Gunning Fog Index vs Flesch-Kincaid Grade Level: Which Readability Formula Is Better?

1. Introduction

You’ve run your text through a readability checker and gotten two different results:

Flesch-Kincaid Grade Level: 9.2 (9th-grade level, high school)

Gunning Fog Index: 11.8 (11th-grade level, late high school)

Which one is right? And more importantly: which one should you trust?

Both Gunning Fog Index and Flesch-Kincaid Grade Level are widely-used readability metrics that output a grade level (1–18+). Both use similar linguistic inputs (sentence length, word complexity). But they weight those inputs differently — and sometimes their assessments diverge significantly.

In this article, we’ll compare these two formulas head-to-head:

• How each one works and what it measures
• The history and origins of each formula
• Real examples where they agree and where they diverge
• When to use Gunning Fog (and when Flesch-Kincaid is better)
• How to interpret differences between the two
• Which one to rely on for different situations

Whether you’re a writer trying to optimize readability, an educator selecting materials, a researcher analyzing text, or a content strategist building a portfolio, this guide will help you understand both formulas and choose the right one for your needs.

2. Define the Core Concepts: Gunning Fog vs. Flesch-Kincaid

Flesch-Kincaid Grade Level (Quick Review)

Flesch-Kincaid Grade Level expresses text difficulty as a U.S. grade level (1–18+). It measures:

• Average sentence length (words per sentence)
• Average word complexity (syllables per word)

Formula:

Grade = 0.39 × (words ÷ sentences) + 11.8 × (syllables ÷ words) − 15.59

Output: Grade 1–18+ (e.g., “Grade 8.5”)

Flesch-Kincaid is weighted equally between sentence length and word complexity. If your text has long sentences OR complex words, your grade level goes up. Both factors matter equally.

Gunning Fog Index (Quick Review)

Gunning Fog Index also expresses text difficulty as a U.S. grade level (1–18+). But it measures:

• Average sentence length (words per sentence)
• Percentage of “complex words” (words with 3+ syllables)

Formula:

Grade = 0.4 × [(words ÷ sentences) + 100 × (complex words ÷ words)]

Where “complex words” = words with 3+ syllables (excluding proper nouns, familiar jargon, compound words)

Output: Grade 1–18+ (e.g., “Grade 11.2”)

Gunning Fog weighs complex words much more heavily than Flesch-Kincaid. It assumes that words with 3+ syllables are the primary driver of reading difficulty.

The Key Difference

In plain English: Gunning Fog is more sensitive to jargon and multi-syllabic words. Flesch-Kincaid is more balanced across all linguistic factors.

3. The History: Why These Two Formulas Evolved Differently

Flesch-Kincaid (1948, adapted 1975)

As covered in our Flesch-Kincaid article, Rudolf Flesch created the Flesch Reading Ease formula in 1948, and the U.S. Navy adapted it to a grade-level scale in 1975.

Goal: A general-purpose readability metric that works across all types of writing (news, books, technical documents, marketing).

Design philosophy: Readability is primarily driven by two factors—sentence length AND word complexity—and both should be weighted significantly.

Gunning Fog Index (1952)

Robert Gunning, an American readability consultant, created the Gunning Fog Index in 1952 — earlier than Flesch-Kincaid’s adaptation, though later than the original Flesch formula.

Goal: A readability metric specifically designed to detect jargon and overly complex vocabulary in business and technical writing.

Design philosophy: Many writers and businesses hide behind complex words to sound impressive. Counting 3+ syllable words directly targets this problem. Gunning believed that complex vocabulary was the primary obstacle to clear communication.

The “fog” metaphor: Complex vocabulary creates a “fog” that obscures meaning. The Gunning Fog Index counts how thick that fog is.

Why Two Different Approaches?

The two formulas evolved from different concerns:

• Flesch-Kincaid: Designed as a general metric. Sentence length matters; word choice matters. Both contribute to difficulty.
• Gunning Fog: Designed to target a specific problem: business jargon. The focus is on catching unnecessarily complex words.

Over time, both became widely adopted. Different industries prefer different formulas:

• Gunning Fog: Popular in business, marketing, and journalism (where clarity and avoiding jargon is paramount)
• Flesch-Kincaid: Popular in education and software (Microsoft Word uses it by default)

4. How Each Formula Works: The Technical Breakdown

Flesch-Kincaid Formula & Example

Grade = 0.39 × (words ÷ sentences) + 11.8 × (syllables ÷ words) − 15.59

Sample text: “Technology companies must innovate continuously. However, innovation requires significant investment. Most firms struggle with this balance.”

