GLM-5.3 vs Qwen3.7 Max: Specs & Benchmark Comparison
GLM-5.3 is developed by Zhipu AI, while Qwen3.7 Max comes from Alibaba. Qwen3.7 Max was released in May 2026, and GLM-5.3 followed 3 months later in August 2026. GLM-5.3 has a published size of about 753 billion parameters; Alibaba has not disclosed the parameter count of Qwen3.7 Max.
The two models share 2 published benchmarks. GLM-5.3 leads on 2 of them. The widest gaps are on Humanity's Last Exam, where GLM-5.3 scores 62.5% against 41.4%; NL2Repo, where GLM-5.3 scores 58.0% against 47.2%. Averaged across everything we track, GLM-5.3 sits at 52.2% and Qwen3.7 Max at 73.6%.
GLM-5.3 is the cheaper API at $1.40 per million input tokens and $4.40 per million output tokens, about 79% below Qwen3.7 Max at $2.50 and $7.50. Both accept a context window of about 1M tokens.
| Characteristic | GLM-5.3 | Qwen3.7 Max |
|---|---|---|
| Company | Zhipu AI | Alibaba |
| Release Date | August 14, 2026 | May 19, 2026 |
| Parameters | 753B | — |
| Multimodal | No | No |
| Context (input) | 1.0M | 1.0M |
| Context (output) | 131K | 66K |
| Input Price / 1M | $1.40 | $2.50 |
| Output Price / 1M | $4.40 | $7.50 |
| Average Score | 52.2% | 73.6% |
| Benchmarks | ||
| Humanity's Last Exam | 62.5% | 41.4% |
| NL2Repo | 58.0% | 47.2% |
Visual Benchmark Comparison
Verdict
GLM-5.3 leads in 3 out of 4 comparison categories.
Both models show comparable average scores: GLM-5.3 — 0.5, Qwen3.7 Max — 0.7.
GLM-5.3 is 1.7x cheaper: input $1.40/1M vs $2.50/1M tokens.
GLM-5.3 supports a larger context: 1M vs 1M tokens.
GLM-5.3 is newer: released 8/14/2026 vs 5/19/2026.
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Frequently Asked Questions
Which is better for coding — GLM-5.3 or Qwen3.7 Max?
Which model is cheaper — GLM-5.3 or Qwen3.7 Max?
Which has a larger context window — GLM-5.3 or Qwen3.7 Max?
The GLM-5.3 and Qwen3.7 Max comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the GLM-5.3 or Qwen3.7 Max page. See also the complete list of AI model comparisons.