Claude Opus 4.7 vs GPT-5.5
| Signal | Claude Opus 4.7 | Delta | GPT-5.5 |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 93 | +1 | |
Pricing | 75 | +5 | |
Context window size | 86 | 0 | |
Recency | 100 | -- | |
Output Capacity | 85 | -- | |
| Overall Result | 2 wins | of 6 | 1 wins |
Score History
94.7
current score
Claude Opus 4.7
right now
92.2
current score
Claude Opus 4.7
Anthropic
GPT-5.5
OpenAI
Claude Opus 4.7 saves you $250.00/month
That's $3000.00/year compared to GPT-5.5 at your current usage level of 100K calls/month.
| Metric | Claude Opus 4.7 | GPT-5.5 | Winner |
|---|---|---|---|
| Overall Score | 95 | 92 | Claude Opus 4.7 |
| Rank | #5 | #7 | Claude Opus 4.7 |
| Quality Rank | #5 | #7 | Claude Opus 4.7 |
| Adoption Rank | #5 | #7 | Claude Opus 4.7 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 1050K | GPT-5.5 |
| Pricing | $5.00/$25.00/M | $5.00/$30.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Opus 4.7 |
| Benchmarks | 93 | 93 | Claude Opus 4.7 |
| Pricing | 75 | 70 | Claude Opus 4.7 |
| Context window size | 86 | 86 | GPT-5.5 |
| Recency | 100 | 100 | Claude Opus 4.7 |
| Output Capacity | 85 | 85 | 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 #5), placing it in the top 99% of all 290 models tracked.
Scores 92/100 (rank #7), placing it in the top 98% 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-5.5 when you need:
- Step-by-step reasoning and chain-of-thought problem solving
Claude Opus 4.7 offers 14% better value per quality point. At 1M tokens/day, you'd spend $450.00/month with Claude Opus 4.7 vs $525.00/month with GPT-5.5 - a $75.00 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
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. Claude Opus 4.7 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 ($25.00/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 and GPT-5.5 are extremely close in overall performance (only 2.5 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
By Use Case
Best for Quality
Claude Opus 4.7
Marginally better benchmark scores; both are excellent
Best for Cost
Claude Opus 4.7
14% 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 Cost - 14% lower pricing; better value at scale
- 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-5.5 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Claude Opus 4.7
Anthropic
GPT-5.5
OpenAI
Claude Opus 4.7 saves you $6.00/month
That's 13% cheaper than GPT-5.5 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-5.5 |
|---|---|---|
| Context Window | 1M | 1.1M |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Apr 16, 2026 | Apr 24, 2026 |
The ranking likely reflects GPT-5.5's file handling capability (text+image+file->text vs Claude's text+image->text) which matters significantly for real-world coding workflows. Additionally, GPT-5.5's 10% larger context window (1.1M vs 1.0M tokens) provides meaningful advantages for large codebases and multi-file projects.
For high-volume code generation tasks, Claude Opus 4.7's $5/M cheaper output pricing translates to substantial savings - saving $5,000 per billion tokens generated. However, GPT-5.5's file input modality eliminates preprocessing steps for documentation, PDFs, and non-text assets, potentially offsetting the 1.2x price ratio through reduced pipeline complexity.
GPT-5.5's 100K additional context tokens (1.1M vs 1.0M) provide a 10% buffer for complex refactoring operations that need to analyze entire microservices or multiple related files. Claude Opus 4.7's lower output cost ($25/M vs $30/M) makes it more economical for iterative refactoring workflows where you're generating multiple variations.
The migration requires rearchitecting any workflows that currently process binary files, PDFs, or spreadsheets outside the LLM pipeline, as GPT-5.5 handles these natively while Claude requires text extraction. Both models share identical capabilities otherwise (Vision, Function Calling, JSON Mode, etc.), making the migration primarily about input pipeline changes rather than output format adjustments.
The identical 66/100 scores suggest performance parity on standard benchmarks, while Claude's 17% lower output cost ($25/M vs $30/M) makes it superior for high-volume tasks like test generation or documentation. The 7-position rank difference appears driven by GPT-5.5's file handling rather than core coding capability, as both share the same 128K output limit and all major features.