Compare / GLM-4.7-Flash vs Qwen3 30B-A3B Thinking 2507
GLM-4.7-Flash vs Qwen3 30B-A3B Thinking 2507
Updated November 2026
Verdict
GLM-4.7-Flash scores higher on the Small LM Index: 66.1 vs 59.3, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, GLM-4.7-Flash wins 4 and Qwen3 30B-A3B Thinking 2507 wins 1. Both need about 20.4 GB of memory at 4-bit quantization. Only GLM-4.7-Flash has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted.
Pick GLM-4.7-Flash if…
- you want the strongest model overall
- you need math and reasoning
- you want a hosted API rather than running it yourself
- you want the more widely used model (more quantized builds and community support)
Pick Qwen3 30B-A3B Thinking 2507 if…
- you work with long documents
Specifications
| GLM-4.7-Flash | Qwen3 30B-A3B Thinking 2507 | |
|---|---|---|
| Small LM Index | 66.1 (likely 63–70) | 59.3 (likely 55–63) |
| Lab | Z.ai (GLM) · China | Alibaba (Qwen) · China |
| Released | 2026-01-19 | 2025-07-30 |
| Parameters | 31B (3B active, MoE) | 30.5B (3.3B active, MoE) |
| Memory at 4-bit | ~20.4 GB | ~20.4 GB |
| Context window | 200K tokens | 262K tokens |
| Input | text | text |
| Reasoning | Always thinks | Always thinks |
| License | MIT | Apache-2.0 |
| API price / 1M tokens | $0.15 ($0.061 in, $0.40 out) | Self-host only |
| Hugging Face downloads (30 days) | 956.4K | 66.6K |
Benchmarks
Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.
| Benchmark | GLM-4.7-Flash | Qwen3 30B-A3B Thinking 2507 |
|---|---|---|
| MMLU-Pro Knowledge | ≈ 82 | 80.9 |
| GPQA Diamond Knowledge | 75.2 | 73.4 |
| Humanity's Last Exam Reasoning | 14.4 | 9.8 |
| AIME Math | 91.6 | 85.0 |
| LiveCodeBench Coding | 64.0 | 66.0 |
| SWE-bench Verified Coding | 59.2 | 22.0* |
| τ²-Bench Agentic | 79.5 | — |
| IFBench Instruction following | — | 51.5 |
Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.
Questions
Which is better, GLM-4.7-Flash or Qwen3 30B-A3B Thinking 2507?
GLM-4.7-Flash scores higher on the Small LM Index: 66.1 vs 59.3, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, GLM-4.7-Flash wins 4 and Qwen3 30B-A3B Thinking 2507 wins 1.
Which is cheaper to run?
Only GLM-4.7-Flash has a hosted API on OpenRouter ($0.15 per million tokens); the other has to be self-hosted. Both need about 20.4 GB of memory at 4-bit quantization.
Which has the longer context window?
Qwen3 30B-A3B Thinking 2507 has the longer context window: 262K tokens against 200K.
Can I use GLM-4.7-Flash and Qwen3 30B-A3B Thinking 2507 commercially?
GLM-4.7-Flash is released under MIT and Qwen3 30B-A3B Thinking 2507 under Apache-2.0. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.
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