Metrics:

• Words: 22
• Sentences: 3
• Syllables: tech-nol-o-gy (4) + com-pan-ies (3) + must (1) + in-no-vate (3) + con-tin-u-ous-ly (5) + How-ev-er (3) + in-no-va-tion (4) + re-quires (2) + sig-nif-i-cant (4) + in-vest-ment (3) + Most (1) + firms (1) + strug-gle (2) + with (1) + this (1) + bal-ance (2) = ~45 syllables

Calculate:

• Words per sentence: 22 ÷ 3 = 7.33
• Syllables per word: 45 ÷ 22 = 2.05

Flesch-Kincaid:

• Grade = 0.39 × 7.33 + 11.8 × 2.05 − 15.59
• Grade = 2.86 + 24.19 − 15.59
• Grade = 11.46 (11th-grade level)

Gunning Fog Formula & Example (Same Text)

Grade = 0.4 × [(words ÷ sentences) + 100 × (complex words ÷ words)]

Complex words in the text (3+ syllables):

• Technology (4)
• Companies (3)
• Innovate (3)
• Continuously (5)
• However (3)
• Innovation (4)
• Requires (2) — NOT complex
• Significant (4)
• Investment (3)
• Struggle (2) — NOT complex
• Balance (2) — NOT complex

Complex words count: 9 out of 22 words

Calculate:

• Words per sentence: 22 ÷ 3 = 7.33
• Complex word percentage: 9 ÷ 22 = 0.409 (40.9%)

Gunning Fog:

• Grade = 0.4 × [7.33 + 100 × 0.409]
• Grade = 0.4 × [7.33 + 40.9]
• Grade = 0.4 × 48.23
• Grade = 19.29 (college graduate level!)

Why Such a Big Difference?

Flesch-Kincaid: 11.46 (11th grade) Gunning Fog: 19.29 (graduate level)

The difference is 7.83 grade levels — enormous.

Why? Because this text is loaded with complex words (technology, innovation, continuously, significant, investment). Gunning Fog’s heavy weighting of 3+ syllable words catches this. Flesch-Kincaid notices the complexity but doesn’t penalize it as heavily.

This text has moderate sentence length (7.33 words per sentence is short-to-moderate), which keeps Flesch-Kincaid’s score lower. But it has high vocabulary complexity, which keeps Gunning Fog’s score high.

5. Real-World Examples: Where They Agree & Where They Diverge

Example 1: Simple Text (They Converge)

Sample: “The cat sat on the mat. It was warm. The cat liked it.”

Metrics:

• Flesch-Kincaid: Grade 1.8
• Gunning Fog: Grade 2.1

Result: Strong agreement. Both recognize this as extremely easy text. No complex words, short sentences.

Example 2: Moderate Text (They Converge)

Sample: “Many people enjoy reading Wikipedia. However, some articles can be difficult. The language is sometimes complex.”

Metrics:

• Flesch-Kincaid: Grade 8.2
• Gunning Fog: Grade 8.7

Result: Close agreement. Both recognize moderate difficulty. Some complex words, moderate sentences.

Example 3: Jargon-Heavy Text (They Diverge Significantly)

Sample: “The implementation of artificial intelligence infrastructure necessitates comprehensive technological infrastructure assessment. Organizations must prioritize algorithmic optimization and cybersecurity architecture resilience.”

Metrics:

• Flesch-Kincaid: Grade 12.1
• Gunning Fog: Grade 16.4

Result: Gunning Fog much higher. Why? Loaded with complex words (artificial, implementation, infrastructure, assessment, algorithmic, optimization, cybersecurity, architecture, resilience). Gunning Fog counts these heavily; Flesch-Kincaid does not.

This is the key divergence pattern: When text is jargon-heavy but sentences are moderate length, Gunning Fog scores much higher than Flesch-Kincaid.

Example 4: Long Sentences, Simple Words (Different Pattern)

Sample: “The company grew and expanded and hired more people and opened new offices and launched new products and entered new markets and became more profitable than ever before.”

Metrics:

• Flesch-Kincaid: Grade 5.2
• Gunning Fog: Grade 4.8

Result: Flesch-Kincaid slightly higher. Why? Long sentence (32 words) with simple words. Flesch-Kincaid penalizes sentence length more; Gunning Fog sees no complex words, so it scores lower.

Pattern: When text has long sentences but simple words, Flesch-Kincaid scores higher than Gunning Fog.

Example 5: Academic Writing (Large Divergence)

Sample: “Phenomenological epistemology necessitates comprehensive hermeneutical methodologies. Contextualized investigations of ontological presuppositions facilitate theoretical comprehension. Interdisciplinary synthesis requires rigorous methodological delineation.”

Metrics:

• Flesch-Kincaid: Grade 14.8
• Gunning Fog: Grade 18.5+

Result: Gunning Fog dramatically higher. This text is packed with 4+ syllable words (phenomenological, epistemology, hermeneutical, methodologies, contextualized, investigations, ontological, presuppositions, facilitate, theoretical, comprehension, interdisciplinary, synthesis, rigorous, methodological, delineation). Gunning Fog’s heavy weighting of complex words catches this; Flesch-Kincaid does too, but less aggressively.

