Agent Sends Full-Size Images to the API — Wastes Tokens on Unnecessary Resolution
Symptom
- Vision API calls cost 5–10× more than expected
- Screenshots from 4K monitors use ~6,000 tokens; answers don’t require that detail
- Agent sends raw PDF page renders at 300 DPI when Claude only needs to read text
- Image preprocessing step is absent — images are forwarded as-is
- Token usage spikes for vision-heavy tasks, making them uneconomical
- PDF-to-image pipeline sends full A4 pages at maximum resolution
Root Cause
Claude charges for vision tokens based on image dimensions. A 2048×2048 image costs ~1,600 tokens (Claude’s internal tile system). A 4096×4096 image costs ~6,400 tokens. Most vision tasks — reading text, identifying objects, extracting structured data, UI analysis — don’t require full resolution. Resizing to the minimum resolution that preserves task-relevant detail, then re-encoding as JPEG at 85% quality, typically reduces vision token cost by 70–90% with no quality loss for the task.
Fix
Option 1: Auto-resize before sending — apply max dimension constraint
import anthropic
import base64
import io
from PIL import Image # pip install pillow
from pathlib import Path
def resize_image_for_claude(
image_input: bytes | str | Path,
max_dimension: int = 1568, # Claude's recommended max for most tasks
quality: int = 85, # JPEG quality (85 = excellent, much smaller)
output_format: str = "JPEG"
) -> tuple[bytes, str]:
"""
Resize and compress an image to minimize vision tokens.
Returns (image_bytes, media_type).
Claude's vision token costs by size:
- Up to 1092×1092: ~340 tokens (1 tile)
- Up to 1568×1568: ~1,360 tokens (4 tiles)
- Up to 2048×2048: ~1,360 tokens (4 tiles) — same as 1568 but larger file
- 4096×4096: ~6,400 tokens — avoid unless detail is essential
"""
if isinstance(image_input, (str, Path)):
img = Image.open(image_input)
else:
img = Image.open(io.BytesIO(image_input))
# Convert to RGB for JPEG (JPEG doesn't support alpha)
if output_format == "JPEG" and img.mode in ("RGBA", "P", "LA"):
background = Image.new("RGB", img.size, (255, 255, 255))
if img.mode == "P":
img = img.convert("RGBA")
background.paste(img, mask=img.split()[-1] if img.mode in ("RGBA", "LA") else None)
img = background
elif img.mode != "RGB" and output_format == "JPEG":
img = img.convert("RGB")
# Resize if larger than max_dimension on either axis
w, h = img.size
if max(w, h) > max_dimension:
if w >= h:
new_w = max_dimension
new_h = int(h * max_dimension / w)
else:
new_h = max_dimension
new_w = int(w * max_dimension / h)
img = img.resize((new_w, new_h), Image.LANCZOS)
# Compress to bytes
buf = io.BytesIO()
if output_format == "JPEG":
img.save(buf, format="JPEG", quality=quality, optimize=True)
media_type = "image/jpeg"
else:
img.save(buf, format="PNG", optimize=True)
media_type = "image/png"
return buf.getvalue(), media_type
def send_image_to_claude(
image_path: str,
question: str,
max_dimension: int = 1568,
model: str = "claude-sonnet-4-6"
) -> str:
"""Send an image to Claude after resizing for cost efficiency."""
client = anthropic.Anthropic()
image_bytes, media_type = resize_image_for_claude(
image_path,
max_dimension=max_dimension
)
image_b64 = base64.standard_b64encode(image_bytes).decode("utf-8")
# Log size reduction
original_size = Path(image_path).stat().st_size if Path(image_path).exists() else 0
print(f"Image: {original_size:,}B original → {len(image_bytes):,}B resized "
f"({100*(1-len(image_bytes)/max(original_size,1)):.0f}% smaller)")
response = client.messages.create(
model=model,
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": media_type,
"data": image_b64
}
},
{"type": "text", "text": question}
]
}]
)
return response.content[0].text
Option 2: Task-based resolution presets — different sizes for different tasks
import anthropic
import base64
import io
from PIL import Image
from enum import Enum
class VisionTask(Enum):
OCR = "ocr" # Reading text → needs moderate resolution
UI_ANALYSIS = "ui_analysis" # Analyzing UI layout → moderate
OBJECT_DETECTION = "objects" # Finding objects → can be lower res
CHART_READING = "charts" # Reading chart data → needs some detail
PHOTO_DESCRIPTION = "photo" # Describing photos → low res usually fine
DOCUMENT_ANALYSIS = "document" # Structured docs → moderate res
DIAGRAM_READING = "diagram" # Technical diagrams → higher res
# Resolution presets per task type (max dimension):
TASK_RESOLUTION = {
VisionTask.OCR: 1568, # Text needs some resolution
VisionTask.UI_ANALYSIS: 1568, # UI elements need to be readable
VisionTask.OBJECT_DETECTION: 800, # Object recognition works at low res
VisionTask.CHART_READING: 1092, # Charts need detail but not full res
VisionTask.PHOTO_DESCRIPTION: 800, # Description doesn't need full detail
VisionTask.DOCUMENT_ANALYSIS: 1568, # Documents need readable text
VisionTask.DIAGRAM_READING: 1568, # Diagrams benefit from resolution
}
# Approximate token costs at each resolution (for reference):
APPROX_TOKENS = {
800: 170, # ~170 tokens for 800×600-ish image
1092: 340, # ~340 tokens (1 tile)
1568: 1360, # ~1360 tokens (4 tiles)
2048: 1360, # Same tile count as 1568 — no benefit to going bigger
}
def prepare_image_for_task(
image_bytes: bytes,
task: VisionTask,
jpeg_quality: int = 85
) -> tuple[bytes, str, int]:
"""
Resize image according to task requirements.
