Best models

Best AI models for long documents

Every model in the live reference catalogue with a context window of at least 200k tokens, ranked by the intelligence index (Artificial Analysis, 0-100) with vendor list prices per million tokens. 12 qualify today.

Strongest

Claude Opus 5.5

intelligence index 58 · $8.00 blended

Best value

Muse Spark 1.3

48 points for $2.00

Cheapest

Muse Spark 1.3

$2.00 blended · index 48

Ranked by the intelligence index

Strongest first; price breaks ties. Value is index points per blended dollar. Click a model for its full specification, or compare two side by side.

#	Model	Intelligence index	Input / 1M	Output / 1M	Context	Value	Compare
1	Claude Opus 5.5Anthropic	58	$4.00	$20.0	1M	7
2	Claude Sonnet 5.5Anthropic	56	$2.00	$10.0	1M	14	vs Claude Opus 5.5
3	Claude Fable 5.1Anthropic	53	$10.0	$50.0	1M	3	vs Claude Opus 5.5
4	GPT-6 AstraOpenAI	53	$10.0	$50.0	1.1M	3	vs Claude Opus 5.5
5	Gemini 4 ArgonGoogle	53	$2.00	$10.0	1.0M	13	vs Claude Opus 5.5
6	GPT-6.1 SolOpenAI	52	$2.00	$10.0	1.1M	13	vs Claude Opus 5.5
7	Claude Opus 5Anthropic	51	$5.00	$25.0	1M	5	vs Claude Opus 5.5
8	Claude Fable 5Anthropic	50	$10.0	$50.0	1M	2	vs Claude Opus 5.5
9	Muse Spark 1.3Meta	48	$1.25	$4.25	1.0M	24	vs Claude Opus 5.5
10	GPT-6 SolOpenAI	48	$2.00	$10.0	1.1M	12	vs Claude Opus 5.5
11	GPT-5.6 SolOpenAI	47	$4.00	$20.0	1.1M	6	vs Claude Opus 5.5
12	Grok 4.7xAI	46	$2.00	$6.00	500K	15	vs Claude Opus 5.5

What to weigh for long documents

A long document is a question of two numbers: whether the model can read it at all - the context window - and what reading it costs - the input price times the length. Only models accepting 200,000 tokens or more qualify here, which covers a long contract, a day of meeting transcripts or a mid-sized codebase in one request.

Input price dominates this task, the opposite of coding. A 150k-token contract summarised ten times a day is 1.5M input tokens against a few thousand output tokens, so a model with a low input rate and cached-input pricing wins even when its output rate is high. The cached-input column on each model page is the one to compare.

Two routing habits pay here. First, the request's size is one of the signals the router extracts, and a target whose context window does not fit it is not admissible, so a short question never pays for a long-context model. Second, retrieval narrowing can cut the input to the relevant passages before a model sees it - cheaper than any model choice.

The rule, in OpenSmartRoute

Declare the models as targets and let the rule send the hard requests to the strongest one and the rest to the best value; the trace names the rule that fired.

yaml

# rules.yaml - long-documents: the strongest qualifying model for the hard requests, the best value for the rest
rules:
  - name: long-documents-hard
    when: { actions: [summarize, extract], min_complexity: 0.5 }
    prefer: [claude-opus-5.5]
    weight: 0.8
  - name: long-documents-default
    when: { max_complexity: 0.45 }
    prefer: [muse-spark-1.3]
    weight: 0.6

Try a request Price a workload

Frequently asked

Which AI model has the largest context window?: The context column in the ranking above shows it for the qualifying models; the catalogue is live, so the largest window today may not be the largest next quarter.
Is a bigger context window always better?: No. Models lose precision deep into a long input, and you pay for every token read. Fit the window to the document and narrow the input when you can.
How does the router handle a request that does not fit a model?: The request's size is part of the signals the router extracts; a target whose window is too small is not admissible for it, so nothing is truncated silently.

Best models for other tasks

For the business

What it means for which models you pay for.

Catalogue of every vendor
The right model, not the famous one
Price, context, abilities and benchmark indexes side by side, with the model that fits each task named.
The rankings
89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
Per million tokens, every model
Prices you can compare
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
The model catalogue

Which LLM is the cheapest for our workload?: The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.
How much can we save on our LLM bill?: On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.
How often do model prices change?: The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

Let the router pick the long documents model per request.

The ranking above is where to start; the router finishes the job - the strongest qualifying model for the hard requests, the best value for the rest, and every decision explained.

Create a free account Price a workload

Free plan, no card. Fifteen thousand decisions a month with the full trace.

Model

Intelligence index

Input / 1M

Output / 1M

Context

Value

Compare

Claude Opus 5.5Anthropic

$4.00

$20.0

Claude Sonnet 5.5Anthropic

$2.00

$10.0

vs Claude Opus 5.5

Claude Fable 5.1Anthropic

$10.0

$50.0

vs Claude Opus 5.5

GPT-6 AstraOpenAI

$10.0

$50.0

1.1M

vs Claude Opus 5.5

Gemini 4 ArgonGoogle

$2.00

$10.0

1.0M

vs Claude Opus 5.5

GPT-6.1 SolOpenAI

$2.00

$10.0

1.1M

vs Claude Opus 5.5

Claude Opus 5Anthropic

$5.00

$25.0

vs Claude Opus 5.5

Claude Fable 5Anthropic

$10.0

$50.0

vs Claude Opus 5.5

Muse Spark 1.3Meta

$1.25

$4.25

1.0M

vs Claude Opus 5.5

GPT-6 SolOpenAI

$2.00

$10.0

1.1M

vs Claude Opus 5.5

GPT-5.6 SolOpenAI

$4.00

$20.0

1.1M

vs Claude Opus 5.5

Grok 4.7xAI

$2.00

$6.00

500K

vs Claude Opus 5.5

What to weigh for long documents

The rule, in OpenSmartRoute

Declare the models as targets and let the rule send the hard requests to the strongest one and the rest to the best value; the trace names the rule that fired.

yaml

# rules.yaml - long-documents: the strongest qualifying model for the hard requests, the best value for the rest
rules:
  - name: long-documents-hard
    when: { actions: [summarize, extract], min_complexity: 0.5 }
    prefer: [claude-opus-5.5]
    weight: 0.8
  - name: long-documents-default
    when: { max_complexity: 0.45 }
    prefer: [muse-spark-1.3]
    weight: 0.6

Frequently asked

Which AI model has the largest context window?

The context column in the ranking above shows it for the qualifying models; the catalogue is live, so the largest window today may not be the largest next quarter.

Is a bigger context window always better?

No. Models lose precision deep into a long input, and you pay for every token read. Fit the window to the document and narrow the input when you can.

How does the router handle a request that does not fit a model?

The request's size is part of the signals the router extracts; a target whose window is too small is not admissible for it, so nothing is truncated silently.

Best AI models for long documents

Ranked by the intelligence index

What to weigh for long documents

The rule, in OpenSmartRoute

Frequently asked

Related

Best models for other tasks

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Let the router pick the long documents model per request.

Best AI models for long documents

Ranked by the intelligence index

What to weigh for long documents

The rule, in OpenSmartRoute

Frequently asked

Related

Best models for other tasks

What it means for which models you pay for.

The right model, not the famous one

Lower cost per request

Prices you can compare

Let the router pick the long documents model per request.