-Bench v2.1 (multi-step CLI task completion). If you are building a coding assistant that needs to work autonomously across entire codebases, GLM 5.2's agentic benchmarks are more relevant than HumanEval alone.

Can I use GLM 5.2 for free?

GLM 5.2 is available for free testing without an API key at glm5.app/chat. For production API usage, Z.ai pricing applies: $1.40/M input tokens, $4.40/M output tokens, and $0.26/M for cache reads. The model is also accessible via OpenRouter at the identifier z-ai/glm-5.2.

What is the difference between total and active parameters in MoE models?

Both models use Mixture of Experts (MoE) architecture, where only a subset of parameters are active during any single forward pass. GLM 5.2 has 753B total parameters but activates 40B per token; Qwen 3 has 235B total with 22B active. In practice this means inference compute — and therefore cost — scales with active parameters, not total parameters. Neither model is as expensive to run as a comparable dense 750B or 235B model would be.

Does either model support extended thinking?

Yes. Both GLM 5.2 and Qwen 3 235B-A22B support extended thinking (also called chain-of-thought or reasoning mode), where the model works through a problem internally before producing output. This significantly boosts performance on complex math, logic, and multi-step coding tasks. The feature is available via API parameters on both Z.ai and OpenRouter.

Which model is better for processing long documents?

GLM 5.2 by a wide margin. Its 1M-token context can hold an entire book, legal contract, large codebase, or multi-year earnings transcript in a single API call. Qwen 3's 128K limit requires chunking anything longer, which introduces engineering complexity and risks losing cross-chunk relationships that matter for accurate synthesis.

Can I self-host either model?

Both models are open-weight and hosted on HuggingFace. GLM 5.2 weights are at THUDM/GLM-5.2 under an MIT license; Qwen 3 is at Qwen/Qwen3-235B-A22B under Apache 2.0. Full self-hosting of the flagship models requires significant GPU infrastructure. Smaller distilled variants in both families are available for local or on-premise deployment with more modest hardware requirements.

Bottom Line

GLM 5.2 and Qwen 3 235B-A22B are the two strongest Chinese open-weight models available today, and they serve different optimization targets. Qwen 3 is the most cost-efficient frontier-class model for high-volume text generation at $1.20/M output tokens. GLM 5.2 is the stronger choice when context length, inference speed, or agentic benchmark performance are requirements rather than nice-to-haves — and its 158 t/s throughput, 1M context, and leading SWE-bench results are genuinely differentiated at the frontier. Neither is universally better; the right answer depends on your token budget and what you are building.

Try GLM 5.2 — no API key needed: glm5.app/chat.

Sources

• Artificial Analysis Intelligence Index — GLM 5.2
• GLM 5.2 on HuggingFace (THUDM/GLM-5.2)
• Qwen 3 235B-A22B on HuggingFace
• Z.ai API documentation
• Artificial Analysis Speed Benchmarks — GLM 5.2 Providers