GLM 5.2 Beats GPT-5.5 on Coding Benchmarks with 1M Context & 6× Lower Cost 

GLM-5.2 is not just another model release. It redefines what “long-horizon” coding means. Z.ai released GLM-5.2 on June 16, 2026, a 753-billion-parameter open-weights model with a stable 1-million-token context window, built specifically for long, complex coding tasks. GLM-5.2 beats OpenAI’s GPT-5.5 on multiple coding benchmarks while costing roughly one-sixth as much to operate. It’s available immediately on Hugging Face, the Z.ai API, and more than 20 third-party coding environments. For developers running repo-scale agents, that context size alone changes what’s technically possible today. 

Why a 1M-Token Context Window Matters for Coding

GLM-5.2 supports a 1,000,000-token context window, labeled glm-5.2[1m], with up to 131,072 output tokens per response. That capacity is roughly five times larger than its predecessor. 

• GLM-5.1’s context window topped out at 200,000 tokens, a fraction of what GLM-5.2 now supports.
• The 1M-token context stably sustains long, messy coding-agent trajectories without losing track of earlier instructions.
• GLM-5.2 introduces a dual thinking-effort system, labeled High and Max, to balance capability against latency and compute cost.
• GLM-5.2 is built on the same 744-billion-parameter Mixture-of-Experts architecture as the original GLM-5 release.

The Benchmark Numbers Behind the GPT-5.5 Comparison

Where GLM-5.2 Wins Outright

On SWE-bench Pro, a benchmark testing real-world software engineering tasks, GLM-5.2 scored 62.1, decisively outperforming GPT-5.5’s 58.6. That gap is significant for a model running at a fraction of the price.

• On FrontierSWE, which measures long-horizon task completion, GLM-5.2 scored 74.4% against GPT-5.5’s 72.6%.
• GLM-5.2 came close to Anthropic’s Claude Opus 4.8, which scored 75.1% on the same FrontierSWE test.
• On MCP-Atlas, a tool-usage benchmark, GLM-5.2 scored 77.0, ahead of GPT-5.5’s 75.3.
• GLM-5.2 posted 81.0 on Terminal-Bench 2.1, the highest score among all open-source models tested.

The Pricing Gap That Makes 1M Context Affordable

GLM-5.2’s context window would matter far less if it carried GPT-5.5’s price tag. It doesn’t. GPT-5.5 charges $5 per million input tokens and $30 per million output tokens, available only through OpenAI’s closed API. Running a million-token context at that rate would be prohibitively expensive for most teams.

GLM-5.2’s enterprise subscription tiers start at just $12.60 per month, with the core model weights available under an unrestricted MIT open-source license. That license structure lets businesses download the model freely and run it on their own infrastructure for the cost of compute alone.

Open Weights Make the Context Window Usable at Scale

The full weights are live on Hugging Face under the handle zai-org/GLM-5.2, meaning developers can use, modify, and commercially deploy the model with essentially zero restrictions. That’s a meaningful departure from how closed models like GPT-5.5 are licensed.

Z.ai’s documentation explicitly states the license guarantees “no regional limits” and allows “technical access without borders.” Enterprises with sovereign data or regulated workloads can now self-host a model running a context window once reserved for premium closed APIs. Companies tracking this shift closely include Microsoft (NASDAQ: MSFT), Alphabet (NASDAQ: GOOGL), and Nvidia (NASDAQ: NVDA), all tied to compute demand for models at this scale.

Final Thoughts

GLM-5.2 marks the third major release in Z.ai’s GLM-5 family within months, a pace few AI labs have matched. The combination of a 1-million-token context window, benchmark wins over GPT-5.5, and a fraction of the operating cost gives engineering teams a genuine reason to reconsider their default model choice. Whether OpenAI responds with a price adjustment or a new release will likely become clear within weeks, given how fast this rivalry keeps moving.

Disclaimer

The content shared by Meyka AI PTY LTD is solely for research and informational purposes. Meyka is not a financial advisory service, and the information provided should not be considered investment or trading advice.