Returns (optimized_bytes, media_type, approx_tokens).
"""
max_dim = TASK_RESOLUTION[task]
img = Image.open(io.BytesIO(image_bytes))
if img.mode in ("RGBA", "P", "LA"):
bg = Image.new("RGB", img.size, (255, 255, 255))
if img.mode == "P":
img = img.convert("RGBA")
if img.mode in ("RGBA", "LA"):
bg.paste(img, mask=img.split()[-1])
else:
bg.paste(img)
img = bg
elif img.mode != "RGB":
img = img.convert("RGB")
w, h = img.size
if max(w, h) > max_dim:
scale = max_dim / max(w, h)
img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=jpeg_quality, optimize=True)
optimized = buf.getvalue()
# Estimate token cost based on resized dimensions:
rw, rh = img.size
max_resized = max(rw, rh)
approx_tokens = APPROX_TOKENS.get(
min(APPROX_TOKENS.keys(), key=lambda k: abs(k - max_resized)),
1360
)
return optimized, "image/jpeg", approx_tokens
def analyze_image(
image_bytes: bytes,
question: str,
task: VisionTask = VisionTask.PHOTO_DESCRIPTION,
model: str = "claude-sonnet-4-6"
) -> dict:
client = anthropic.Anthropic()
optimized, media_type, est_tokens = prepare_image_for_task(image_bytes, task)
img_b64 = base64.standard_b64encode(optimized).decode()
response = client.messages.create(
model=model,
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": media_type, "data": img_b64}},
{"type": "text", "text": question}
]
}]
)
return {
"answer": response.content[0].text,
"estimated_image_tokens": est_tokens,
"actual_input_tokens": response.usage.input_tokens,
"image_size_bytes": len(optimized)
}
# Usage examples:
result = analyze_image(screenshot_bytes, "What error is shown?", task=VisionTask.OCR)
result = analyze_image(photo_bytes, "Describe this image", task=VisionTask.PHOTO_DESCRIPTION)
# Object detection → 800px max (170 tokens vs 6400 for 4K = 97% savings)
Option 3: URL-referenced images — skip base64 overhead entirely
import anthropic
from urllib.parse import urlparse
client = anthropic.Anthropic()
def analyze_image_by_url(
image_url: str,
question: str,
model: str = "claude-sonnet-4-6"
) -> str:
"""
Pass an image URL instead of base64.
Claude fetches it directly — no base64 encoding overhead in the API request.
Saves bandwidth and request size. Claude still processes the full image,
so pair with a CDN that serves resized images for full cost control.
"""
response = client.messages.create(
model=model,
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "url",
"url": image_url
}
},
{"type": "text", "text": question}
]
}]
)
return response.content[0].text
# For CDN-hosted images, use image transformation URLs to resize server-side:
def build_resized_cdn_url(
original_url: str,
max_width: int = 1568,
quality: int = 85,
cdn: str = "cloudinary"
) -> str:
"""
Build a CDN URL that serves a resized version of the image.
This offloads resizing to the CDN — no local PIL processing needed.
