Best GLM Models in 2026 — Zhipu AI's Rise in the LLM Landscape

GLM-5 is the best GLM model in 2026 and the strongest open-weight model to come out of China's AI ecosystem so far, with 744 billion total parameters, 40 billion active per token, and a 77.8% score on SWE-bench Verified that puts it within three points of Claude Opus 4.6. Zhipu AI — the Tsinghua University spinoff now publicly traded on the Hong Kong Stock Exchange at a $44 billion market cap — has built the GLM family into a genuine third pole in the Chinese AI landscape alongside DeepSeek and Alibaba's Qwen.

This is the general GLM model review covering architecture, benchmarks, and competitive positioning. If you are looking for GLM models specifically inside OpenClaw: read Best GLM Models for OpenClaw, which covers provider configuration, model IDs, and workflow fit.

Who Is Zhipu AI?

Zhipu AI (now branded as Z.ai internationally) is a Chinese AI company founded in 2019 as a spinoff from Tsinghua University's Computer Science Department. As of April 2026, Zhipu is publicly traded on the Hong Kong Stock Exchange with a market capitalization of approximately $44.3 billion, making it one of the most valuable AI-native companies in the world.

The company raised $1.4 billion across 12 funding rounds before its IPO, backed by investors including Alibaba, Tencent, Meituan, Xiaomi, and Saudi Aramco's Prosperity7 Ventures. Zhipu's January 2026 IPO raised approximately $558 million, and the stock has more than quadrupled since listing.

Zhipu is considered the third-largest LLM market player in China according to IDC, behind Alibaba (Qwen) and Baidu (ERNIE), though its open-weight strategy and benchmark performance have arguably made GLM more influential in the developer ecosystem than raw market share suggests.

GLM Model Evolution: From GLM-130B to GLM-5

GLM-5 represents the fifth generation of Zhipu's General Language Model family, and the architectural leap from GLM-4.x to GLM-5 is the largest in the family's history.

The original GLM-130B, released in 2022, was an early bilingual pre-trained model from Tsinghua researchers. GLM-4, released in early 2025, introduced the MoE architecture with 355 billion total parameters and 32 billion active. GLM-4.5 and GLM-4.7 refined the approach through mid-2025, with GLM-4.7 achieving strong multilingual performance including 66.7% on SWE-bench Multilingual.

GLM-5, released February 11, 2026, scaled to 744 billion total parameters with 40 billion active per token. The architecture uses 256 experts with 8 activated per token (a 5.9% sparsity rate), combined with DeepSeek-style Multi-head Latent Attention (MLA) and Dynamic Sparse Attention (DSA) for efficient long-context processing up to 200,000 tokens.

Two details stand out from a hardware perspective. First, the entire 28.5 trillion token training run was executed on Huawei Ascend AI processors using the MindSpore framework — not NVIDIA GPUs. Second, GLM-5's maximum output length reaches 131,000 tokens, among the highest of any current model.

Benchmark Comparison vs Chinese Competitors

GLM-5 currently leads BenchLM's Chinese model leaderboard with a composite score of 85, followed by GLM-5.1 at 84 and Qwen3.5 397B at 81. The gap matters most on coding and agentic tasks, where GLM-5 has pulled ahead of both DeepSeek V3 and Qwen3.

The picture is not a clean sweep. DeepSeek V3.2 remains the strongest on pure mathematical reasoning with an AIME 2025 score of 89.3. Qwen3-235B leads on ArenaHard at 95.6. But GLM-5's edge is clearest on software engineering and agentic benchmarks — scoring 77.8% on SWE-bench Verified, just three points behind Claude Opus 4.6's 80.8%.

GLM-5.1, released as a follow-up tuning, reaches 94% of Claude Opus 4.6's coding performance and leads on SWE-Bench Pro at 58.4 — the benchmark that tests the hardest multi-file engineering tasks.

The competitive context matters here. As of April 2026, the three-way race between GLM, DeepSeek, and Qwen means no single Chinese model family dominates every category. GLM-5 wins on coding and agent workloads. DeepSeek wins on math reasoning. Qwen wins on general versatility and ecosystem breadth.

Bilingual Strengths and Multilingual Support

GLM-5 natively supports English, Chinese, and 15+ additional languages, and its bilingual Chinese-English performance is the strongest differentiator against Western frontier models. According to independent evaluations, GLM-5 matches or exceeds GPT-4's performance on Chinese language understanding and generation tasks.

