the first open-weights model from Reflection, a 501B-total, 23B-active mixture-of-experts built for agentic tool use, coding and general reasoning. it reads text only and its weights ship under Apache 2.0.
pick it if
you want a long-window text model priced with the flash tier
you want Apache 2.0 weights, with BF16, FP8 and NVFP4 builds to self-host
you run tool-calling agents on text and repeat long prompts, so cached input matters
skip it if
you need image, audio or video input; Beam reads text only
long autonomous coding is the job; its reported DeepSWE v1.1 score sits below GLM-5.3 Flash and DeepSeek V4.1 Flash
GLM-5.3 is Z.ai's open-weight, text-only reasoning model for coding and long-horizon tasks. it keeps GLM-5.2's base model and price, with its gains coming from post-training, and reads up to 1M tokens of context.
pick it if
you want open weights for coding and long-horizon tasks
you work with long inputs: 1M context, cache hits at $0.26 per 1M
you need JSON structured output from a reasoning model
skip it if
you need image input; GLM-5.3 Flash reads images for far less
you want answers without reasoning; thinking cannot be switched off
faq
which is better, Beam or GLM-5.3?
on Terminal Bench 2.1, as each maker reports it, Beam scores 80.1% and GLM-5.3 88.2. these are each maker's own numbers, not a controlled head-to-head; the spec table lists every benchmark both publish.
which is cheaper, Beam or GLM-5.3?
Beam lists lower input and output prices. on a coding-agent session (2M tokens in, 60,000 out) Beam costs $0.27 and GLM-5.3 $3.06.
can i switch between Beam and GLM-5.3 mid-task?
yes. in an octomind session, /model octohub:beam and /model octohub:glm-5.3 switch models without losing the conversation, so you can draft on one and review on the other.
try either model
switch between models inside the same octomind session.