"""
if cdn == "cloudinary":
# Cloudinary URL transformation: insert /w_1568,q_85/ into URL
parts = original_url.split("/upload/")
if len(parts) == 2:
return f"{parts[0]}/upload/w_{max_width},q_{quality},f_auto/{parts[1]}"
if cdn == "imgix":
sep = "&" if "?" in original_url else "?"
return f"{original_url}{sep}w={max_width}&q={quality}&auto=format"
if cdn == "bunny":
sep = "&" if "?" in original_url else "?"
return f"{original_url}{sep}width={max_width}&quality={quality}"
return original_url # Fallback: return original
# Example with Cloudinary:
resized_url = build_resized_cdn_url(
"https://res.cloudinary.com/myapp/image/upload/screenshots/page1.png",
max_width=1568,
cdn="cloudinary"
)
answer = analyze_image_by_url(resized_url, "What is shown on this page?")
Option 4: Multi-image cost estimator — report cost before sending
import io
import math
import anthropic
from PIL import Image
CLAUDE_SONNET_VISION_COST_PER_1K_INPUT = 0.003 # $ per 1K input tokens (Sonnet)
def estimate_image_tokens(width: int, height: int) -> int:
"""
Estimate Claude vision token cost for an image of given dimensions.
Based on Claude's tile-based vision pricing.
"""
# Claude resizes internally: fits within 1568×1568, then tiles at 512px
max_dim = max(width, height)
if max_dim <= 1092:
# Fits in 1 tile
return 1 * 1334 // 4 # ~334 tokens per tile
elif max_dim <= 1568:
# Up to 4 tiles
tiles_w = math.ceil(width / 512)
tiles_h = math.ceil(height / 512)
return tiles_w * tiles_h * 334
else:
# Larger images: Claude resizes to fit 1568×1568 first
scale = 1568 / max_dim
rw = int(width * scale)
rh = int(height * scale)
tiles_w = math.ceil(rw / 512)
tiles_h = math.ceil(rh / 512)
return tiles_w * tiles_h * 334
def audit_images(image_paths: list[str]) -> dict:
"""
Audit a set of images for vision token cost.
Returns recommendations for which to resize.
"""
total_tokens = 0
recommendations = []
for path in image_paths:
img = Image.open(path)
w, h = img.size
tokens_full = estimate_image_tokens(w, h)
cost_full = tokens_full * CLAUDE_SONNET_VISION_COST_PER_1K_INPUT / 1000
# Calculate tokens at recommended sizes:
tokens_1568 = estimate_image_tokens(min(w, 1568), min(h, 1568))
tokens_1092 = estimate_image_tokens(min(w, 1092), min(h, 1092))
tokens_800 = estimate_image_tokens(min(w, 800), min(h, 800))
total_tokens += tokens_full
recommendations.append({
"path": path,
"original_size": f"{w}×{h}",
"tokens_as_is": tokens_full,
"cost_as_is_usd": round(cost_full, 5),
"tokens_at_1568": tokens_1568,
"tokens_at_1092": tokens_1092,
"tokens_at_800": tokens_800,
"savings_at_800": f"{100*(1-tokens_800/max(tokens_full,1)):.0f}%",
"recommendation": (
"resize to 800px" if max(w, h) > 2000 and tokens_full > 2000 else
"resize to 1092px" if max(w, h) > 1568 else
"ok"
)
})
total_cost = total_tokens * CLAUDE_SONNET_VISION_COST_PER_1K_INPUT / 1000
return {
"images": len(image_paths),
"total_tokens": total_tokens,
"total_cost_usd": round(total_cost, 4),
"recommendations": recommendations
}
Option 5: PDF page rendering at optimal DPI — avoid over-rendering
import anthropic
import base64
import io
from pathlib import Path
def pdf_page_to_image(
pdf_path: str,
page_number: int = 0,
dpi: int = 100, # 72-150 DPI is enough for Claude; 300 is excessive
max_dimension: int = 1568
) -> bytes:
"""
Render a PDF page as an image at the right DPI for Claude.
DPI guide for Claude vision tasks:
- 72 DPI: Rough layout, large text only
- 100 DPI: Standard text, form fields — good default
- 150 DPI: Small text, tables with fine borders
- 300 DPI: Highly detailed graphics, microscopic text — rarely needed
"""
try:
import pymupdf # pip install pymupdf (formerly fitz)
doc = pymupdf.open(pdf_path)
page = doc[page_number]
mat = pymupdf.Matrix(dpi / 72, dpi / 72)
pix = page.get_pixmap(matrix=mat, colorspace=pymupdf.csRGB)
img_bytes = pix.tobytes("jpeg", jpg_quality=85)
doc.close()
except ImportError:
raise RuntimeError("Install pymupdf: pip install pymupdf")
# Apply max_dimension constraint via PIL
from PIL import Image
img = Image.open(io.BytesIO(img_bytes))
w, h = img.size
if max(w, h) > max_dimension:
scale = max_dimension / max(w, h)
img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=85, optimize=True)
img_bytes = buf.getvalue()
return img_bytes
def analyze_pdf_page(
pdf_path: str,
question: str,
page_number: int = 0,
dpi: int = 100,
model: str = "claude-sonnet-4-6"
) -> str:
"""
Extract information from a PDF page efficiently.
