Claude Opus 4.7 vs GPT-5.4
| Signal | Claude Opus 4.7 | Delta | GPT-5.4 |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 93 | +3 | |
Pricing | 75 | -10 | |
Context window size | 95 | 0 | |
Recency | 100 | +4 | |
Output Capacity | 82 | -- | |
| Overall Result | 2 wins | of 6 | 2 wins |
Score History
95.1
current score
Claude Opus 4.7
right now
91.9
current score
Claude Opus 4.7
Anthropic
GPT-5.4
OpenAI
GPT-5.4 saves you $750.00/month
That's $9000.00/year compared to Claude Opus 4.7 at your current usage level of 100K calls/month.
| Metric | Claude Opus 4.7 | GPT-5.4 | Winner |
|---|---|---|---|
| Overall Score | 95 | 92 | Claude Opus 4.7 |
| Rank | #8 | #20 | Claude Opus 4.7 |
| Quality Rank | #8 | #20 | Claude Opus 4.7 |
| Adoption Rank | #8 | #20 | Claude Opus 4.7 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 1050K | GPT-5.4 |
| Pricing | $5.00/$25.00/M | $2.50/$15.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Opus 4.7 |
| Benchmarks | 93 | 90 | Claude Opus 4.7 |
| Pricing | 75 | 85 | GPT-5.4 |
| Context window size | 95 | 96 | GPT-5.4 |
| Recency | 100 | 96 | Claude Opus 4.7 |
| Output Capacity | 82 | 82 | Claude Opus 4.7 |
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 95/100 (rank #8), placing it in the top 98% of all 290 models tracked.
Scores 92/100 (rank #20), placing it in the top 93% of all 290 models tracked.
With only a 3-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.
Choose Claude Opus 4.7 when you need:
- Step-by-step reasoning and chain-of-thought problem solving
Choose GPT-5.4 when you need:
- High-volume production workloads where API costs must be minimized
- Step-by-step reasoning and chain-of-thought problem solving
GPT-5.4 offers 42% better value per quality point. At 1M tokens/day, you'd spend $262.50/month with GPT-5.4 vs $450.00/month with Claude Opus 4.7 - a $187.50 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 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 ($15.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (95/100) 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 4.7 has a moderate advantage with a 3.1999999999999886-point lead in composite score. It wins on more signal dimensions, but GPT-5.4 has specific strengths that could make it the better choice for certain workflows.
By Use Case
Best for Quality
Claude Opus 4.7
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-5.4
42% lower pricing; better value at scale
Best for Reliability
Claude Opus 4.7
Higher uptime and faster response speeds
Best for Prototyping
Claude Opus 4.7
Stronger community support and better developer experience
Best for Production
Claude Opus 4.7
Wider enterprise adoption and proven at scale
by Anthropic
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Claude Opus 4.7 | GPT-5.4 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Claude Opus 4.7
Anthropic
GPT-5.4
OpenAI
GPT-5.4 saves you $16.50/month
That's 42% cheaper than Claude Opus 4.7 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 4.7 | GPT-5.4 |
|---|---|---|
| Context Window | 1M | 1.1M |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Apr 16, 2026 | Mar 5, 2026 |
For high-volume production workloads, GPT-5.4's 40% lower output pricing adds up quickly - saving $10,000 per billion tokens while delivering slightly better performance (67 vs 66). However, Claude Opus 4.7's 2x cheaper input pricing ($5/M vs $2.5/M) makes it more cost-effective for workflows with high input-to-output ratios like code review or documentation analysis.
The 2-position rank difference reflects that the #6 model likely scores 66 or 67, creating a tight cluster where GPT-5.4's 1.1M token context window (10% larger than Claude's 1.0M) and 40% lower output pricing provide meaningful differentiation. In the top tier of coding models, these marginal advantages compound when processing large codebases or generating extensive refactorings.
Claude Opus 4.7's 50% cheaper input pricing ($5/M vs $2.5/M for GPT-5.4) makes it ideal for rapid prototyping where you're constantly feeding in new prompts and context. A typical prototyping session with 100K input tokens and 20K output tokens costs $1.00 on Claude vs $0.55 on GPT-5.4, but Claude's advantage grows as the input-to-output ratio increases beyond 5:1.
GPT-5.4's 1.1M context window allows processing 10% more code context than Claude's 1.0M, crucial for understanding entire monorepos or analyzing cross-file dependencies. While both cap output at 128K tokens, GPT-5.4's file handling modality (absent in Claude) enables direct processing of tar archives or zip files without base64 encoding overhead.
The 1.7x output cost multiplier translates to $16,700 extra per billion output tokens compared to GPT-5.4, which is hard to justify for a 1-point lower score (66 vs 67). However, teams deeply integrated with Anthropic's safety features and prompt engineering patterns might find migration costs exceed the pricing delta, especially if their workflows leverage Claude's 2x cheaper input pricing on high-context operations.