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.

Long documents on a budget

Find the cheapest text model that still takes a long document, with writing quality kept in the ranking.

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

Room for a long document, cheapest task first.

Requirements

model.class = text-generator: The result must be generated as text.
model.context_window >= 400000: The model must accept a long document in one request.

Preferences

-offering.cost_per_task: Prefer the cheaper task; long inputs make price differences large.
writing: Keep writing quality so the cheapest poor summary does not win on cost alone.

Applied template spec

spec_version

where

model.class = text-generator
model.context_window >= 400000

optimize

weights

-offering.cost_per_task: 0.6
writing: 0.4

Worked answer

Decision status: partial.

No recommendation: the public image has no answer with enough evidence.

This is a partial answer. Unknown facts or missing objective evidence can leave eligible candidates unranked.

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

Score and score interval use the objective's dimensionless normalised units. Cost per task is for the engine-selected offering and the applied spec's task size. When task size is omitted, the engine uses its documented default. P(best) is the engine probability. Unavailable means the engine did not return a value. Thin evidence is undetermined and is not a recommendation.

Thin evidence: undetermined

Model	Score	Score interval	P(best)	Cost per task, USD	Capability estimates and intervals
`google/gemini-3-7-flash`	0.881779	[0.37961, 1.383948]	0.1484375	0.045	writing: 0.434597365; interval [-1.064541992, 1.933736721]; benchmarks 3; direct benchmarks 0
`google/gemini-3-8-flash`	0.8515	[0.349331, 1.353669]	0.09765625	0.045	writing: 0.344204382; interval [-1.154934975, 1.843343738]; benchmarks 3; direct benchmarks 0
`google/gemini-3-1-pro-preview`	0.728664	[0.226495, 1.230833]	0.09375	0.128	writing: 0.245370198; interval [-1.253769159, 1.744509554]; benchmarks 3; direct benchmarks 0
`zhipu/glm-5-3`	0.708545	[0.206376, 1.210714]	0.046875	0.0736	writing: 0.009738247; interval [-1.489401109, 1.508877604]; benchmarks 3; direct benchmarks 0
`meta/muse-spark-1-3`	0.706279	[0.036064, 1.376494]	0.14453125	0.067	writing: -0.018325411; interval [-2.019136338, 1.982485515]; benchmarks 1; direct benchmarks 0
`anthropic/claude-opus-4-6`	0.691879	[0.18971, 1.194048]	0.0703125	0.3	writing: 0.690665563; interval [-0.808473794, 2.189804919]; benchmarks 3; direct benchmarks 0
`google/gemini-3-5-flash`	0.68636	[0.184191, 1.18853]	0.08203125	0.096	writing: 0.015804984; interval [-1.483334372, 1.514944341]; benchmarks 3; direct benchmarks 0
`meta/muse-spark-1-1`	0.653444	[-0.01677, 1.323659]	0.09375	0.067	writing: -0.176054846; interval [-2.176865772, 1.824756081]; benchmarks 1; direct benchmarks 0
`openai/gpt-5-6-sol`	0.620134	[0.117965, 1.122303]	0.04296875	0.24	writing: 0.282841067; interval [-1.216298289, 1.781980424]; benchmarks 3; direct benchmarks 0
`zhipu/glm-5-2`	0.616417	[0.114248, 1.118586]	0.0390625	0.0736	writing: -0.265292672; interval [-1.764432028, 1.233846685]; benchmarks 3; direct benchmarks 0
`anthropic/claude-opus-4-7`	0.616056	[0.113887, 1.118225]	0.0390625	0.3	writing: 0.464308039; interval [-1.034831318, 1.963447396]; benchmarks 3; direct benchmarks 0
`deepseek/deepseek-v4-pro`	0.574443	[0.072274, 1.076612]	0.0390625	0.06864	writing: -0.406605901; interval [-1.905745257, 1.092533456]; benchmarks 3; direct benchmarks 0
`openai/gpt-5-4`	0.530055	[0.027886, 1.032224]	0.01171875	0.16	writing: -0.244266234; interval [-1.743405591, 1.254873122]; benchmarks 3; direct benchmarks 0
`anthropic/claude-opus-5`	0.501435	[-0.000734, 1.003604]	0.0234375	0.3	writing: 0.122128685; interval [-1.377010672, 1.621268041]; benchmarks 3; direct benchmarks 0
`anthropic/claude-fable-5`	0.4	[-0.102169, 0.902169]	0.015625	0.6	writing: 0.787525395; interval [-0.711613961, 2.286664752]; benchmarks 3; direct benchmarks 0
`anthropic/claude-fable-5-1`	0.296568	[-0.205601, 0.798737]	0.01171875	0.6	writing: 0.478747567; interval [-1.02039179, 1.977886923]; benchmarks 3; direct benchmarks 0
`openai/gpt-6-astra`	0.19979	[-0.302379, 0.701959]	0.0	0.6	writing: 0.189832533; interval [-1.309306823, 1.68897189]; benchmarks 3; direct benchmarks 0

Unknown requirements

Some candidates may qualify. Missing facts:

model.context_window
offering.cost_per_task
writing

Engine warnings

not_separable
proxy_evidence_only

Omitted result rows: models 5; offerings 10. The bands above include all ranked models.

Decision dec_9f274d21f6ba9ba718622e94 on public snapshot snap_05a963d2477ac2fb.

What this template teaches

A lower context floor widens the field; cost leads the ranking and writing keeps it honest.

The live board uses current data. Its answer can change as evidence changes.

Apply this template on Decide

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Use-case examples · How we decide