Image Attachments Consume Too Many Tokens — Vision Costs Spike
Symptom
- Single API call with one screenshot costs 4,000 tokens before any text
- Multi-turn conversation including the same image repeatedly costs 10× more than expected
- Sending a 4K screenshot when the task only needs to read a text label
- Context window fills after 3-4 turns because images are resized too large
- Vision API costs 50× more than text-only equivalent tasks
Root Cause
Vision models tokenize images by dividing them into tiles. Anthropic Claude uses 85 tokens per 85×85 pixel tile (approximately). A 1920×1080 image at full resolution generates ~800 tiles = ~68,000 tokens. Sending the same image multiple turns, or failing to resize before sending, compounds the cost dramatically.
Fix
Option 1: Resize images to minimum needed resolution
from PIL import Image
import io
import base64
def resize_image_for_task(
image_bytes: bytes,
task: str = "general",
max_dimension: int = None
) -> bytes:
"""
Resize image to the minimum resolution needed for the task.
Vision models can read text at much lower resolution than display quality.
"""
# Task-specific max dimensions
task_limits = {
"read_text": (1024, 768), # Text is readable at 1024px wide
"classify": (512, 512), # Classification needs less detail
"detect_ui": (1280, 960), # UI element detection
"count_objects": (800, 600), # Object counting
"general": (1024, 1024), # Default
}
max_w, max_h = task_limits.get(task, (1024, 1024))
if max_dimension:
max_w = max_h = max_dimension
img = Image.open(io.BytesIO(image_bytes))
original_size = img.size
# Only resize if larger than the limit
if img.width > max_w or img.height > max_h:
img.thumbnail((max_w, max_h), Image.LANCZOS)
print(f"Resized: {original_size} → {img.size} "
f"(~{estimate_image_tokens(original_size)} → ~{estimate_image_tokens(img.size)} tokens)")
output = io.BytesIO()
img.save(output, format="JPEG", quality=85, optimize=True)
return output.getvalue()
def estimate_image_tokens(size: tuple[int, int]) -> int:
"""Estimate token count for Claude vision (85px tiles, ~85 tokens each)"""
tiles_w = (size[0] + 84) // 85
tiles_h = (size[1] + 84) // 85
return tiles_w * tiles_h * 85
# Before sending any image:
resized = resize_image_for_task(raw_image_bytes, task="read_text")
# 1920×1080 → 1024×576: ~68,000 → ~19,000 tokens (72% reduction)
Option 2: Cache images — don’t send the same image twice
import hashlib
class ImageCache:
"""
Track which images have already been sent in this conversation.
Replace repeated images with a text reference to the first occurrence.
"""
def __init__(self):
self._sent: dict[str, int] = {} # image_hash → turn_number
def image_hash(self, image_bytes: bytes) -> str:
return hashlib.sha256(image_bytes).hexdigest()[:16]
def should_send(self, image_bytes: bytes, current_turn: int) -> bool:
h = self.image_hash(image_bytes)
if h in self._sent:
print(f"Image already sent at turn {self._sent[h]} — skip resend")
return False
self._sent[h] = current_turn
return True
def reference_text(self, image_bytes: bytes) -> str:
h = self.image_hash(image_bytes)
turn = self._sent.get(h, "unknown")
return f"[Image already provided at turn {turn} — refer to that image]"
cache = ImageCache()
def build_message_content(text: str, images: list[bytes], turn: int) -> list:
"""Build message content, deduplicating images already sent"""
content = []
for img_bytes in images:
if cache.should_send(img_bytes, turn):
content.append({
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": base64.b64encode(img_bytes).decode()
}
})
else:
# Reference instead of resend — 0 tokens vs 5,000+ tokens
content.append({
"type": "text",
"text": cache.reference_text(img_bytes)
})
content.append({"type": "text", "text": text})
return content
Option 3: Use prompt caching for repeated images (Anthropic)
import anthropic
# Anthropic supports prompt caching for image content
# Cache the image in the first call — subsequent calls cost only 10% of original
client = anthropic.Anthropic()
def call_with_image_cache(
image_bytes: bytes,
questions: list[str]
) -> list[str]:
"""
Process multiple questions about the same image.
Cache the image so only the first call incurs full image token cost.