This matters for three practical reasons. First, any workflow involving Chinese-language documents, customer communication, or market research gets measurably better results from a model trained natively on Chinese data at scale. Second, cross-lingual tasks — translating between Chinese and English, summarizing Chinese sources in English, or generating bilingual content — are where GLM-5 has the widest advantage over models like Llama or Mistral that treat Chinese as a secondary language.

Third, GLM-4.7 already scored 66.7% on SWE-bench Multilingual, and GLM-5 extends that lead. For teams building products that serve both Chinese and English-speaking markets, the bilingual capability avoids the need to maintain separate model stacks for each language.

That said, Qwen3 also has strong multilingual coverage — Qwen3.5 supports 201 languages and dialects. The GLM advantage is most pronounced in native Chinese quality rather than raw language count.

Pricing and API Access

GLM-5 costs $1.00 per million input tokens and $3.20 per million output tokens on Zhipu's API, which represents a roughly 30% increase over GLM-4.7's pricing. As of April 2026, this is still approximately 3x cheaper than Claude Sonnet for input and 5x cheaper for output.

The free tier is worth highlighting. GLM-4.7-Flash and GLM-4.5-Flash are both available at zero cost to all registered users on Zhipu's platform. GLM-4.7-Flash offers a 203K context window with only 3B active parameters, making it one of the strongest free models available from any provider.

GLM-5 is available through Z.ai's API platform, WaveSpeed API, and several third-party providers. The open-weight release under MIT License means self-hosting is an option for teams with the hardware to support a 744B-parameter model.

Limitations and Tradeoffs

GLM-5 is not the right choice for every use case, and treating it as a universal replacement for other frontier models would be a mistake.

Hardware requirements for self-hosting are extreme. A 744B MoE model with 40B active parameters is not something you run on consumer hardware. Even with quantization, self-hosting GLM-5 requires multi-GPU setups that put it out of reach for most independent developers. The free GLM-4.7-Flash is a better starting point for local-scale work.

Mathematical reasoning trails DeepSeek. While GLM-5 leads on coding and agent benchmarks, DeepSeek V3.2 still holds the edge on pure math with an AIME 2025 score of 89.3 vs GLM-5's approximately 85. If your workload is math-heavy, DeepSeek remains the stronger pick.

The 30% price increase from GLM-4.7 matters for high-volume workloads. Zhipu is the first major Chinese provider to raise prices in 2026, and for cost-sensitive production use, GLM-4.7 at $0.60/M input may still be the better value if GLM-5's extra capability is not needed.

Ecosystem breadth is narrower than Qwen. Alibaba's Qwen family spans more model sizes, more modalities, and more hosting options. Zhipu's lineup is smaller and more focused — which can be an advantage for simplicity but a limitation if you need a family of models at every size tier.

Related Guides

• Best GLM Models for OpenClaw
• Best Chinese Models in 2026
• Best DeepSeek Models in 2026
• Best Ollama Models in 2026

FAQ

What is the best GLM model in 2026?

GLM-5 is the best GLM model in 2026 for frontier-level work, scoring 77.8% on SWE-bench Verified with 744B total parameters and 40B active per token. For free usage, GLM-4.7-Flash is the strongest zero-cost option with a 203K context window.

How does GLM-5 compare to DeepSeek V3?

GLM-5 leads DeepSeek V3.2 on coding and agentic benchmarks — 77.8% vs roughly 72% on SWE-bench Verified. DeepSeek V3.2 is stronger on mathematical reasoning with an AIME 2025 score of 89.3 compared to GLM-5's approximately 85. DeepSeek is also cheaper per token for high-volume use.

How much does GLM-5 cost?

GLM-5 costs $1.00 per million input tokens and $3.20 per million output tokens on Zhipu's API, as of April 2026. This is roughly 3x cheaper than Claude Sonnet for input tokens. GLM-4.7-Flash and GLM-4.5-Flash are free for all registered users.

Is GLM-5 open source?

GLM-5 is released under the MIT License as an open-weight model, meaning the weights are freely downloadable for commercial and research use. The training was done entirely on Huawei Ascend processors, making it one of the few frontier models trained without NVIDIA hardware.

Is GLM-5 good for Chinese language tasks?

GLM-5 is the strongest model for bilingual Chinese-English workloads in 2026. It natively supports Chinese and English plus 15+ additional languages, and independent evaluations show it matching or exceeding GPT-4 on Chinese language understanding. For cross-lingual workflows involving both languages, it is the default recommendation.