[Bug] File loading adds 70% token overhead due to line number formatting

Found 2 possible duplicate issues:

1. https://github.com/anthropics/claude-code/issues/18218
2. https://github.com/anthropics/claude-code/issues/6910

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Found 2 possible duplicate issues: 1. [[Bug] Read tool consuming excessive tokens with line number prefixes #18218](https://github.com/anthropics/claude-code/issues/18218) 2. [[BUG] Read tool does not limit itself to 2000 lines by default #6910](https://github.com/anthropics/claude-code/issues/6910) ...

This is NOT a duplicate - it provides comprehensive analysis that unifies multiple scattered reports into a single root cause with quantifiable data and solutions.

How This Report Differs

vs #18218 (Read tool token consumption):

• That report identified line number overhead but couldn't explain the full discrepancy (missing ~4k tokens)
• My report quantifies the exact 1.7x overhead multiplier with reproducible tests
• Tests both @ syntax AND Read tool (not just Read)
• Provides actionable solutions (@raw: syntax, lazy injection)

vs #6910 (2000-line limit):

• That issue was closed as "not planned" and only addressed Read tool
• My report reveals the 2000-line truncation also affects @ syntax (undocumented)
• Combines BOTH the truncation issue AND the 70% token overhead into a unified analysis

New Contributions

1. Exact overhead quantified: 1.7x multiplier on all file loading
2. Reproducible test suite: Controlled files with known token counts (test files included)
3. Both environments tested: Identical behavior in CLI and VSCode
4. Root cause explained: Line numbers are UI concern, not AI concern
5. Solutions with ROI: 60-70% token reduction for typical workflows

This Explains Multiple Related Reports

Your report unifies scattered complaints that describe symptoms without identifying the root cause:

• #4804 - "60-80% of tokens are waste" → Root cause: 70% formatting overhead
• #16856 - "4x faster token consumption in v2.1.1" → May relate to formatting changes
• #15687 - "Read tool offset/limit consumes MORE tokens" → Because each read adds 1.7x overhead
• #8185 - "Premature context compaction" → Context fills faster due to overhead
• #2745 - "Excessive token usage" → Files consume 1.7x expected tokens
• #4002 - "File content exceeds token limit" → 28k raw becomes ~48k with overhead

Why This Matters

Current state (my real-world testing):

• 6 documentation files: 31,000 tokens (raw)
• Actual context consumption: 52,700 tokens
• Waste: 21,700 tokens (40% of available context)

With proposed solutions:

• Same 6 files: 31,000 tokens (raw)
• Optimized context: ~32,000 tokens
• Savings: 20,700 tokens = space for 5+ additional files

Actionable Solutions Proposed

1. Quick win: Add @raw:filename syntax (no line numbers for read-only context)
2. Read tool flag: --no-line-numbers option
3. Transparency: Show truncation warnings ("Loaded lines 1-2000 of 5000")
4. Optimal: Keep line numbers in UI layer only, inject lazily when Edit tool invoked

This report provides the data, analysis, and roadmap the team needs to resolve multiple user complaints with a single architectural improvement.

@bcherny - could you please have a look at this issue? This affects all Claude Code users and wastes 40% of context.
Would appreciate urgent review given the widespread impact.

Whoa. This is outstanding. I'm going to try to repro. If this is fact true - we're all getting MASSIVELY hit w/ respect to our 'Max' usage.

@puya any chance you know when this was introduced (how far we should roll back to)? Thank you.

@puya any chance you know when this was introduced (how far we should roll back to)? Thank you.

@willshen no unfortunately I'm not sure. I'm not even certain that this wasn't always there!

@puya since line number formatted is roughly a fixed size for each line, wouldn't the overhead % depend on the length of the line and become negligible as the line length increases?

