ModelSpec is an analysis of alternatives for AI models: it decides which models fit a job from your requirements, sourced benchmarks and real cost, and shows its work.

DeepSeek V4 Pro vs GLM-5.3 for Private assistant you host yourself, as of 2026-09-23

Find a commercially usable open-weights assistant that you can operate yourself.

Worked example as of 2026-09-23, from the public data image.

What the evidence says

The evidence separates them: GLM-5.3 leads for Private assistant you host yourself, with a 100% chance of being the best model in the full lineup for this job, as of 2026-09-23.

DeepSeek V4 Pro has an under 1% chance of matching or beating GLM-5.3 on this job's objective.

probability bands: a model joins the leader when P(its score >= the leader's) >= 0.25 under the score posteriors; a weighted capability with an 80% interval wider than 2.8 is not enough evidence

Decision status: partial. P(best) and score intervals retain the full ranked lineup for this job, including models beyond this pair and models with thin evidence. Models with unknown objective values remain unranked and are outside this probability. P(best) is not a head-to-head probability. Separation from this challenger need not mean a unique best model across that lineup.

Scores and score intervals use the objective's dimensionless normalised units. Cost per task is in USD for the selected offering and the applied spec's task size. When task size is omitted, the engine uses its documented default. Unavailable means the engine returned no value.

Open weights you run yourself; commercial licence.

Requirements

• model.class = text-generator: A conversational assistant needs a text-generating model.
• model.weights_openness = open_weights: Self-hosting requires downloadable weights; add a model.fits_hardware Must for the device you own.
• licence.commercial_use in {permitted, permitted_with_conditions}: The licence must allow commercial use, including use with stated conditions.

Preferences

• chat_preference: Prefer assistants whose open-ended responses people rate more highly.
• -offering.cost_per_task: Prefer lower task cost when the snapshot has an offering price.

Evidence for DeepSeek V4 Pro

Missing facts for DeepSeek V4 Pro

• feature.batch: not_disclosed
• model.languages: unknown
• model.max_output_tokens: not_disclosed
• origin.base_lineage: not_disclosed
• origin.lab_jurisdiction: not_disclosed
• origin.weights_hosting: not_disclosed
• offering.api_model_id: unknown
• offering.attestation.baa: not_disclosed
• offering.attestation.soc2: not_disclosed
• offering.data.retention: not_disclosed
• offering.data.trains_on_customer_data: not_disclosed
• offering.data.zero_retention: not_disclosed
• offering.price.batch_input: not_disclosed
• offering.price.batch_output: not_disclosed
• offering.private_deployment: unknown
• offering.router_slug: unknown
• offering.speed.throughput: unknown
• offering.speed.time_to_first_token: unknown

Evidence for GLM-5.3

Missing facts for GLM-5.3

• feature.batch: not_disclosed
• model.languages: unknown
• origin.lab_jurisdiction: not_disclosed
• origin.weights_hosting: not_disclosed
• offering.api_model_id: unknown
• offering.attestation.baa: not_disclosed
• offering.attestation.soc2: not_disclosed
• offering.data.retention: not_disclosed
• offering.data.trains_on_customer_data: not_disclosed
• offering.data.zero_retention: not_disclosed
• offering.price.batch_input: not_disclosed
• offering.price.batch_output: not_disclosed
• offering.private_deployment: unknown
• offering.router_slug: unknown
• offering.speed.throughput: unknown
• offering.speed.time_to_first_token: unknown

What this template teaches

Use Must for deployment and licence requirements, then Prefer to balance assistant quality and cost. Lab jurisdiction is a buyer-set Must (origin.lab_jurisdiction in {…}); an unverified jurisdiction does not pass, so set the countries on the facet board. The same condition works as an inventory profile rule.

Decision dec_180a6dc921c00532eceebd84 on public snapshot snap_05a963d2477ac2fb.

Read the full dated job answer.

The live board uses current data. This link applies the template to its full lineup.

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