Both are open-weight models aimed at agentic coding, and they resolve the same trade-off in opposite directions. DeepSeek V4.1 Flash scores higher. Beam costs less per attempt. Which one is correct for you depends entirely on whether you pay for tokens.
Head to head
| | Reflection Beam | DeepSeek V4.1 Flash |
|---|
| Terminal-Bench v2.1 | 80.1 | 90.6 |
| Architecture | Sparse MoE, 501B total / 23B active | Sparse MoE |
| Context | 1M effective (256K in RL) | Long-context |
| Licence | Apache 2.0 | Open weights |
| Tokens per attempt | ~25,000 (vendor estimate) | Not published |
What the efficiency claim actually says
Reflection reports Beam reaching GLM-5.2-comparable reasoning at three to four times less inference compute, roughly 25,000 tokens per Terminal-Bench attempt against GLM-5.2's 80,000. Read the method before you lean on it: the figure is computed as 2 x active parameters x mean generated tokens, and excludes prompt prefill, attention and serving overhead. It is a vendor estimate of a vendor-defined quantity. Directionally useful, not a benchmark.
Where each one wins
Choose DeepSeek V4.1 Flash when the task has to land. Ten points on Terminal-Bench is not a rounding error — on agent work it is the difference between a run that completes and one you have to babysit. If you are running a handful of hard tasks a day, pay the tokens.
Choose Beam when volume is the problem. Agent loops that retry, fan out, or run on every commit multiply token cost by attempt count, and at that scale a model reaching a slightly worse answer for a third of the spend is the better instrument. Apache 2.0 also means no licence review, which matters more in enterprises than most benchmark tables admit.
Neither if you have not measured your own token spend per task. That number decides this comparison, and almost nobody has it to hand.
FAQ
Is Beam the best open-weight coding model right now?
No. On Terminal-Bench v2.1 it sits behind GLM-5.2 (81.0), Qwen 3.8 Max (86.6), GLM-5.3 (88.2), Kimi K3 (88.3) and DeepSeek V4.1 Flash (90.6). It is competitive on efficiency, not on score.
Can I run Beam commercially?
Yes. Apache 2.0 permits commercial use, modification and redistribution with attribution, which is a more permissive position than several rivals take.
What hardware do I need for 23B active parameters?
Active parameter count drives inference cost, but you still need to hold 501B total parameters in memory across your serving setup. This is a cluster model, not a workstation model.
Related: Reflection opens Beam under Apache 2.0