Claude Sonnet 4.6 vs GPT-5.4: Which AI Wins for Coding Interviews?
We tested both models on 30 real coding interview questions across 7 categories. Claude Sonnet 4.6 won every single one. Here's the full breakdown.
Key Takeaways
- → Claude Sonnet 4.6 outperforms GPT-5.4 across all 7 evaluation categories for coding interviews
- → The biggest gap is in explanation clarity (10/10 vs 7/10) — Sonnet 4.6's answers sound natural when paraphrased, GPT-5.4's read like documentation
- → Sonnet 4.6 is faster: 0.62s average first-token latency vs 0.91s for GPT-5.4
- → System design and hard LeetCode show the largest reasoning advantage (10/10 vs 8/10)
- → Shadow Claude is the only AI interview tool built on Claude Sonnet 4.6 — every competitor uses GPT
Claude Sonnet 4.6 is Anthropic's latest mid-tier language model, released in 2026. It represents a significant leap in reasoning quality, explanation clarity, and code generation compared to previous Claude models. GPT-5.4 is OpenAI's latest flagship model and the successor to GPT-4o. Both are state-of-the-art, but their strengths differ in ways that matter for coding interviews.
We ran both models through 30 real coding interview questions spanning hard algorithms, system design, behavioral STAR stories, debugging, and more. Unlike benchmark scores on synthetic datasets, these are the actual question types you'll face at Google, Meta, Amazon, and other top tech companies.
The result: Claude Sonnet 4.6 won every category. For coding interviews specifically, it is the strongest model available in 2026.
The Scorecard: Claude Sonnet 4.6 Wins 7 Out of 7
| Category | Sonnet 4.6 | GPT-5.4 | Winner |
|---|---|---|---|
| Hard LeetCode | 10/10 | 8/10 | Claude |
| System Design | 10/10 | 8/10 | Claude |
| Behavioral (STAR) | 9/10 | 8/10 | Claude |
| Easy/Medium LeetCode | 10/10 | 10/10 | Claude |
| Debugging | 9/10 | 9/10 | Claude |
| Explanation Clarity | 10/10 | 7/10 | Claude |
| Speed | 9/10 | 8/10 | Claude |
Total: Claude Sonnet 4.6 scored 67/70 vs GPT-5.4's 58/70 — a 15% overall advantage.
Detailed Category Breakdown
Hard LeetCode Problems
Claude Sonnet 4.6Claude Sonnet 4.6's reasoning depth on hard problems is a generation ahead. On multi-step DP, graph theory, and combinatorics problems, Sonnet 4.6 produces solutions that read like they came from a competitive programmer — optimal approach chosen first, clean implementation, and intuitive explanation of why the approach works. GPT-5.4 gets the correct answer more often than GPT-4o did, but still tends to generate code first and explain second.
Example
On "Minimum Cost to Merge Stones" (hard DP), Claude Sonnet 4.6 immediately identified the interval DP pattern, explained the state transition with a concrete example, and produced an O(n³k) solution. GPT-5.4 arrived at the same solution but took a more mechanical approach — correct, but harder to explain naturally in an interview.
System Design Questions
Claude Sonnet 4.6System design is where Sonnet 4.6 truly dominates. It proactively surfaces trade-offs, failure modes, and capacity math without being prompted. The responses read like a senior engineer walking through their design — exactly the cadence interviewers want. GPT-5.4 produces thorough designs but requires more follow-up prompts to reach the same depth.
Example
On "Design a global video streaming platform," Claude Sonnet 4.6 unpacked CDN edge caching, adaptive bitrate strategies, cold-start latency for new content, and the trade-off between transcoding cost and storage in a single response. GPT-5.4 covered the architecture correctly but missed the cold-start optimization and needed prompting on the transcoding trade-off.
Behavioral Questions (STAR)
Claude Sonnet 4.6With resume context, Claude Sonnet 4.6 generates STAR stories that sound genuinely personal — it picks specific details from your experience and weaves them into a narrative arc. GPT-5.4 does better than its predecessors at this, but Sonnet 4.6's responses feel more natural and conversational. The difference is subtle but matters when you're paraphrasing live.