Curious also to learn how you estimated the raw token count for the input files, and see what might be the cause of the additional input token count (maybe it's something other than just the line formatting)

@puya since line number formatted is roughly a fixed size for each line, wouldn't the overhead % depend on the length of the line and become negligible as the line length increases? Curious also to learn how you estimated the raw token count for the input files, and see what might be the cause of the additional input token count (maybe it's something other than just the line formatting)

Hey @willshen .. please see explanations below:

Question 1: Shouldn't overhead decrease with longer lines?

"Since line number formatting is roughly a fixed size for each line, wouldn't the overhead % depend on the length of the line and become negligible as the line length increases?"

Short answer: Yes, you're absolutely correct! And our data confirms this.

The Data Shows Exactly This Pattern

| File Type | Avg Line Length | Raw Tokens | Formatted Tokens | Multiplier | Overhead % |
|-----------|----------------|------------|------------------|------------|------------|
| Numbers only | ~1 char/line | 2,001 | 6,005 | 3.00x | 200% |
| Short code | ~7 chars/line | 7,002 | 11,006 | 1.57x | 57% |
| Longer code | ~23 chars/line | 43,002 | 63,006 | 1.47x | 47% |

Why This Happens

Line number formatting adds a fixed cost per line:

     1→content here

Breaking down the overhead:

• 5-6 spaces for padding
• Line number (1-4 digits)
• Arrow →
• Total: ~8-10 characters per line (fixed cost)

For very short lines (like 1\n):

• Original: 1 character
• With formatting: ~10 characters
• Overhead: 900% (extreme case)

For typical code lines (like const value = 123;):

• Original: ~20 characters
• With formatting: ~30 characters
• Overhead: 50%

For very long lines (like documentation paragraphs):

• Original: 200 characters
• With formatting: ~210 characters
• Overhead: 5% (much lower)

Real-World Impact

You're right that overhead becomes proportionally smaller with longer lines, BUT:

1. Most code files have medium-length lines (~20-80 chars)

• This gives 30-60% overhead consistently
• Not negligible!

1. Configuration files, CSV, JSON often have short lines

• These can hit 100-200% overhead
• Very impactful

1. Even "small" overhead compounds

• At 47% overhead (long lines), loading 100k tokens costs 147k
• That's still 47k tokens wasted!

Example: Real Code vs Numbers

Numbers file (test_1000_numbers.txt):

1
2
3
...

• Average line: 1-4 characters
• Overhead: 200% (3x multiplier)

JavaScript file (test_1000_short.js):

const value1 = 1;
const value2 = 2;
...

• Average line: ~20 characters
• Overhead: 57% (1.57x multiplier)

Documentation (typical markdown):

This is a longer paragraph with detailed explanations that spans multiple words and provides context.

• Average line: ~80-100 characters
• Overhead: ~30-40% (1.3-1.4x multiplier estimated)

So Yes, You're Correct!

The overhead IS line-length dependent. However:

• ❌ It doesn't become "negligible" for most real code
• ✅ It varies from 30% to 200% depending on content
• ✅ The average across typical files is ~50-70%
• ✅ Even at the lower end, it's still significant

---

Question 2: How did you measure raw tokens?

"Curious also to learn how you estimated the raw token count for the input files, and see what might be the cause of the additional input token count (maybe it's something other than just the line formatting)"

Method: We Used tiktoken (OpenAI's Official Tokenizer)

Tool used: token-counter CLI tool (this is my opensource repo [https://pypi.org/project/tokens-cli/](url) that you can also install through "pip install token-counter-cli"
Encoding: cl100k_base (the same encoding Claude uses)
Why this is accurate: Claude Code uses the same tokenizer under the hood

Exact Commands Run

# Install token-counter
pip install tiktoken

# Count tokens in a file
token-counter test_1000_numbers.txt

Output:

                        Token Count Results
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ File Path                     ┃ Token Count ┃ Encoding    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ test_1000_numbers.txt         │       2,001 │ cl100k_base │
└───────────────────────────────┴─────────────┴─────────────┘

Verification: You Can Test Yourself

Test files linked below with known token counts:

test_1000_numbers.txt
test_1000_short.js
test_5000_numbers.txt
test_5000_short.js

# Clone or download the test files
cd claude-token-test/

# Verify token counts
token-counter test_1000_numbers.txt  # Should show: 2,001
token-counter test_1000_short.js      # Should show: 7,002

Or use Python directly:

import tiktoken

encoding = tiktoken.get_encoding("cl100k_base")
with open("test_1000_numbers.txt") as f:
    content = f.read()
    tokens = len(encoding.encode(content))
    print(f"Tokens: {tokens}")  # Output: 2001

What Else Could Cause Additional Tokens?

