| Signal | Claude Sonnet 4.6 | Delta | GPT-5.4 Nano |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 82 | -8 | |
Pricing | 85 | -14 | |
Context window size | 95 | +6 | |
Recency | 100 | -- | |
Output Capacity | 85 | -- | |
| Overall Result | 1 wins | of 6 | 2 wins |
Score History
85.2
current score
Claude Sonnet 4.6
right now
79.3
current score
Anthropic
OpenAI
GPT-5.4 Nano saves you $967.50/month
That's $11610.00/year compared to Claude Sonnet 4.6 at your current usage level of 100K calls/month.
| Metric | Claude Sonnet 4.6 | GPT-5.4 Nano | Winner |
|---|---|---|---|
| Overall Score | 85 | 79 | Claude Sonnet 4.6 |
| Rank | #25 | #42 | Claude Sonnet 4.6 |
| Quality Rank | #25 | #42 | Claude Sonnet 4.6 |
| Adoption Rank | #25 | #42 | Claude Sonnet 4.6 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 400K | Claude Sonnet 4.6 |
| Pricing | $3.00/$15.00/M | $0.20/$1.25/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Sonnet 4.6 |
| Benchmarks | 82 | 90 | GPT-5.4 Nano |
| Pricing | 85 | 99 | GPT-5.4 Nano |
| Context window size | 95 | 89 | Claude Sonnet 4.6 |
| Recency | 100 | 100 | Claude Sonnet 4.6 |
| Output Capacity | 85 | 85 | Claude Sonnet 4.6 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 85/100 (rank #25), placing it in the top 92% of all 290 models tracked.
Scores 79/100 (rank #42), placing it in the top 86% of all 290 models tracked.
Claude Sonnet 4.6 has a 6-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
GPT-5.4 Nano offers 92% better value per quality point. At 1M tokens/day, you'd spend $21.75/month with GPT-5.4 Nano vs $270.00/month with Claude Sonnet 4.6 - a $248.25 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. GPT-5.4 Nano also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.25/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (85/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Claude Sonnet 4.6 has a moderate advantage with a 5.900000000000006-point lead in composite score. It wins on more signal dimensions, but GPT-5.4 Nano has specific strengths that could make it the better choice for certain workflows.
Best for Quality
Claude Sonnet 4.6
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-5.4 Nano
92% lower pricing; better value at scale
Best for Reliability
Claude Sonnet 4.6
Higher uptime and faster response speeds
Best for Prototyping
Claude Sonnet 4.6
Stronger community support and better developer experience
Best for Production
Claude Sonnet 4.6
Wider enterprise adoption and proven at scale
by Anthropic
| Capability | Claude Sonnet 4.6 | GPT-5.4 Nano |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Anthropic
OpenAI
GPT-5.4 Nano saves you $21.54/month
That's 92% cheaper than Claude Sonnet 4.6 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Claude Sonnet 4.6 | GPT-5.4 Nano |
|---|---|---|
| Context Window | 1M | 400K |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Feb 17, 2026 | Mar 17, 2026 |
The $15/M vs $1.25/M output pricing gap reflects Anthropic's positioning of Sonnet 4.6 for high-stakes code generation where the 66/100 vs 61/100 performance difference translates to measurably better code quality. For production codebases where a single bug can cost thousands, the 12x price premium becomes negligible compared to the reliability gains at rank #6 vs #13.
Daily costs would be $54 for Sonnet 4.6 ($24 input + $30 output) versus $4.10 for GPT-5.4 Nano ($1.60 input + $2.50 output), making Sonnet 13.2x more expensive. At this scale, the $1,500/month difference only makes sense for revenue-critical applications where Sonnet's superior coding performance directly impacts customer satisfaction or development velocity.
GPT-5.4 Nano's file handling makes it superior for processing PDFs, CSVs, or documents at $0.20/M input despite scoring 61/100 vs Sonnet's 66/100. However, Sonnet 4.6's 2.5x larger context window (1M vs 400K tokens) means it can handle entire codebases in memory, making it worth the $3/M input cost for complex refactoring tasks that would require multiple passes with Nano.
Claude Sonnet 4.6's 1M token context can fit roughly 250K lines of code versus GPT-5.4 Nano's 100K lines, enabling whole-repository analysis that likely contributes to its #6 vs #13 ranking. This 2.5x advantage becomes critical for large-scale refactoring or cross-file dependency analysis, where Nano would need multiple costly re-prompting cycles to achieve similar results.
The migration from 61/100 to 66/100 performance is best leveraged by exploiting Sonnet's 1M context window for comprehensive test suite generation and multi-file refactoring that Nano's 400K limit constrains. However, teams should batch operations carefully since the 12x output cost multiplier ($15/M vs $1.25/M) can quickly erode ROI on verbose tasks like documentation generation where the quality delta is minimal.