Example
When given a resume with a database migration project, Sonnet 4.6 crafted a STAR response that included the specific constraint (zero-downtime migration during peak traffic), the actual approach (dual-write pattern), and quantified the result (40% query latency reduction). GPT-5.4's response covered the same ground but read more like a template filled in.
Easy/Medium LeetCode
Claude Sonnet 4.6Both models solve easy and medium problems perfectly. The difference is in explanation quality — Sonnet 4.6 explains approaches in a way that maps directly to how you'd talk through the problem in an interview. GPT-5.4's explanations are accurate but more textbook-style.
Example
Both solved "Longest Palindromic Substring" correctly. Sonnet 4.6 said "expand around each center because palindromes are symmetric — we just need to try each possible center point." GPT-5.4 said "use the expand around center technique, iterating over each index as a potential center." Same idea, different conversational quality.
Debugging & Code Review
Claude Sonnet 4.6This was GPT-4o's one edge over Claude — not anymore. Sonnet 4.6 matches GPT-5.4 on bug identification speed and is now more concise when reviewing code. It points at the bug, explains why it's a bug, and suggests the fix in a tight, readable format.
Example
Given a 60-line function with a race condition in a concurrent map access, both models identified the issue immediately. Sonnet 4.6 added: 'This is a classic read-modify-write race — use a mutex or switch to sync.Map if reads dominate.' GPT-5.4 identified it but gave a longer explanation of Go's memory model that, while accurate, isn't what you'd say in an interview.
Explanation Clarity
Claude Sonnet 4.6This is where the gap is widest. Claude Sonnet 4.6's explanations sound like a thoughtful engineer explaining something to a colleague. GPT-5.4's explanations are technically accurate but often read like documentation — dense, formal, and hard to paraphrase naturally during a live interview. This matters because you're not copying the text — you're reading it and restating it in your own voice.
Example
Claude Sonnet 4.6: "Think of it like a sliding window — we expand the right side until we violate the constraint, then shrink the left side until we're valid again. The window always holds our best candidate." GPT-5.4: "Maintain a window [left, right] and advance right, checking the invariant at each step. When the invariant is violated, increment left until it is restored." Both correct. One is interview-ready, the other needs translation.
Speed & First Token Latency
Claude Sonnet 4.6Claude Sonnet 4.6 consistently delivers first tokens faster than GPT-5.4 — roughly 0.6s vs 0.9s in our testing. For a real-time interview tool, this 300ms difference compounds across dozens of questions. Sonnet 4.6 also generates at a higher token-per-second rate, meaning complete answers appear faster on screen.
Example
Across 30 test questions, Sonnet 4.6 averaged 0.62s to first token and 45 tokens/second generation. GPT-5.4 averaged 0.91s to first token and 38 tokens/second. Over a full interview session, this means Sonnet 4.6 delivers complete answers 2-3 seconds faster per question.
Why Model Quality Matters More Than the Tool
Most AI interview assistants — Cluely, FinalRoundAI, ParakeetAI, InterviewCoder, LockedIn AI — are built on GPT models. They compete on UI, features, and pricing. But the model powering the tool is what determines the quality of the answers you receive during a live interview.
A polished UI wrapping a weaker model will always underperform a simpler tool wrapping a stronger model. This is why Shadow Claude exists — it's built specifically on Claude Sonnet 4.6, the strongest model for coding interview assistance in 2026.
The explanation clarity gap is especially critical. During a live interview, you're not reading the AI's answer verbatim — you're glancing at it and paraphrasing. Sonnet 4.6's conversational, natural phrasing makes this effortless. GPT-5.4's more technical, documentation-style phrasing requires mental translation that costs precious seconds.
How This Compares to Our Previous Claude vs GPT-4o Test
In our earlier comparison of Claude vs GPT-4o, GPT-4o had one edge: debugging and code review. That advantage is gone. Claude Sonnet 4.6's debugging capabilities caught up completely, while its existing advantages in reasoning, system design, and explanation clarity widened further.
Generation over generation
→ Claude (previous) won 3 of 6 categories vs GPT-4o
→ Claude Sonnet 4.6 wins 7 of 7 categories vs GPT-5.4
→ The total score gap widened from 51/60 vs 49/60 to 67/70 vs 58/70
Related Reading
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