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, best available

Find a text model that accepts million-token documents and writes the strongest analysis of them.

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

Million-token context; strongest analysis, cost no object.

Requirements

model.class = text-generator: The result must be generated as text.
model.context_window >= 1000000: The model must accept a million-token document in one request.

Preferences

writing: Prefer clear, high-quality long-form writing.
reasoning: Prefer stronger multi-step analysis of the document.

Applied template spec

spec_version

where

model.class = text-generator
model.context_window >= 1000000

optimize

weights

writing: 0.6
reasoning: 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
`anthropic/claude-fable-5-1`	0.844852	[-0.46377, 2.153474]	0.12890625	0.6	reasoning: 0.710115622; interval [-0.98201435, 2.402245595]; benchmarks 2; direct benchmarks 0 writing: 0.478747567; interval [-1.02039179, 1.977886923]; benchmarks 3; direct benchmarks 0
`anthropic/claude-opus-4-6`	0.783043	[-0.464983, 2.031068]	0.09765625	0.3	reasoning: 0.197361213; interval [-1.310140483, 1.704862909]; benchmarks 3; direct benchmarks 0 writing: 0.690665563; interval [-0.808473794, 2.189804919]; benchmarks 3; direct benchmarks 0
`anthropic/claude-fable-5`	0.779899	[-0.535743, 2.095541]	0.1171875	0.6	reasoning: 0.039498036; interval [-1.674020333, 1.753016406]; benchmarks 2; direct benchmarks 0 writing: 0.787525395; interval [-0.711613961, 2.286664752]; benchmarks 3; direct benchmarks 0
`google/gemini-3-7-flash`	0.741648	[-0.57285, 2.056146]	0.08984375	0.045	reasoning: 0.463255629; interval [-1.246777358, 2.173288616]; benchmarks 2; direct benchmarks 0 writing: 0.434597365; interval [-1.064541992, 1.933736721]; benchmarks 3; direct benchmarks 0
`google/gemini-3-8-flash`	0.731577	[-0.513785, 1.976939]	0.078125	0.045	reasoning: 0.570955383; interval [-0.928430754, 2.070341519]; benchmarks 3; direct benchmarks 0 writing: 0.344204382; interval [-1.154934975, 1.843343738]; benchmarks 3; direct benchmarks 0
`anthropic/claude-opus-5`	0.581563	[-0.735916, 1.899041]	0.0625	0.3	reasoning: 0.45386262; interval [-1.265252052, 2.172977292]; benchmarks 2; direct benchmarks 0 writing: 0.122128685; interval [-1.377010672, 1.621268041]; benchmarks 3; direct benchmarks 0
`openai/gpt-6-astra`	0.572754	[-0.672909, 1.818417]	0.05859375	0.6	reasoning: 0.323374241; interval [-1.176927713, 1.823676194]; benchmarks 3; direct benchmarks 0 writing: 0.189832533; interval [-1.309306823, 1.68897189]; benchmarks 3; direct benchmarks 0
`google/gemini-3-1-pro-preview`	0.567522	[-0.680504, 1.815548]	0.046875	0.128	reasoning: 0.22241102; interval [-1.285090676, 1.729912715]; benchmarks 3; direct benchmarks 0 writing: 0.245370198; interval [-1.253769159, 1.744509554]; benchmarks 3; direct benchmarks 0
`anthropic/claude-opus-4-7`	0.565969	[-0.682057, 1.813994]	0.0390625	0.3	reasoning: -0.117498278; interval [-1.624999974, 1.390003417]; benchmarks 3; direct benchmarks 0 writing: 0.464308039; interval [-1.034831318, 1.963447396]; benchmarks 3; direct benchmarks 0
`openai/gpt-5-6-sol`	0.562079	[-0.757485, 1.881643]	0.07421875	0.24	reasoning: 0.148460441; interval [-1.577008032, 1.873928913]; benchmarks 2; direct benchmarks 0 writing: 0.282841067; interval [-1.216298289, 1.781980424]; benchmarks 3; direct benchmarks 0
`meta/muse-spark-1-3`	0.43148	[-1.230522, 2.093482]	0.08203125	0.067	reasoning: 0.211605235; interval [-1.789205691, 2.212416162]; benchmarks 1; direct benchmarks 0 writing: -0.018325411; interval [-2.019136338, 1.982485515]; benchmarks 1; direct benchmarks 0
`google/gemini-3-5-flash`	0.425416	[-0.900578, 1.75141]	0.046875	0.096	reasoning: 0.140878753; interval [-1.604182337, 1.885939842]; benchmarks 2; direct benchmarks 0 writing: 0.015804984; interval [-1.483334372, 1.514944341]; benchmarks 3; direct benchmarks 0
`moonshot/kimi-k3`	0.346732	[-0.971863, 1.665327]	0.01171875	Unavailable	reasoning: 0.00591179; interval [-1.716605348, 1.728428927]; benchmarks 2; direct benchmarks 0 writing: -0.052632519; interval [-1.551771876, 1.446506837]; benchmarks 3; direct benchmarks 0
`zhipu/glm-5-3`	0.341605	[-0.971449, 1.654658]	0.015625	0.0736	reasoning: -0.105195063; interval [-1.810826758, 1.600436632]; benchmarks 2; direct benchmarks 0 writing: 0.009738247; interval [-1.489401109, 1.508877604]; benchmarks 3; direct benchmarks 0
`openai/gpt-5-4`	0.324027	[-0.937394, 1.585447]	0.0	0.16	reasoning: 0.230107427; interval [-1.318205455, 1.77842031]; benchmarks 3; direct benchmarks 0 writing: -0.244266234; interval [-1.743405591, 1.254873122]; benchmarks 3; direct benchmarks 0
`meta/muse-spark-1-1`	0.169981	[-1.49202, 1.831983]	0.0390625	0.067	reasoning: -0.343673105; interval [-2.344484032, 1.657137821]; benchmarks 1; direct benchmarks 0 writing: -0.176054846; interval [-2.176865772, 1.824756081]; benchmarks 1; direct benchmarks 0
`zhipu/glm-5-2`	0.147607	[-1.174006, 1.46922]	0.0078125	0.0736	reasoning: -0.275228659; interval [-2.006940496, 1.456483179]; benchmarks 2; direct benchmarks 0 writing: -0.265292672; interval [-1.764432028, 1.233846685]; benchmarks 3; direct benchmarks 0
`deepseek/deepseek-v4-pro`	0.0	[-1.322691, 1.322691]	0.00390625	0.06864	reasoning: -0.508628162; interval [-2.24362481, 1.226368486]; benchmarks 2; direct benchmarks 0 writing: -0.406605901; interval [-1.905745257, 1.092533456]; benchmarks 3; direct benchmarks 0

Unknown requirements

Some candidates may qualify. Missing facts:

model.context_window
reasoning
writing

Engine warnings

not_separable
proxy_evidence_only

Omitted result rows: models 8; offerings 10. The bands above include all ranked models.

Decision dec_a25fc5e5b80140a456dd7910 on public snapshot snap_05a963d2477ac2fb.

What this template teaches

Context capacity is a Must; with no cost term, only writing and reasoning rank.

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

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Use-case examples · How we decide