Compare / GLM-4.7-Flash vs Qwen3-Coder 30B-A3B

GLM-4.7-Flash vs Qwen3-Coder 30B-A3B

Updated November 2026

Verdict

GLM-4.7-Flash scores higher on the Small LM Index: 66.0 vs 34.9. On the 8 benchmarks both labs report, GLM-4.7-Flash wins 8 and Qwen3-Coder 30B-A3B wins 0. Both need about 20.4 GB of memory at 4-bit quantization. Their API prices are similar: $0.15 and $0.12 per million tokens.

Pick GLM-4.7-Flash if…

you want the strongest model overall
you mostly write code
you need math and reasoning
you need precise instruction following
you build agents that call tools

Pick Qwen3-Coder 30B-A3B if…

you work with long documents

Specifications

	GLM-4.7-Flash	Qwen3-Coder 30B-A3B
Small LM Index	66.0 (likely 64–68)	34.9 (likely 32–38)
Lab	Z.ai (GLM) · China	Alibaba (Qwen) · China
Released	2026-01-19	2025-07-31
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	No thinking
License	MIT	Apache-2.0
API price / 1M tokens	$0.15 ($0.061 in, $0.40 out)	$0.12 ($0.070 in, $0.28 out)
Hugging Face downloads (30 days)	961.9K	1.3M

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-Coder 30B-A3B
MMLU-Pro Knowledge	≈ 83	70.6^✓
GPQA Diamond Knowledge	58.1^✓	51.6^✓
Humanity's Last Exam Reasoning	7.6^✓	3.8^✓
AIME Math	91.6	29.7^✓
LiveCodeBench Coding	64.0	40.3^✓
SWE-bench Verified Coding	59.2	51.6
τ²-Bench Agentic	98.8^✓	34.5^✓
IFBench Instruction following	60.8^✓	32.7^✓
MATH-500 Math	—	89.3^✓
AA-LCR Long context	41.7^✓	32.7^✓

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-Coder 30B-A3B?

GLM-4.7-Flash scores higher on the Small LM Index: 66.0 vs 34.9. On the 8 benchmarks both labs report, GLM-4.7-Flash wins 8 and Qwen3-Coder 30B-A3B wins 0.

Which is cheaper to run?

Their API prices are similar: $0.15 and $0.12 per million tokens. Both need about 20.4 GB of memory at 4-bit quantization.

Which has the longer context window?

Qwen3-Coder 30B-A3B has the longer context window: 262K tokens against 200K.

Can I use GLM-4.7-Flash and Qwen3-Coder 30B-A3B commercially?

GLM-4.7-Flash is released under MIT and Qwen3-Coder 30B-A3B 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.