You're right to ask! Here's what we investigated:

Hypothesis 1: Conversation Overhead

What it is: Your prompt + Claude's response
Measured: ~200-500 tokens per exchange
Accounted for: Yes, we subtracted this in our calculations

Example:

• User prompt: "Read this file" (~10 tokens)
• Claude's response: "I can see the file contains..." (~200 tokens)
• Total conversation: ~210 tokens

We subtracted this to isolate just the file overhead.

Hypothesis 2: System Prompt & Tools

What it is: Tool definitions, system instructions
Measured: ~20-40k tokens (shown in /context as "System tools", "System prompt")
Accounted for: Yes, these appear in separate categories, NOT in "Messages"

From /context output:

System prompt     3.2k      1.6%
System tools      17.6k     8.8%
Messages          61.4k     30.7%   ← Only this includes file content

Hypothesis 3: Line Number Formatting

What it is: The 1→content formatting
Measured: By simulating the formatting and counting tokens
Result: This FULLY explains the overhead!

Our simulation:

# Simulated formatting
formatted = "     1→1\n     2→2\n     3→3\n..."

# Count tokens
simulated_tokens = count_tokens(formatted)  # 6,005 tokens
raw_tokens = count_tokens(raw_file)          # 2,001 tokens

# Overhead matches!
overhead = simulated_tokens - raw_tokens  # 4,004 tokens (200%)

Hypothesis 4: Hidden Metadata

What it is: File paths, timestamps, etc.
Measured: Tested with simulation - not significant
Result: Minimal impact (<1% of overhead)

Hypothesis 5: VSCode Extension Context

What it is: <ide_opened_file> tags when you open files
Measured: Tested in both VSCode AND terminal CLI
Result: Same overhead in both - not VSCode-specific!

Test results:

• VSCode: 6.2k tokens (for 2k file)
• CLI: 6.3k tokens (for 2k file)
• Difference: Only 100 tokens - not significant

The Smoking Gun: Simulation Matched Reality

We simulated ONLY the line number formatting and got results very close to actual:

| File | Raw | Simulated | Actual (Claude Code) | Difference |
|------|-----|-----------|----------------------|------------|
| test_1000_numbers.txt | 2,001 | 6,005 | ~6,200 | 195 tokens (3%) |
| test_5000_short.js | 16,002 | 24,002 | ~27,100 | 3,098 tokens (13%) |

Conclusion: Line number formatting accounts for 87-97% of the overhead. The remaining 3-13% is likely:

• Conversation overhead variance
• Minor formatting differences we didn't capture exactly
• Possible additional metadata

Could It Be Something Else?

We considered:

• ✅ Git status context - No, appears in separate category
• ✅ File metadata - Tested, minimal impact
• ✅ VSCode-specific - No, same in CLI
• ✅ Model-specific - No, consistent across sessions
• ✅ Compression artifacts - No, using same tokenizer

The evidence is strong: It's the line number formatting.

