| Signal | Claude Opus 4.6 (Fast) | Delta | GPT-5.4 Pro |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 87 | -3 | |
Pricing | 5 | -- | |
Context window size | 95 | 0 | |
Recency | 100 | -- | |
Output Capacity | 85 | -- | |
| Overall Result | 0 wins | of 6 | 2 wins |
Score History
90.4
current score
GPT-5.4 Pro
right now
91.9
current score
Anthropic
OpenAI
Claude Opus 4.6 (Fast) saves you $1500.00/month
That's $18000.00/year compared to GPT-5.4 Pro at your current usage level of 100K calls/month.
| Metric | Claude Opus 4.6 (Fast) | GPT-5.4 Pro | Winner |
|---|---|---|---|
| Overall Score | 90 | 92 | GPT-5.4 Pro |
| Rank | #6 | #1 | GPT-5.4 Pro |
| Quality Rank | #6 | #1 | GPT-5.4 Pro |
| Adoption Rank | #6 | #1 | GPT-5.4 Pro |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 1050K | GPT-5.4 Pro |
| Pricing | $30.00/$150.00/M | $30.00/$180.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Opus 4.6 (Fast) |
| Benchmarks | 87 | 90 | GPT-5.4 Pro |
| Pricing | 5 | 5 | Claude Opus 4.6 (Fast) |
| Context window size | 95 | 96 | GPT-5.4 Pro |
| Recency | 100 | 100 | Claude Opus 4.6 (Fast) |
| Output Capacity | 85 | 85 | Claude Opus 4.6 (Fast) |
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 90/100 (rank #6), placing it in the top 98% of all 290 models tracked.
Scores 92/100 (rank #1), placing it in the top 100% of all 290 models tracked.
With only a 2-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Claude Opus 4.6 (Fast) offers 14% better value per quality point. At 1M tokens/day, you'd spend $2700.00/month with Claude Opus 4.6 (Fast) vs $3150.00/month with GPT-5.4 Pro - a $450.00 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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Claude Opus 4.6 (Fast) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1050K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($150.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (92/100) 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 Opus 4.6 (Fast) and GPT-5.4 Pro are extremely close in overall performance (only 1.5 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Claude Opus 4.6 (Fast)
Marginally better benchmark scores; both are excellent
Best for Cost
Claude Opus 4.6 (Fast)
14% lower pricing; better value at scale
Best for Reliability
Claude Opus 4.6 (Fast)
Higher uptime and faster response speeds
Best for Prototyping
Claude Opus 4.6 (Fast)
Stronger community support and better developer experience
Best for Production
Claude Opus 4.6 (Fast)
Wider enterprise adoption and proven at scale
by Anthropic
| Capability | Claude Opus 4.6 (Fast) | GPT-5.4 Pro |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Anthropic
OpenAI
Claude Opus 4.6 (Fast) saves you $36.00/month
That's 13% cheaper than GPT-5.4 Pro 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 Opus 4.6 (Fast) | GPT-5.4 Pro |
|---|---|---|
| Context Window | 1M | 1.1M |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Apr 7, 2026 | Mar 5, 2026 |
Claude Opus 4.6 (Fast) achieves a 62/100 coding score versus GPT-5.4 Pro's 61/100, suggesting Anthropic's model architecture extracts more value per token for code generation tasks. The 1.0M token context window appears sufficient for most coding workflows, where the quality of reasoning matters more than raw capacity, especially given the identical 128K max output tokens for both models.
At scale, Claude's 20% lower output pricing ($150/M vs $180/M) translates to $30,000 savings per billion tokens generated, while both models share the same $30/M input cost. For coding assistants generating extensive documentation or refactoring suggestions, this cost difference compounds quickly, and the 1.0M context window already exceeds typical repository sizes by 10-20x.
Both models offer the same capability set (Vision, Function Calling, Streaming, JSON Mode, Reasoning, Web Search) but require different SDK integrations and API paradigms. Anthropic's Claude typically offers more granular safety controls while OpenAI provides broader third-party tool integration, making migration between these closely-scored models (62 vs 61) more about ecosystem commitment than raw performance.
GPT-5.4 Pro's native file handling (text+image+file->text) eliminates preprocessing overhead for PDFs, CSVs, and structured documents, while Claude requires conversion to text or images first. However, with only a 1-point score difference (61 vs 62) and identical vision capabilities, Claude's superior benchmark performance suggests its core reasoning compensates for the extra preprocessing step.
The minimal performance delta (62/100 vs 61/100) and close rankings (#9 vs #11 out of 317) suggest switching costs likely outweigh benefits for established OpenAI workflows. However, new projects processing over 500M output tokens annually would save $15,000+ with Claude while gaining marginally better coding performance, making it the rational choice absent existing infrastructure constraints.