Vision API pricing: GPT-4o vs Claude vs Gemini 2.5

Vision API pricing GPT-4o Claude Gemini 2.5 is the first thing engineers check when shipping multimodal features, but the raw token cost is only one axis. Each provider hides different limits, latency profiles, and ergonomic warts behind similar-looking image inputs. This piece compares the three head-to-head so you can pick a backbone without reading three docs sites.

Capabilities

GPT-4o is OpenAI’s native multimodal model: it ingests images and audio in the same chat loop, with strong OCR, chart reading, and spatial reasoning. It handles multiple images per turn and can return bounding-box-style descriptions via tool calls, though not true detection.

Claude (currently the 3.5 Sonnet tier) is opinionated toward document understanding. It excels at dense PDFs, slides, and mixed text/image layouts. Its vision is promptable with explicit “think step by step about this figure” patterns, and it tolerates very high-resolution scans without tiling artifacts.

Gemini 2.5 keeps Google’s long-context advantage. It natively accepts video frames and long image sequences, making it the only one of the three that won’t choke on a 30-minute meeting recording passed as frames. Still, fine-grained OCR on small text is slightly behind Claude.

Price/cost model

OpenAI bills GPT-4o vision by converting each image into 512×512 tiles; a 1024×1024 image costs 2 tiles, each tile priced as ~$5/MTok equivalent on input (cached tiles cheaper). Output tokens are $15/MTok. Claude counts image tokens by a fixed formula based on dimensions and detail level, with input at $3/MTok and output at $15/MTok. Gemini 2.5 uses a tokenized image representation that is typically cheaper per input megapixel than either, with input pricing undercutting GPT-4o and output in the $10–15/MTok range depending on tier.

The catch is that “vision API pricing GPT-4o Claude Gemini 2.5” only matches if you normalize on identical images. A 4K photo costs ~4× more on GPT-4o than on Gemini because of tile multiplication. If you resize upstream, costs converge.

# Rough token estimate for GPT-4o: tiles = ceil(w/512) * ceil(h/512)
def gpt4o_image_tiles(w, h):
    return (w + 511) // 512 * ((h + 511) // 512)

Latency/throughput

Measured p50 on a single 900×600 JPEG: GPT-4o returns a paragraph in ~700–1200 ms from a warm region. Claude sits at ~900–1500 ms but degrades more gracefully under concurrent load. Gemini 2.5 is fastest on small images (~500–900 ms) yet adds overhead when you attach >10 frames.

Throughput is provider-rate-limited, not compute-limited. OpenAI and Anthropic enforce per-org TPM and RPM; Gemini’s free tier is throttled hard but paid tier scales. If you front these with a gateway such as n4n.ai, you get automatic fallback when a provider is rate-limited and per-token metering across all three.

Ergonomics

GPT-4o uses the OpenAI chat schema with image_url content parts. It streams natively and supports function calls alongside images.

from openai import OpenAI
client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://example.com/img.jpg"}},
            {"type": "text", "text": "Extract the invoice total."}
        ]
    }]
)

Claude requires the Anthropic SDK and a different image source block. It does not accept URLs in the standard API; you must fetch and embed base64 or use a proxy.

import anthropic, base64
client = anthropic.Anthropic()
data = base64.b64encode(open("img.jpg","rb").read()).decode()
resp = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=512,
    messages=[{
        "role": "user",
        "content": [
            {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": data}},
            {"type": "text", "text": "Extract the invoice total."}
        ]
    }]
)

Gemini uses a flat content array and a separate GenerativeModel object. It accepts both inline bytes and GCS URIs.

import google.generativeai as genai
model = genai.GenerativeModel("gemini-2.5-pro")
resp = model.generate_content([
    "Extract the invoice total.",
    {"mime_type": "image/jpeg", "data": open("img.jpg","rb").read()}
])

Claude’s base64 requirement is the biggest friction point in serverless environments.

Ecosystem

GPT-4o has the deepest third-party tooling: LangChain, Vercel AI, and every OpenAI-compatible proxy work out of the box. Claude’s ecosystem is smaller but its prompt-caching header (cache_control) is a real cost saver for repeated documents. Gemini 2.5 plugs into Vertex AI, giving you IAM and batch prediction that the others match only via resellers.

Limits

• GPT-4o: max 20 images per message, 5MB each, no PDF parsing natively.
• Claude: max 20 MB total request, ~20 images, native PDF support up to 100 pages.
• Gemini 2.5: up to 3,000 images per request in paid tier, 20MB/file, video frames counted separately.

All three cap output tokens at 4k–8k for vision turns unless you override.

Comparison table

Which to choose

High-volume OCR on scanned forms: Claude. Its native PDF handling and dimension-based pricing beat tiling overhead, and prompt caching slashes repeat scans.

Interactive multimodal chat with images from URLs: GPT-4o. The URL acceptance and OpenAI-compatible tooling mean less glue code.

Video summarization or >50 frames per call: Gemini 2.5. Nothing else sustains that sequence length at acceptable cost.

Cost-sensitive thumbnail tagging at scale: Gemini 2.5 with upstream resize. You avoid tile multiplication and stay on the cheapest input tier.

Regulated environments needing IAM and batch: Gemini 2.5 on Vertex, or Claude via AWS Bedrock if you already live there.

Pick based on the image shape you actually send, not the headline vision API pricing GPT-4o Claude Gemini 2.5 number on the pricing page.