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.

Gemini 3.8 Flash vs Muse Spark 1.3 for High volume (good enough), as of 2026-09-23

Find a low-cost text model for many short assistant requests without ignoring response quality.

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

What the evidence says

The evidence separates them: Gemini 3.8 Flash leads for High volume (good enough), with a 45% chance of being the best model in the full lineup for this job, as of 2026-09-23.

Muse Spark 1.3 has an under 1% chance of matching or beating Gemini 3.8 Flash 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.

Short requests at scale; cheapest first, quality kept.

Requirements

• model.class = text-generator: The workload needs generated text responses.

Preferences

• -offering.cost_per_task: At high volume, small differences in task cost dominate total spend.
• chat_preference: Keep a quality signal so cheap but poor responses do not win by cost alone.

Evidence for Gemini 3.8 Flash

Missing facts for Gemini 3.8 Flash

• model.hardware_fit_indeterminate: unknown
• model.languages: unknown
• model.parameters_total: unknown
• origin.base_lineage: not_disclosed
• origin.weights_hosting: not_disclosed
• offering.api_model_id: unknown
• offering.attestation.baa: not_disclosed
• offering.attestation.soc2: not_disclosed
• offering.private_deployment: unknown
• offering.router_slug: unknown
• offering.speed.throughput: unknown
• offering.speed.time_to_first_token: unknown

Evidence for Muse Spark 1.3

Missing facts for Muse Spark 1.3

• feature.batch: not_disclosed
• licence.commercial_use: not_disclosed
• licence.fine_tuning: not_disclosed
• licence.output_training: not_disclosed
• licence.user_cap: not_disclosed
• model.hardware_fit_indeterminate: unknown
• model.languages: unknown
• model.max_output_tokens: not_disclosed
• model.parameters_total: unknown
• origin.base_lineage: 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.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

Keep the model class as a Must, then make cost the stronger Prefer without turning quality into a gate.

Decision dec_d2c32dfb4977b778053cd577 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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