Vision Model Selector

Rank models with documented image input using context, price, providers, and published benchmark evidence.

Interactive Tool

Build a Scenario

Runs in your browser
Ranking priority
Changes weights, not the eligibility gate.
ResultValueHow to read it
Eligible models122Models that pass the selected evidence and constraint gates.
Ranked shortlist20Top candidates shown below.
Priced candidates94Models with both input and output price minima.
RankModelFit scoreContextInput / 1MWorkload costPublished benchmark rankProviders
1Gemini 3.1 ProGoogle DeepMind85.441.05M tokens$2.00 / 1M$440.0032
2GPT-6.1 SolOpenAI83.331.05M tokens$2.00 / 1M$400.0042
3GPT-5.6 TerraOpenAI82.281.05M tokens$2.00 / 1M$440.0052
4Claude Sonnet 5Anthropic81.671M tokens$2.00 / 1M$400.0063
5GPT-6 AstraOpenAI80.431.05M tokens$10.00 / 1M$2,000.0022
6Gemini 3.8 FlashGoogle DeepMind80.211.05M tokens$0.75 / 1M$150.0072
7Claude Fable 5.1Anthropic79.831M tokens$10.00 / 1M$2,000.0011
8GPT-6 LunaOpenAI79.811.05M tokens$0.10 / 1M$20.0082
9InklingThinking Machines Lab79.521.05M tokens$0.95 / 1M$176.00114
10MiniMax M3MiniMax79.231.05M tokens$0.28 / 1M$50.00125
11Qwen3.8 MaxQwen78.721M tokens$1.65 / 1M$264.02104
12Kimi K3Moonshot AI76.621.05M tokens$0.24 / 1M$262.62145
13Claude Opus 5.5Anthropic74.591M tokens$4.00 / 1M$800.0092
14Qwen3.8 27BQwen72.58262.14K tokens$0.20 / 1M$64.00163
15Grok 4.20 Multi AgentxAI71.851M tokens$1.25 / 1M$175.00131
16Gemini 3.5 Flash LiteGoogle DeepMind71.481.05M tokens$0.30 / 1M$80.00172
17GLM 5.3 FlashZ.ai71.111M tokens$0.08 / 1M$12.50184
18Grok 4.7xAI69.51500K tokens$2.00 / 1M$320.00151
19DeepSeek V4.1 FlashDeepSeek68.651.05M tokens$0.15 / 1M$27.00193
20GPT-5.4 MiniOpenAI67.09400K tokens$0.75 / 1M$165.00202

Fit scores are transparent screening aids, not universal quality claims. Unknown capability, context, or price fields are never filled by inference.

Inputs

What the Calculation Needs

3 input groups
InputHow it is used
Minimum contextText and image-token envelope required by the workload.
Monthly tokensText-token volume used for a comparable cost screen.
PriorityWeight performance, cost, or context capacity.

Methodology

Candidates need documented image input or vision capability. Ranking uses only sourced catalog fields and published benchmark coverage; it does not infer visual quality from a model name.

How to Interpret the Result

Check image size, count, file-type, OCR, and video limits on the exact endpoint. Text-token pricing may not capture image-unit charges.

Boundaries

What the Result Does Not Prove

  1. Image billing units vary and may not map to text tokens.
  2. Vision support does not imply OCR, localization, or video support.
  3. Multimodal benchmark coverage remains uneven.

Catalog values retain their source and freshness on the linked model, provider, benchmark, or comparison page. Editable scenario assumptions are not Model Markets measurements.

Continue the Analysis

Related Tools

All tools →
ToolNext question
Model SelectorTurn workload constraints into a short, inspectable model shortlist instead of a universal best-model claim.
Benchmark ComparisonInspect like-for-like published results without blending incompatible metrics or versions.
Image Generation CostConvert successful-image demand into actual generated attempts and monthly unit cost.

Questions

Vision Model Selector FAQs

What does the Vision Model Selector calculate?+

Create a sourced shortlist for image understanding, document analysis, and multimodal prompts. It returns vision-capable candidates with context, price, provider coverage, and benchmark rank.

Does the Vision Model Selector use current model data?+

Where the calculation needs model facts, it uses the current Model Markets catalog snapshot updated 2026-10-19. User-entered assumptions remain clearly editable, and unsupported values stay unknown rather than being inferred.

What should I verify before using the Vision Model Selector result?+

Image billing units vary and may not map to text tokens. Vision support does not imply OCR, localization, or video support. Multimodal benchmark coverage remains uneven. Open the linked canonical records and primary sources before making a production or purchasing decision.

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