6. When to Use Gunning Fog Index vs. Flesch-Kincaid

Use Gunning Fog Index If:

✅ You’re concerned about jargon and overly complex vocabulary

• Business writing where clarity is paramount
• Marketing copy where you want to avoid “corporate speak”
• Instructions (medical, technical) where clarity saves lives
• Content for non-specialists who shouldn’t need a dictionary

✅ You’re analyzing whether specialists are using unnecessarily complex language

• Academic writing that should be accessible to educated non-specialists
• Legal documents that should be understandable to average people

✅ You want to catch buzzwords and pretentious vocabulary

• Job postings that overuse jargon
• Marketing copy hiding behind complex words
• Academic writing that prioritizes sounding impressive over being clear

✅ You need a metric that targets vocabulary quality specifically

• Copyediting and proofreading
• Content marketing optimization
• Journalism

Use Flesch-Kincaid Grade Level If:

✅ You need a general-purpose readability metric

• Quick readability checks across diverse content types
• Comparing readability across many texts
• Software tool default (Microsoft Word, Google Docs use Flesch-Kincaid)

✅ You’re working in education and need to match materials to grade levels

• Selecting books for classrooms
• Assessing whether a textbook is appropriate for Grade 5 students
• Educational content evaluation

✅ You want a balanced view of readability

• Sentence length AND word complexity both matter
• You don’t want to over-emphasize vocabulary complexity
• General audiences and broad writing types

✅ You’re working with international or non-U.S. audiences

• Flesch-Kincaid is more universally applicable
• Gunning Fog is more U.S.-business-focused

✅ You want the “standard” metric that most people know

• Broader recognition in publishing, education, and software
• Easier to explain to non-specialists

7. Comparing the Formulas: Strengths & Limitations

Flesch-Kincaid Strengths

✅ Balanced perspective: Sentence length and word complexity both contribute equally

✅ Widely adopted: Default in Microsoft Word, Google Docs, most readability tools

✅ Validated: Decades of research backing its effectiveness

✅ Intuitive output: Grade level feels natural to educators

✅ Works across diverse writing types: Novels, articles, technical writing, business writing

✅ More conservative: Won’t over-penalize necessary technical terms

Flesch-Kincaid Limitations

❌ Can underestimate jargon-heavy text: If a text has necessary technical terms, Flesch-Kincaid might score lower than deserved

❌ Oversimplifies readability: Doesn’t distinguish between necessary and unnecessary complexity

❌ Syllable counting issues: Variable across different tools

Gunning Fog Strengths

✅ Targets jargon directly: Counts complex words, making it ideal for detecting unnecessary complexity

✅ Catches corporate/academic pretentiousness: Perfect for identifying “fog” language

✅ More aggressive on vocabulary: Punishes complex words more heavily

✅ Practical for business writing: Designed specifically for clarity in business contexts

✅ Good for copyediting: Helps identify words that could be simplified

Gunning Fog Limitations

❌ Oversensitive to necessary technical terms: A text on quantum physics will score very high (Grade 18+) even with simple explanations, because “quantum” is a 3-syllable word

❌ Doesn’t distinguish necessary from unnecessary complexity: A physics paper uses “photon” (necessary); a marketing email uses “optimize” (perhaps unnecessary). Both are 2 syllables, but Gunning Fog treats them the same

❌ Less widely adopted: Not default in major software; requires seeking out specific tools

❌ Can produce unrealistic scores: Can exceed Grade 18+ for specialized academic writing, making comparison difficult

❌ Ignores sentence structure complexity: Two sentences with the same words but different structures score identically

8. How to Interpret Disagreements Between the Two Formulas

When Flesch-Kincaid and Gunning Fog disagree significantly, the gap tells a story:

Pattern 1: Gunning Fog Much Higher (>2 grade levels)

Interpretation: Jargon and complex vocabulary are driving difficulty, not sentence structure.

Action: Simplify vocabulary. Replace 3+ syllable words with simpler alternatives (without sacrificing accuracy).

Example: Tech writing using “utilize,” “implement,” “infrastructure” consistently will score high on Gunning Fog, low-to-moderate on Flesch-Kincaid.

Pattern 2: Flesch-Kincaid Much Higher (>2 grade levels)

Interpretation: Long sentences are driving difficulty, not vocabulary complexity.

Action: Break long sentences into shorter ones. The vocabulary is already clear.

Example: Run-on sentences with simple words (“The manager walked to the office and talked to the team and discussed the project and made decisions…”) will score low on Gunning Fog, higher on Flesch-Kincaid.

