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Vision Cost - How Multimodal Pricing Actually Works

🚀 Open the calculator

Meet Mei Lin. Product Engineer launching a receipt-OCR feature. "Vision pricing is confusing - per image? Per token? Tiles? What does my actual feature cost?"

🔥 Spec says 50K receipts/day. CFO needs a number by tomorrow.

The story

Vision pricing isn't text pricing with a sticker tax. Different vendors price images differently - OpenAI uses tile counts (each 512×512 tile costs ~85 tokens at low detail, 765 at high), Anthropic charges per image based on dimensions, Google bills per image at flat rates. Comparison isn't apples-to-apples until you normalize.

Mei's receipt-OCR scanning 50K images/day at high detail on GPT-5.5 Vision: ~$2,800/mo. Same workload on Claude Vision: ~$2,200/mo. On Gemini 3 Pro Vision: ~$1,400/mo. Quality differences are real but small for OCR tasks - the spread is mostly about pricing model, not capability.

Three levers cut vision costs 50-70% if you use them. (1) Resolution tier - 'low detail' is 5× cheaper and fine for most non-detailed tasks. (2) Image preprocessing - resize before upload (vendor downsizes anyway, you may as well control the tier). (3) Cheap-tier vision (Gemini Flash, Haiku Vision) for simple classification.

📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Vision Cost - How Multimodal Pricing Actually Works full size

About this calculator: Vision Cost - How Multimodal Pricing Actually Works

Vision pricing is weirder than text. Tile-based, resolution-tier, per-image and per-token mixed. Real math across GPT-5.5 Vision, Claude, Gemini for production.

Inputs you control

Input Impact on result Range Typical
Images processed per day Total daily images. Mei: 50K receipts. Consumer photo apps: millions. 10 – 1M 50000
Avg tokens per image Low detail: ~85-255 tokens. High detail: ~765-3000+. Receipts: ~1500. Photos: ~2500. Documents: ~3000+. 85 – 5K 1500
Output tokens per image What the model returns. OCR: 200-500. Classification: 50. Detailed analysis: 500-1500. 50 – 2K 200

Outputs computed for you · model: multimodal_stack

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

Below: live sliders. Move them to see numbers change in real time. * Output uses the generic compute model — for precise numbers use the full calculator below.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

50,000

Total daily images. Mei: 50K receipts. Consumer photo apps: millions.

Estimated: —
1,500

Low detail: ~85-255 tokens. High detail: ~765-3000+. Receipts: ~1500. Photos: ~2500. Documents: ~3000+.

Estimated: —
200

What the model returns. OCR: 200-500. Classification: 50. Detailed analysis: 500-1500.

Estimated: —

Ready to run the numbers?

Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Reading your result

Per-image cost is the unit. Total cost / images = unit economics. OCR at $0.005-0.02/image is healthy. Above $0.05/image, you're using too-premium a tier.

Watch resolution waste. If you upload 4K images for a 'is this a receipt yes/no?' task, you're paying 5× too much. Resize to thumbnails for classification.

The vendor spread is bigger for vision than text. Up to 50% between vendors at equivalent quality. Worth shopping more aggressively here than for text.

What "good" looks like:
  • Classification (low detail): $0.001-0.005/image
  • OCR (high detail, receipts): $0.005-0.02/image
  • Detailed analysis (high detail, photos): $0.02-0.08/image
  • Document understanding (high detail, multi-page): $0.05-0.30/image

Vision-capable vendors right now

Verified 20 hours ago
  1. 1
    Claude Opus 4.7
    $5.00 in · $25.00 out ·

Three real scenarios

Same calculator, three different team sizes. Click a tab to see how the numbers shift.

$30,000 / month ≈ $360,000 / year

Is-this-a-receipt classifier. Low detail (200 tokens), short output (50 tokens), cheap tier (Haiku Vision / Gemini Flash). 100K/day = $300-450/mo.

Healthy range: <$500/mo at 100K/day

See inputs used
imagesPerDay
100,000
avgImageTokens
200
outputTokens
50
modelTier
cheap
workingDaysPerMonth
30

Trade-offs

Cost isn't the only dimension. Click any constraint — see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Gemini 3 Flash Vision Cheapest per-image
  2. Anthropic Haiku Vision Cheap + good quality
  3. GPT-5 Mini Vision Mid-tier

Vision pricing varies more than text. Gemini Flash is 3-5× cheaper than GPT-5.5 Vision for similar quality on simple tasks. Worth multi-vendor testing on your actual images.

Use cases

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button to load it into the calculator above.

$60,000 / month ≈ $720,000 / year

Content moderation classifier. Low detail sufficient (just need yes/no). Cheap tier. ~$1K/mo at 200K daily.

Healthy range: <$1.5K/mo at 200K/day

See inputs used
imagesPerDay
200,000
avgImageTokens
200
outputTokens
50
modelTier
cheap
workingDaysPerMonth
30

What this calculator can't tell you

Honest limitations — every model is wrong; some are useful. Where this one falls short:

For these, use: Cost Calculator for full bill. Audio Cost for voice + vision combo apps.

Where to go next

Voice + vision multi-modal →

If you need both, model the full stack.

 Full multimodal RAG architecture →

Image embedding + vector search + LLM read.

 Vision at consumer scale →

Per-image costs compound fast.

Methodology

Source
https://platform.claude.com/docs/en/build-with-claude/vision
Extraction
Per-vendor vision pricing extracted daily from official docs.
Editorial gate
8-layer defense — see aicost.ai/ai-cost-economics
Last verified
6/4/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

3 years of pricing history

Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.

View 3-year history for →
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →