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
Alibaba's omnimodal Qwen3.8 Flash-tier model: it reads text, images, audio and video and answers in text across a 1M-token window. it replaced Qwen3.8 Flash, retired 2026-10-06, at the same price.
pick it if
you need audio or video input at the Flash-tier price
you reuse long prompts: cache hits bill about a tenth of input
you want a 1M-token window well below Qwen3.8 Max's price
skip it if
you need audio or image output; it returns text only
faq
which is better, Beam or Qwen3.8 Omni Flash?
on GPQA Diamond, as each maker reports it, Beam scores 90.6% and Qwen3.8 Omni Flash 91.0. 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 Qwen3.8 Omni Flash?
Beam lists lower input and output prices. on a coding-agent session (2M tokens in, 60,000 out) Beam costs $0.27 and Qwen3.8 Omni Flash $0.33.
which one accepts images?
Qwen3.8 Omni Flash supports vision; Beam does not.
can i switch between Beam and Qwen3.8 Omni Flash mid-task?
yes. in an octomind session, /model octohub:beam and /model octohub:qwen3.8-omni-flash 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.