| Signal | Claude Sonnet 4.6 | Delta | GPT-5.4 Mini |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 82 | -8 | |
Pricing | 85 | -10 | |
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 Mini saves you $750.00/month
That's $9000.00/year compared to Claude Sonnet 4.6 at your current usage level of 100K calls/month.
| Metric | Claude Sonnet 4.6 | GPT-5.4 Mini | Winner |
|---|---|---|---|
| Overall Score | 85 | 79 | Claude Sonnet 4.6 |
| Rank | #25 | #43 | Claude Sonnet 4.6 |
| Quality Rank | #25 | #43 | Claude Sonnet 4.6 |
| Adoption Rank | #25 | #43 | Claude Sonnet 4.6 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 400K | Claude Sonnet 4.6 |
| Pricing | $3.00/$15.00/M | $0.75/$4.50/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Sonnet 4.6 |
| Benchmarks | 82 | 90 | GPT-5.4 Mini |
| Pricing | 85 | 96 | GPT-5.4 Mini |
| 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 #43), 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 Mini offers 71% better value per quality point. At 1M tokens/day, you'd spend $78.75/month with GPT-5.4 Mini vs $270.00/month with Claude Sonnet 4.6 - a $191.25 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 Mini 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 ($4.50/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 Mini 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 Mini
71% 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 Mini |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Anthropic
OpenAI
GPT-5.4 Mini saves you $16.65/month
That's 71% 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 Mini |
|---|---|---|
| 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 output pricing reflects Claude Sonnet 4.6's 2.5x larger context window (1M vs 400K tokens) and its #6 ranking among 316 coding models, placing it in the top 2% versus GPT-5.4 Mini's #15 position. For production coding assistants handling large codebases or multi-file analysis, the 1M token context often justifies the premium as it reduces the need for context window management and chunking strategies.
The $10.50/M output price differential means that at just 10M tokens per month, you're paying $105 more for Claude Sonnet 4.6 - but at 100M tokens monthly, that gap becomes $1,050. For context, 100M output tokens roughly equals 75,000 pages of generated code or documentation, making GPT-5.4 Mini the pragmatic choice for high-volume code generation tasks where the 61/100 performance is sufficient.
The 66 vs 61 score gap likely manifests in complex reasoning tasks like architectural refactoring or debugging race conditions across multiple files, where Claude Sonnet 4.6's 1M context window can hold entire microservice codebases simultaneously. GPT-5.4 Mini's 400K window forces more frequent context resets, potentially missing subtle cross-file dependencies that contribute to its 9-position lower ranking.
For a typical code review workflow processing 50K tokens of input (roughly 40 files) and generating 5K tokens of analysis, Claude Sonnet 4.6 costs $0.225 versus GPT-5.4 Mini's $0.060 per review. At 1,000 reviews monthly, that's a $165 difference - negligible for teams prioritizing the 8% better accuracy but meaningful for startups running automated PR analysis on every commit.
Beyond the 3.3x output cost increase, GPT-5.4 Mini's file handling capability (absent in Claude Sonnet 4.6) enables direct processing of binary formats like compiled artifacts or encoded test data. Teams with established pipelines around this modality face non-trivial migration costs that may outweigh the 5-point performance gain, especially given both models share identical function calling and JSON mode implementations.