SAT, OCTOBER 17, 2026
Independent · In‑Depth · Practitioner‑Tested
LLMs

Beam vs GLM-5.3: Eight Points, or a Third the Cost?

What an eight-point benchmark gap is worth once you are paying per token

🕐 6 min read 👁 9 views 📅 Oct 17, 2026

GLM-5.3 scores 88.2 on Terminal-Bench v2.1. Beam scores 80.1. On a leaderboard that settles it. On an invoice it does not, which is the whole reason this comparison is worth eight points of your attention.

Head to head

 Reflection BeamGLM-5.3
Terminal-Bench v2.180.188.2
SWE-Bench Verified80.9Not directly comparable
Active parameters23B of 501BLarger active set
LicenceApache 2.0Open weights

The efficiency argument, stated carefully

Reflection's comparison is against GLM-5.2, not 5.3: comparable reasoning quality at three to four times less inference compute, roughly 25,000 tokens per attempt against 80,000. Beam's own score sits one point below GLM-5.2 and eight below 5.3. So the honest framing is that Beam trades a small quality gap against 5.2 for a large cost gap, and a larger quality gap against 5.3 for a cost gap nobody has measured.

The figures are Reflection's, computed as 2 x active parameters x mean generated tokens, excluding prefill, attention and serving overhead. Nobody has independently reproduced them.

Decision framework

  • Hard tasks, low volume → GLM-5.3. Eight points on agent benchmarks is real, and at low volume you will not feel the tokens.
  • High-volume agent loops → Beam. Retries multiply token spend, and a cheaper attempt changes the shape of what you can afford to run.
  • Strict licence review → Beam. Apache 2.0 is the cleanest answer to a legal team.
  • Undecided → measure your tokens per completed task for a week first. Without that number this comparison has no answer.

FAQ

Which is better, Beam or GLM-5.3?

GLM-5.3 on measured capability — 88.2 against 80.1 on Terminal-Bench v2.1. Beam's case is cost per attempt, which depends on your workload rather than on the model.

Are Beam's efficiency numbers verified?

No. They are Reflection's own estimates using a formula that excludes prefill, attention and serving overhead. Treat them as a direction, not a measurement.

Is either one usable commercially?

Beam is Apache 2.0, which permits commercial use, modification and redistribution with attribution. Check GLM's current licence terms directly before deploying.

Related: Reflection opens Beam

⚖ Our Verdict

GLM-5.3 on measured capability. Beam only if you have actually measured your token spend per completed task and the number hurts.