GLM 5.3 Flash the brings open-weight leader’s cost down 80%+ on botsbench

The latest botsbench results on the release of GLM 5.3 family are newsworthy not in intelligence for agentic AI investigations, but cost. Run on cybt-ctf, our 59 task SIEM investigation benchmark, we see that Z.ai’s effective answer to OpenAI’s Luna is GLM 5.3 Flash. Also, the story on quants makes GLM 5.3 Flash more interesting as well.

As a reminder, just a few months ago, Z.ai’s GLM 5.2 release was the first time we measured an open-weight model performed at frontier levels on botsbench.com investigation tasks. Despite that, OpenAI’s 80% price cuts on GPT-5.6 Luna meant we still preferred OpenAI Luna over GLM 5.2 as both smarter and cheaper. With the new GLM 5.3 family, we are not seeing smarter results on botsbench: the important story is that GLM 5.3 Flash pricing means frontier open-weight AI has effectively caught up to GPT-5.6 Luna in quality and cost.

GLM 5.3 Flash means surprisingly little hardware goes a long way. It also gives an extended shelf-life to older GPUs. For the first time, we’re reporting a quant on botsbench: our own GLM 5.3 Flash W4AFP8 that makes it more manageable on older GPUs. Out-of-the-box, GLM 5.3 Flash ships as a 643 GB BF16 release, which typically requires 8 x H200s to run inference. We squeezed to 177GB to run on 2 x H200, with little impact on botsbench scores.

For those who are curious, we quantized the routed MoE experts to int4, paired with static float8 activations, and left attention, shared experts and the dense layers at block-float8. The int4 experts are 88% of the weights, so compressing those while leaving the precision-sensitive paths alone cut the size to 177 GB. The main reason we picked this approach was to avoid requiring the small floating-point units only available in recent GPUs, as many of the teams we work with that self-host have GPUs that are a few years old. On GPQA-Diamond the quant scores 90.9% against 91.2% published for the full-precision model. On botsbench, it scored 35/59, compared to 36/59 unquantized. 

Note that we avoided any cyber-specific fine tuning or abliterations, which bring concerns of overfitting and testing for more unexpected loss of function. Our quant shows general open-weight models are running at quality levels cyber teams would have loved from frontier models last year, and require only a couple older GPUs.

Also of note, the top botsbench investigation scores are still from combining the Louie harness with proprietary models – and those lead with a sizeable margin. However, Louie with GLM 5.3 Flash breaks into the top 5 of the global leaderboard, and lands noticeably ahead of regular coding agent harnesses with other models: GLM 5.3 Flash is both good and cheap.

For more info, you can drill into the GLM 5.3  page on botsbench https://botsbench.com/cybt-ctf-glm53.html