"""
image_b64 = base64.b64encode(image_bytes).decode()
# First call: image is cached with cache_control
# Subsequent calls: image tokens are read from cache at 10% cost
results = []
for i, question in enumerate(questions):
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": image_b64
},
"cache_control": {"type": "ephemeral"} # Cache this image
},
{
"type": "text",
"text": question
}
]
}]
)
results.append(response.content[0].text)
if i == 0:
print(f"First call: full image tokens. Cache created.")
else:
print(f"Call {i+1}: image served from cache (~10% token cost)")
return results
# Ask 5 questions about the same image:
answers = call_with_image_cache(screenshot_bytes, [
"What is the page title?",
"How many buttons are visible?",
"What color is the header?",
"Is there an error message?",
"What is the main CTA text?",
])
Option 4: Convert image to text when possible
import pytesseract
from PIL import Image
import io
def image_to_text_if_feasible(image_bytes: bytes, threshold_tokens: int = 1000) -> str | bytes:
"""
If image is primarily text, extract it via OCR — costs ~0 tokens vs thousands.
Falls back to image if OCR confidence is too low.
"""
img = Image.open(io.BytesIO(image_bytes))
estimated_tokens = estimate_image_tokens(img.size)
if estimated_tokens < threshold_tokens:
return image_bytes # Small image — send as-is
# Try OCR
try:
ocr_result = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)
# Check average confidence
confidences = [c for c in ocr_result["conf"] if c != -1]
avg_confidence = sum(confidences) / len(confidences) if confidences else 0
if avg_confidence > 70: # High confidence — text image
text = pytesseract.image_to_string(img)
word_count = len(text.split())
text_tokens = word_count // 0.75 # ~0.75 words per token
if text_tokens < estimated_tokens * 0.3:
print(f"OCR: {estimated_tokens} image tokens → ~{text_tokens:.0f} text tokens")
return f"[Image converted to text via OCR]\n{text}"
except Exception as e:
print(f"OCR failed: {e}")
return image_bytes # Fall back to sending image
# Usage:
content = image_to_text_if_feasible(screenshot_bytes)
if isinstance(content, str):
# Send as text — cheap
message_content = [{"type": "text", "text": content}]
else:
# Send as image — resize first
resized = resize_image_for_task(content, task="read_text")
message_content = [{"type": "image", "source": {...}}]
Option 5: Image token budget guard
MAX_IMAGE_TOKENS_PER_CALL = 10_000 # Hard budget per API call
def enforce_image_token_budget(
images: list[bytes],
budget: int = MAX_IMAGE_TOKENS_PER_CALL
) -> list[bytes]:
"""
Resize images to fit within a token budget.
Distributes budget evenly across all images.
"""
if not images:
return images
per_image_budget = budget // len(images)
result = []
for img_bytes in images:
img = Image.open(io.BytesIO(img_bytes))
current_tokens = estimate_image_tokens(img.size)
if current_tokens <= per_image_budget:
result.append(img_bytes)
continue
# Calculate scale factor to hit budget
scale = (per_image_budget / current_tokens) ** 0.5
new_w = int(img.width * scale)
new_h = int(img.height * scale)
img = img.resize((new_w, new_h), Image.LANCZOS)
print(f"Budget resize: {current_tokens} → {estimate_image_tokens((new_w, new_h))} tokens")
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=80)
result.append(buf.getvalue())
return result
# Ensure all images in a call fit within budget:
safe_images = enforce_image_token_budget(user_images, budget=8000)
Image Token Cost by Resolution
| Resolution | Approx tokens | Cost ratio |
|---|---|---|
| 3840×2160 (4K) | ~230,000 | 100× |
| 1920×1080 (FHD) | ~68,000 | 30× |
| 1280×720 (HD) | ~31,000 | 13× |
| 1024×768 | ~20,000 | 9× |
| 512×512 | ~5,000 | 2× |
| 256×256 | ~1,300 | 1× |
Expected Token Savings
4K screenshot per turn, 10 turns: ~2,300,000 tokens 1024×576 resize + cache: ~190,000 tokens (92% reduction)
Environment
- Any agent with vision/multimodal capability; critical for UI automation, document processing, and screenshot analysis agents
- Source: direct measurement of Claude vision token costs at various resolutions
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