Head to head

Beam vs GLM 5.3

0:12

Beam wins 0 of 12 shared benchmarks

Verdict

GLM 5.3 is clearly stronger on paper. It leads on all 12 shared benchmarks, by wide margins on DeepSWE, SWE Atlas and AutomationBench, and by less than a point on AA-LCR. Both GLM models are 753-billion-parameter MoEs with about 40 billion active per token, so Beam's case against GLM 5.3 rests on its smaller active size and cost per token, not on scores.

Benchmark by benchmark

Benchmark	Beam	GLM 5.3	Difference
DeepSWE v1.1	44.4	61.0	-16.6
SWE-Bench Pro v2-Hard	77.2	84.3	-7.1
Terminal-Bench v2.1	80.1	88.2	-8.1
SWE Atlas Codebase QnA	34.6	61.0	-26.4
Humanity's Last Exam (no tools)	36.2	42.3	-6.1
SciCode	49.7	59.0	-9.3
CritPt (AA)	16.3	19.1	-2.8
GPQA Diamond	90.5	91.7	-1.2
AutomationBench (public)	37.0	48.2	-11.2
MCP Atlas	78.7	84.2	-5.5
AA-LCR (long context)	79.3	79.7	-0.4
AA Omniscience index (public split)	13.0	22.6	-9.6

All scores are from Reflection's announcement (October 8, 2026 revision). Scores for GLM 5.3 are as cited by Reflection from Artificial Analysis and DataCurve.

The two models

	Beam	GLM 5.3
Developer	Reflection AI	Z.ai (Zhipu AI)
Based in	United States	China
Parameters (total / active)	501B / 23B	753B / 40B
Weights	Apache 2.0, promised for later in October 2026	GLM-5.3 License (MIT-like, review above $10B revenue)
Released	October 5, 2026	August 27, 2026
How to access	Waitlisted beta API; weights coming	Hugging Face, Z.ai API, OpenRouter