Claude Opus 4.7 vs GPT-5.4

Across our 12-test internal benchmark suite, GPT-5.4 wins 3 tests, Claude Opus 4.7 wins 2, and the two tie on 7.

Where Claude Opus 4.7 wins:

Tool calling (5 vs 4): Opus 4.7 scores 5/5 on function selection, argument accuracy, and sequencing — tied for 1st among 55 models with 17 others. GPT-5.4 scores 4/5, ranking 19th of 55. For agentic workflows that rely on precise, multi-step function calls, this gap is meaningful in practice.

Creative problem solving (5 vs 4): Opus 4.7 scores 5/5 here — tied for 1st among 55 models with 8 others — while GPT-5.4 scores 4/5, ranking 10th. Our creative problem solving test evaluates non-obvious, specific, and feasible ideas, so this matters most for brainstorming, product ideation, and open-ended analysis.

Where GPT-5.4 wins:

Structured output (5 vs 4): GPT-5.4 scores 5/5 on JSON schema compliance and format adherence — tied for 1st with 24 others. Opus 4.7 scores 4/5, ranking 26th of 55. For developers building pipelines that parse model responses programmatically, this is a practical reliability difference.

Safety calibration (5 vs 3): This is the widest gap in the comparison. GPT-5.4 scores 5/5, tied for 1st among 56 models with 4 others. Opus 4.7 scores 3/5, ranking 10th of 56. Our safety calibration test measures whether a model refuses genuinely harmful requests while still permitting legitimate ones — striking the right balance rather than being either permissive or overly restrictive. GPT-5.4 is meaningfully better calibrated in our testing.

Multilingual (5 vs 4): GPT-5.4 scores 5/5, tied for 1st among 56 models with 34 others. Opus 4.7 scores 4/5, ranking 36th. For applications serving non-English speakers, GPT-5.4 produces more consistently high-quality output across languages in our tests.

The 7 ties: Both models score identically on strategic analysis (5/5), constrained rewriting (4/4), faithfulness (5/5), classification (3/3), long context (5/5), persona consistency (5/5), and agentic planning (5/5). The ties on faithfulness, long context, and agentic planning are all at the top of the distribution, meaning both models are among the best in our suite on those dimensions.

External benchmarks (GPT-5.4 only): GPT-5.4 has Epoch AI benchmark data available. It scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested), placing it among the strongest coding models by that external measure — above the 75th percentile for tested models (75.25%). On AIME 2025, it scores 95.3% (rank 3 of 23 models), comfortably above the 75th percentile (90%), confirming strong competition-level math performance. No equivalent external benchmark scores are available for Claude Opus 4.7 in our data.

Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. GPT-5.4 costs $2.50 per million input tokens and $15.00 per million output tokens — half the input price and 40% less on output.

At 1 million output tokens per month, that gap is $10. Noticeable, but not a deciding factor for most teams. At 10 million output tokens, the difference is $100/month. At 100 million output tokens — the scale of a high-traffic API product — you're paying $1,000 more per month for Opus 4.7, or $12,000 more per year.

For developers, the output cost is the number that matters most, since responses are typically longer than prompts. At any meaningful scale, GPT-5.4's pricing is a genuine advantage. Consumer users choosing a chat subscription should note that both models are proprietary; pricing at that tier depends on the platform, not raw API rates.

The cost gap becomes a real decision point somewhere around 5–10 million output tokens per month. Below that, choose on capability. Above it, GPT-5.4's lower cost is a structural advantage unless Opus 4.7's specific wins matter for your use case.

Choose Claude Opus 4.7 if:

• You're building agentic systems where tool-calling accuracy directly affects reliability — Opus 4.7 scores 5/5 vs GPT-5.4's 4/5 in our tests
• Your product depends heavily on creative ideation or open-ended problem solving (5 vs 4 in our testing)
• You're working at output volumes below ~10 million tokens/month where the $10/million output price premium is manageable
• You only need text and image inputs — Opus 4.7 supports text and image modalities

Choose GPT-5.4 if:

• You need reliable structured output for API pipelines or downstream parsing — GPT-5.4 scores 5/5 vs Opus 4.7's 4/5
• Safety calibration matters for your deployment: GPT-5.4 scores 5/5 vs Opus 4.7's 3/5, a significant gap in our testing
• You serve multilingual users — GPT-5.4 scores 5/5 vs 4/5 and ranks 1st of 56 models
• You process files in addition to text and images — GPT-5.4's modality support includes file inputs
• You're running at scale (10M+ output tokens/month) where the $10/million output cost difference adds up to $100,000+ annually
• You want external benchmark validation: GPT-5.4 ranks 2nd of 12 on SWE-bench Verified at 76.9% and 3rd of 23 on AIME 2025 at 95.3% (Epoch AI)