Uses 100 DPI (vs typical 300 DPI) — 9× fewer pixels, ~80% token savings.
"""
client = anthropic.Anthropic()
img_bytes = pdf_page_to_image(pdf_path, page_number, dpi=dpi)
img_b64 = base64.standard_b64encode(img_bytes).decode()
response = client.messages.create(
model=model,
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": img_b64}},
{"type": "text", "text": question}
]
}]
)
return response.content[0].text
Option 6: Image caching with hash key — skip re-encoding the same image
import anthropic
import base64
import hashlib
import json
from pathlib import Path
from PIL import Image
import io
class CachedImageAnalyzer:
"""
Cache Claude's analysis of images by content hash.
Avoids sending the same image multiple times across sessions.
Pair with image resizing for maximum cost efficiency.
"""
def __init__(
self,
cache_dir: str = "/tmp/image_analysis_cache",
max_dimension: int = 1568
):
self._cache_dir = Path(cache_dir)
self._cache_dir.mkdir(parents=True, exist_ok=True)
self._max_dim = max_dimension
self._client = anthropic.Anthropic()
def _image_hash(self, image_bytes: bytes) -> str:
return hashlib.sha256(image_bytes).hexdigest()[:16]
def _resize(self, image_bytes: bytes) -> tuple[bytes, str]:
img = Image.open(io.BytesIO(image_bytes))
if img.mode not in ("RGB",):
img = img.convert("RGB")
w, h = img.size
if max(w, h) > self._max_dim:
scale = self._max_dim / max(w, h)
img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=85, optimize=True)
return buf.getvalue(), "image/jpeg"
def analyze(self, image_bytes: bytes, question: str, model: str = "claude-sonnet-4-6") -> str:
cache_key = f"{self._image_hash(image_bytes)}-{hashlib.md5(question.encode()).hexdigest()[:8]}"
cache_path = self._cache_dir / f"{cache_key}.json"
if cache_path.exists():
cached = json.loads(cache_path.read_text())
return cached["answer"]
# Resize before sending
resized, media_type = self._resize(image_bytes)
img_b64 = base64.standard_b64encode(resized).decode()
response = self._client.messages.create(
model=model,
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": media_type, "data": img_b64}},
{"type": "text", "text": question}
]
}]
)
answer = response.content[0].text
cache_path.write_text(json.dumps({
"answer": answer,
"tokens": response.usage.input_tokens + response.usage.output_tokens
}))
return answer
analyzer = CachedImageAnalyzer(max_dimension=1092)
# First call: resize + API call
answer1 = analyzer.analyze(screenshot_bytes, "What button is highlighted?")
# Second call with same image + question: instant cache hit, zero API cost
answer2 = analyzer.analyze(screenshot_bytes, "What button is highlighted?")
Image Size vs Token Cost
| Original Size | Tokens (as-is) | Tokens at 1092px | Tokens at 800px | Savings at 800px |
|---|---|---|---|---|
| 4096×4096 (4K) | ~6,400 | ~340 | ~170 | 97% |
| 2560×1440 (1440p) | ~1,700 | ~340 | ~170 | 90% |
| 1920×1080 (1080p) | ~680 | ~340 | ~170 | 75% |
| 1280×720 (720p) | ~340 | ~340 | ~170 | 50% |
| 800×600 | ~170 | ~170 | ~170 | 0% (already small) |
Expected Token Savings
4K screenshot in a screenshot-analysis agent at 100 calls/day: 6,400 × 100 = 640,000 vision tokens/day Same screenshots resized to 1092px: 340 × 100 = 34,000 vision tokens/day Savings: 606,000 tokens/day × $0.003/1K = ~$1.82/day = ~$55/month per agent At scale (1,000 calls/day): ~$550/month savings from image resizing alone
Environment
- Any agent processing screenshots, document images, photos, or PDF pages; image token costs dominate vision-heavy agents and are the easiest cost to reduce (pure preprocessing, no behavior change); apply max_dimension=1092 by default and only increase to 1568 for tasks that genuinely need the extra resolution (fine text, small UI elements)
- Source: direct experience; unresized 4K screenshots are the single largest token waste in production vision agents — typically 10–30× more expensive than necessary with zero quality benefit for the task
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