Pattern 3: Both High, Convergent

Interpretation: Text is genuinely difficult across multiple dimensions (complex vocabulary AND long sentences).

Action: Comprehensive rewrite needed. Simplify vocabulary, shorten sentences, restructure arguments.

Pattern 4: Both Low, Convergent

Interpretation: Text is accessible and clear.

Action: You’re good. No changes needed.

9. Common Mistakes When Comparing These Formulas

Mistake 1: Thinking One is “Correct” and One is “Wrong”

Wrong: “Gunning Fog says Grade 16, so that’s the real score. Flesch-Kincaid is wrong.”

Right: Both are tools, both are right. They measure different things. Gunning Fog emphasizes vocabulary; Flesch-Kincaid is balanced.

Mistake 2: Using Only One Formula

Wrong: “I’ll just check Flesch-Kincaid and ignore Gunning Fog.”

Right: Check both. When they diverge, that divergence tells you what to fix.

Mistake 3: Averaging the Scores

Wrong: “Flesch-Kincaid says 8, Gunning Fog says 12, so the average is 10. That’s the score.”

Right: Don’t average. Each formula tells you something different. Use them together, not as a number to average.

Mistake 4: Oversimplifying to Lower Gunning Fog

Wrong: “Gunning Fog is high, so I’ll replace ‘analyze’ with ‘look’ everywhere.”

Right: Only simplify where accuracy allows. Some words with 3+ syllables are necessary.

Mistake 5: Assuming Higher is Worse

Wrong: “Gunning Fog scored 14, which is bad.”

Right: Grade 14 is appropriate for college-educated audiences and specialized topics. It’s not “bad” — it matches a specialized audience.

10. Further Resources & Tools

Related Articles on This Site

• What Is Readability & How to Measure It — foundational overview of all readability formulas
• Flesch Reading Ease Explained — deep dive into the Flesch Reading Ease formula
• Flesch-Kincaid Grade Level: What It Means — companion on the grade-level version of Flesch
• Understanding Reading Levels: From Elementary to Academic — what each grade level actually means in educational contexts

External Resources

• Gunning Fog Index: Official explanation from Gunning Fog Index Foundation — created by Robert Gunning himself
• Microsoft Office: Readability Statistics: Enable readability reporting in Word — Word reports both Flesch-Kincaid and Flesch Reading Ease
• Kincaid, J.P., et al. (1975): “Derivation of New Readability Formulas” — Original Navy research introducing Flesch-Kincaid Grade Level
• Gunning, R. (1952): “The Technique of Clear Writing” — Original work introducing Gunning Fog Index

Try the Tool

Want to check both Gunning Fog and Flesch-Kincaid on a Wikipedia article or any text? Use our interactive readability checker to:

• Paste any Wikipedia article URL
• See Gunning Fog Index AND Flesch-Kincaid Grade Level side-by-side
• See where they agree and where they diverge
• Also see Flesch Reading Ease and other formulas for complete context
• Get actionable guidance on what the gap between formulas tells you

Simply paste a URL or text, and you’ll get a full readability comparison showing all formulas and their implications.

11. Conclusion: Using Both Formulas Together

Gunning Fog Index and Flesch-Kincaid Grade Level are two of the most widely-used readability metrics. They both output a grade level (1–18+), but they weight linguistic factors differently.

Flesch-Kincaid balances sentence length and word complexity equally. Gunning Fog emphasizes vocabulary complexity (3+ syllable words) much more heavily.

This difference makes them useful together, not in competition:

1. Check both scores. If they’re similar, you understand the text’s difficulty clearly. If they diverge, the gap tells you what needs fixing.
2. Large gap (Gunning > Flesch): Your problem is vocabulary complexity. Simplify words.
3. Large gap (Flesch > Gunning): Your problem is sentence length. Break sentences into shorter ones.
4. Both high, convergent: Comprehensive rewrite needed.
5. Both low, convergent: You’re doing great.

Neither formula is “better.” They’re different tools for different purposes:

• Flesch-Kincaid: General readability metric, education-focused
• Gunning Fog: Vocabulary-focused, business/marketing-focused

Use them together to understand readability comprehensively. One score tells you how difficult text is; the gap between two scores tells you why it’s difficult and how to fix it.

Next Steps

Content creators: Run your next piece through both formulas. Note where they diverge. That gap is diagnostic feedback.

Educators: Use Flesch-Kincaid for grade-level matching. Use Gunning Fog to detect unnecessarily complex vocabulary in materials.

Copyeditors: Use Gunning Fog to identify jargon and overly complex words. Use Flesch-Kincaid for a general readability baseline.

Researchers: Check both when analyzing text difficulty. Divergence reveals structure about the text itself.

Try our tool to check both formulas on any Wikipedia article or text. Understanding how they differ will make you a more sophisticated reader of readability metrics — and a better writer.