Best LLM for Cheapest Model with Vision in 2026
Lowest-cost path to vision capability. Below is the current ranked list, based on benchmark scores and capability weights specific to this use case. Each entry includes the model's score, list price, and a one-line "why it ranks here" note.
Ranked list
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GPT-5 Nano
— OpenAI
Score 78.0
$0.05/$0.40 per M
400K ctx
$0.05/M input. Vision is text-only at this tier, but for OCR-style tasks the nano is unbeatable.
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GPT-4o Mini
— OpenAI
Score 82.0
$0.15/$0.60 per M
128K ctx
$0.15/M input + full vision capability. The standard cheap-vision pick.
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Gemini 2.5 Flash
— Google
Score 80.0
$0.30/$2.50 per M
1.0M ctx
$0.30/M input but supports video too.
Selection criteria
Rankings weight the following factors for this use case:
- price: 70%
- multimodal: 30%
Weights reflect what matters for this workload — for example, "code generation" weights coding benchmarks heavily and price moderately, while "customer support" weights price and latency more than peak quality. Reasonable people will weight differently; the cost calculator and comparison tool let you reproduce the math with your own assumptions.
What this use case actually involves
Lowest-cost path to vision capability. Real-world implementations of this workload typically involve a mix of model calls, retrieval, and post-processing. The ranking above is for the model-call portion in isolation; total cost and latency depend on the surrounding architecture.
How the ranking is built
Composite scores are derived from the listed benchmark scores weighted by the factors above, plus capability fit (does the model support tool use, vision, function calling, etc.). The result is not a single "best model" answer — it's an ordered list with a clear rationale for each rank, so you can override based on requirements the ranking can't model (procurement constraints, regional availability, data residency).