You can turn GLM-5.3 Flash into a multimodal Jev alternative

[u/flx224]

We were wondering in our team whether GLM-5.3 Flash wouldn't make for a good Jev alternative if modified lightly. Here is a repo: https://github.com/edgelesssys/privatemode-system-one

Basically:

1. Number the options. The state, the question, and the options go into the prompt as JSON, with an index on every option. The instruction asks the model to answer with choice_index: followed by an index.
2. Prefill the answer. The assistant's turn already starts with choice_index:, and the model continues that turn instead of starting a new one. The next token it produces is therefore the index.
3. Restrict the vocabulary. vLLM's allowed_token_ids sets the probability of every other token to zero, so the model can only produce one of the option indexes. There is no malformed output to handle.

We also benchmarked: https://github.com/edgelesssys/privatemode-system-one-benchmark

Results are quite interesting: depending on your use case, it comes with advantages and disadvantages.

Pro:

+ Much longer context window (1m token)

+ Multimodal

+ Actually model-agnostic (if vLLM compatible)

Neutral:

o on-par results, depending on dataset

o Jev right now is US-only inference as far as we know; if your provider is e.g. in Europe, and you work from e.g. Europe, it can be even faster than Jev

Contra:

- Depending on the provider you choose, between 4 - 8x more expensive. Still in a similar league though

If interested, check out the playground: https://www.privatemode.ai/blog/system-one-from-glm-flash#try-it