Model shortlist

Best vision model APIs for image understanding

Compare vision model APIs for screenshots, documents, product images, and support tickets, with price and capability labels.

What is this shortlist for?: Vision models

Vision APIs are useful for screenshots, receipts, product photos, and ticket attachments. What matters is whether the model accepts the image type you have, whether you need JSON out, and how wrong answers look on your own samples. Price second. This page is a small set of vision-capable candidates so you can A/B a few instead of the whole catalog.

Source basis: NextModel capability mapping and OpenRouter input-modality metadata when available. · Updated 2026-07-01

How to use this shortlist

How to use this shortlist (Vision models)

  1. Match the shortlist to the job. Check whether the Vision models candidates fit your real workload, not only the posted rate.
  2. Run the same prompts. Test two or three candidates on production-like prompts and note quality and output length.
  3. Estimate monthly cost. Use the pricing page or cost calculator with expected token volume.
  4. Set fallback and budget. Pick a primary model, a fallback, and a project budget before production traffic.

Fit score

Recommended candidates vision models

Start with the shortlist, then test real prompts and compare monthly cost before production routing in India.

AnthropicCatalog

Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...

$1.45 / 1M tokensInput$7.23 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...

$0.145 / 1M tokensInput$0.723 / 1M tokensOutput200kContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

$0.723 / 1M tokensInput$3.62 / 1M tokensOutput200kContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...

$0.723 / 1M tokensInput$3.62 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details

Comparison table

Compare the shortlist by price, provider, context, capability, and source.

Use this view when narrowing a production shortlist, building a fallback policy, or comparing model economics for India-based teams.

ModelProviderInputOutputContextCapabilitiesBest forLatencyStatusSource
Anthropic: Claude Fable 5anthropic/claude-fable-5Anthropic$1.45 / 1M tokens$7.23 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Haiku 4.5anthropic/claude-haiku-4.5Anthropic$0.145 / 1M tokens$0.723 / 1M tokens200k
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.5anthropic/claude-opus-4.5Anthropic$0.723 / 1M tokens$3.62 / 1M tokens200k
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.6anthropic/claude-opus-4.6Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Claude Opus 5anthropic/claude-opus-5Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5Anthropic$0.434 / 1M tokens$2.17 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated

FAQ

Vision models FAQ

What should I compare before choosing a vision model API?

Image input support, JSON mode if you need it, latency, output cost, and quality on your real images. Synthetic benchmarks lie more often than people admit.

Can low-cost models handle vision tasks?

Sometimes, for light work. Dense documents and high-accuracy extraction usually need a stronger model and a real eval set.

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