Best models

Best AI models for images and vision

Every model in the live reference catalogue with image input, 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 images and vision

A request with a picture in it - a screenshot, a scanned invoice, a chart - needs a model that accepts image input, and that alone removes most of the catalogue. Among the rest the intelligence index is still the best public proxy for how well the model reads what it sees; vendor demos are not.

Images are priced as tokens too, and the conversion differs by vendor, so the list price per million tokens undercounts a photo-heavy workload. Compare models on your own images in the playground before committing; the ranking here gets you to the shortlist.

The router sees an image as a signal like any other: requests carrying one are only matched to models that accept it, OCR-style reading can be pinned to a self-hosted vision model (the rule below), and the text-only follow-up questions go back to cheaper text models. In practice that splits a support conversation about a screenshot into one request that needs vision and five that do not, and only the first pays the vision model's price - the trace shows the modality check that excluded the text-only models from the first and admitted them for the rest.

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 - vision: the strongest qualifying model for the hard requests, the best value for the rest
rules:
  - name: vision-hard
    when: { all_actions: [image], actions: [ocr, extract] }
    prefer: [claude-opus-5.5]
    weight: 0.8
  - name: vision-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 is best at reading images?: The top of the ranking above among models that accept image input, by the intelligence index. For reading printed text specifically, a self-hosted vision model is often the cheaper pin.
How are images billed?: As input tokens; each vendor converts an image to a token count differently, so the per-million list price understates image-heavy traffic. The model pages carry the input price to compare.
Can text-only follow-ups use a cheaper model?: Yes. The router decides per request, so the question after the image goes to a text model unless the image is still needed.

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 images and vision 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 images and vision

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 - vision: the strongest qualifying model for the hard requests, the best value for the rest
rules:
  - name: vision-hard
    when: { all_actions: [image], actions: [ocr, extract] }
    prefer: [claude-opus-5.5]
    weight: 0.8
  - name: vision-default
    when: { max_complexity: 0.45 }
    prefer: [muse-spark-1.3]
    weight: 0.6

Frequently asked

Which AI model is best at reading images?

The top of the ranking above among models that accept image input, by the intelligence index. For reading printed text specifically, a self-hosted vision model is often the cheaper pin.

How are images billed?

As input tokens; each vendor converts an image to a token count differently, so the per-million list price understates image-heavy traffic. The model pages carry the input price to compare.

Can text-only follow-ups use a cheaper model?

Yes. The router decides per request, so the question after the image goes to a text model unless the image is still needed.

Best AI models for images and vision

Ranked by the intelligence index

What to weigh for images and vision

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 images and vision model per request.

Best AI models for images and vision

Ranked by the intelligence index

What to weigh for images and vision

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 images and vision model per request.