How You Can Verify

1. Check raw tokens yourself:

``bash
pip install tiktoken
python -c "import tiktoken; print(len(tiktoken.get_encoding('cl100k_base').encode(open('yourfile.txt').read())))"
``

1. Test in Claude Code:

• Open Claude Code
• Use @ yourfile.txt
• Run /context
• Compare "Messages" tokens to your raw count

1. Run our test suite:

``bash
cd claude-token-test/
python reproduce_overhead_issue.py
``

---

Summary

Q1: Doesn't overhead decrease with longer lines?
✅ Yes! Our data confirms this (47% for long lines vs 200% for short lines)
❌ But it doesn't become negligible - still 30-70% for most real files

Q2: How did you measure raw tokens & what else could it be?
✅ Used tiktoken (cl100k_base) - same as Claude uses
✅ Investigated 5+ alternative hypotheses
✅ Simulation of line formatting alone explains 87-97% of overhead
✅ All data is reproducible with included test files

The evidence strongly points to line number formatting as the primary cause of overhead, with line-length dependency exactly as you predicted!

---

Files for verification:

• claude-token-test/test_*.txt - Test files with known token counts
• reproduce_overhead_issue.py - Simulation script
• INVESTIGATION_REPORT.md - Full methodology

INVESTIGATION_REPORT.md
reproduce_overhead_issue.py

Try it yourself:

token-counter claude-token-test/test_1000_numbers.txt
# Then load it in Claude Code with @ syntax and run /context

While waiting for an official fix, here are some user-side workarounds that help mitigate the overhead:

1. Delegate file exploration to subagents

Instead of reading files directly in your main session, use Task agents to explore codebases. The overhead stays in the cheaper subagent's context, not your main session:

Task(subagent_type="Explore", prompt="Find all auth-related files and summarize their structure")

The subagent can read dozens of files, but only returns a concise summary to your main session.

2. Use targeted reads with offset/limit

The Read tool accepts offset and limit parameters. Instead of loading entire files:

Read(file_path="/path/to/file.py", offset=100, limit=50)  # Lines 100-150 only

This is especially useful when you know the relevant section (from error messages, grep results, etc.).

3. Route large-context tasks to extended-context models

If you have MCP model routing configured, route file-heavy analysis to models with larger context windows. This preserves your main session's context for interactive work.

4. Grep before Read

Use Grep to find relevant line numbers first, then do targeted reads:

Grep(pattern="def authenticate", path="src/", output_mode="content", -C=5)

This shows the context around matches without loading entire files.

---

These don't fix the root cause but can significantly reduce the impact until an official fix lands.

Still no updating this !?

I noticed the line number and formatting issue today too and before writing a new issue I will append my points here to prevent this from going stale:

The read tool adds approximately 20% formatting overhead in my test with an XML file. The fixed per line overhead applies to all multi-line content, regardless of context. Line numbers and arrow markers may be useful for agent accuracy in edit contexts, but they add unnecessary overhead for information retrieval reads. Leading whitespaces provide no benefit to the model and represent pure formatting waste (similar to JSON pretty vs compact).

Current Behavior

• Every multi-line file read includes:
• Line number characters
• Arrow symbols (→)
• Leading whitespaces
• System reminder about malware awareness

Test Case Metrics (51KB XML, 1523 lines)

• Original: 51,783 characters
• With formatting: 62,784 characters (+21.24% or +20.59% without system reminder)
• Breakdown:
• +1,528 arrow symbols
• +4,163 leading whitespaces
• +4,971 line number characters
• +339 system reminder

Impact

• 339 characters overhead on every read (system reminder per file)
• 7 characters x line count overhead on every multi-line read (leading whitespaces, line number, arrow symbol per line)
• Compounds when file size exceeds limits and content is persisted
• Unnecessary overhead for pure information retrieval use cases

Expected Behavior

• Remove leading whitespaces (not useful for model)
• Remove line numbers for non-edit reads (only needed in edit context)
• Remove or consolidate system reminder (already handled in other security mechanisms)

With the update 2.1.86 there was a change in this logic. The line numbers are still there but the numbers are more compact:

• Before: "\n 2→"
• 7 characters x line count
• Markdown reduces consecutive whitespaces to a single. There should be 5 whitespace in front of the "2".
• Now: "\n2\t"
• (2 fixed characters for + line number) x line count

The system reminder about malware awareness is unchanged. This is an improvement but overhead is still there.