model comparison

vs

Beam vs DeepSeek V4.1 Flash

Reflection against DeepSeek, compared on listed price, real workload cost, context and capabilities.

the short answer

  • on Terminal Bench 2.1, as each maker reports it, Beam scores 80.1% and DeepSeek V4.1 Flash 90.6.
  • Beam lists lower input and output prices.
  • DeepSeek V4.1 Flash supports vision; Beam does not.

side-by-side specs

specBeamDeepSeek V4.1 Flash
providerReflectionDeepSeek
input price$0.125/M$0.3/M
output price$0.4/M$1.2/M
cached input$0.015/M—
blended price (3:1)$0.194/M$0.525/M
context window1M tokens1M tokens
max output128k tokens384k tokens
inputtexttext, images
structured outputyesyes
weightsopen (Apache 2.0)open (MIT)
releasedOctober 2026September 2026
Terminal Bench 2.1 (maker-reported)80.1%90.6
DeepSWE v1.1 (maker-reported)43.3%74.2
GPQA Diamond (maker-reported)90.6%90.9
HLE (maker-reported)36.0%36.8

same work, both models

workloadBeamDeepSeek V4.1 Flash
one chat reply3,000 tokens in, 500 outDeepSeek V4.1 Flash costs 2.6× as much as Beam$0.00058$0.0015
summarise a 100-page report50,000 tokens in, 1,500 out, one requestDeepSeek V4.1 Flash costs 2.5× as much as Beam$0.0069$0.02
1,000 classification calls500 tokens in, 20 out, per call$0.07$0.17
coding-agent session2M tokens in across ~60 turns, 60,000 out, no cache$0.27$0.67

at listed rates. token counts are typical sizes, not measurements.

DeepSeek V4.1 Flash pricing notes

  • the listed price is DeepSeek's peak rate. DeepSeek's own api charges half outside 01:00-04:00 and 06:00-10:00 UTC on weekdays; other serving providers bill the peak rate.
  • cache hits bill $0.006 per 1M on some serving providers and $0.03 per 1M on others.

when to pick which

Beam

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

DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is DeepSeek's open-weight mixture-of-experts model that reads text and images with a 1M-token context. it replaced V4 Flash behind DeepSeek's own deepseek-flash route and costs less than V4 Flash here.

pick it if

  • you want the cheapest DeepSeek model here, and the only one with image input
  • you need long replies: DeepSeek's api allows up to 384K output tokens
  • you want MIT-licensed weights you can self-host

skip it if

  • you need video or audio input; it takes text and images only

faq

which is better, Beam or DeepSeek V4.1 Flash?

on Terminal Bench 2.1, as each maker reports it, Beam scores 80.1% and DeepSeek V4.1 Flash 90.6. 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 DeepSeek V4.1 Flash?

Beam lists lower input and output prices. on a coding-agent session (2M tokens in, 60,000 out) Beam costs $0.27 and DeepSeek V4.1 Flash $0.67.

which one accepts images?

DeepSeek V4.1 Flash supports vision; Beam does not.

can i switch between Beam and DeepSeek V4.1 Flash mid-task?

yes. in an octomind session, /model octohub:beam and /model octohub:deepseek-v4.1-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.

prices from the catalog of 2026-11-09