Claude Opus 4.7 vs GPT-4o-mini

Our 12-test benchmark suite gives a clear picture: Claude Opus 4.7 dominates on reasoning-heavy and agentic tasks, while GPT-4o-mini holds its own in a narrow band of classification and safety work.

Where Opus 4.7 wins:

• Strategic analysis: Opus 4.7 scores 5/5 (tied for 1st among 55 tested models) versus GPT-4o-mini's 2/5 (rank 45 of 55). This is the widest gap in the suite — nuanced tradeoff reasoning with real numbers is where GPT-4o-mini visibly struggles.
• Faithfulness: Opus 4.7 scores 5/5 (tied for 1st among 56 tested) versus GPT-4o-mini's 3/5 (rank 53 of 56 — near the bottom). In our testing, GPT-4o-mini is among the weakest models at sticking to source material without hallucinating, which is a meaningful liability for summarization, document Q&A, and RAG pipelines.
• Agentic planning: Opus 4.7 scores 5/5 (tied for 1st among 55 tested) versus GPT-4o-mini's 3/5 (rank 43 of 55). Goal decomposition and failure recovery matter significantly for multi-step tool use and autonomous agents.
• Creative problem solving: Opus 4.7 scores 5/5 (tied for 1st among 55 tested) versus GPT-4o-mini's 2/5 (rank 48 of 55). Generating non-obvious, specific, feasible ideas is a substantial differentiator.
• Tool calling: Opus 4.7 scores 5/5 versus GPT-4o-mini's 4/5. Both are competitive here, but Opus 4.7 is tied for 1st among 55 models; GPT-4o-mini ranks 19th.
• Persona consistency: Opus 4.7 scores 5/5 (tied for 1st among 55) versus GPT-4o-mini's 4/5 (rank 39 of 55).
• Long context: Opus 4.7 scores 5/5 (tied for 1st among 56 tested) versus GPT-4o-mini's 4/5 (rank 39 of 56). Opus 4.7 also carries a 1,000,000-token context window versus GPT-4o-mini's 128,000 tokens — a hard technical ceiling for document-heavy work.
• Constrained rewriting: Opus 4.7 scores 4/5 (rank 6 of 55) versus GPT-4o-mini's 3/5 (rank 32 of 55).

Where GPT-4o-mini wins:

• Safety calibration: GPT-4o-mini scores 4/5 (rank 6 of 56) versus Opus 4.7's 3/5 (rank 10 of 56). In our testing, GPT-4o-mini is more reliably calibrated between refusing harmful requests and permitting legitimate ones. Opus 4.7's score here is still above the field median (p50 = 2/5), but GPT-4o-mini is measurably better.
• Classification: GPT-4o-mini scores 4/5 (tied for 1st among 54 tested) versus Opus 4.7's 3/5 (rank 31 of 54). For categorization and routing tasks, GPT-4o-mini is among the top performers.

Ties:

• Structured output and multilingual: Both models score 4/5 on each, sharing rank 26 and rank 36 respectively among tested models.

External benchmarks (Epoch AI): GPT-4o-mini has scores on third-party math benchmarks — 52.6% on MATH Level 5 (rank 13 of 14 models with that score in our dataset) and 6.9% on AIME 2025 (rank 21 of 23). These place it at the low end of math-capable models by those external measures. Claude Opus 4.7 does not have corresponding external benchmark scores in our dataset for direct comparison on these tests.

The price gap here is not subtle — Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. GPT-4o-mini runs at $0.15 per million input tokens and $0.60 per million output tokens. That's roughly 33x cheaper on input and 42x cheaper on output.

At 1 million output tokens per month, Opus 4.7 costs $25.00 versus GPT-4o-mini's $0.60. At 10 million output tokens, that's $250 versus $6. At 100 million output tokens — a realistic scale for a production app — Opus 4.7 runs $2,500 per month in output costs alone, compared to $60 for GPT-4o-mini.

Who should care: developers building high-volume consumer products, chatbots, or classification pipelines should run the numbers carefully before choosing Opus 4.7. The cost difference funds significant infrastructure. Opus 4.7's pricing makes sense for low-volume, high-stakes workflows — legal analysis, strategic research, complex agentic tasks — where a wrong answer costs more than the compute. For anything that runs at scale and tolerates mid-tier accuracy, GPT-4o-mini is the rational default.

Choose Claude Opus 4.7 if: your application depends on accurate reasoning over documents (faithfulness score of 5/5 vs 3/5 is critical for RAG and summarization), you're building agentic systems where planning and tool-use reliability matter, you need to process inputs longer than 128,000 tokens, or you're doing strategic analysis work where shallow reasoning produces wrong answers. The cost is real — $25 per million output tokens — but justifiable when quality failures are expensive.

Choose GPT-4o-mini if: you're running at scale (10M+ tokens/month) and mid-tier accuracy is acceptable for your use case, you need a classification or routing layer where it ties for 1st in our tests, your application requires strong safety calibration (4/5 vs 3/5), or you're prototyping and want to minimize spend. At $0.60 per million output tokens, it's one of the most cost-efficient options in the field, and its structured output and multilingual scores (4/5 on both) hold up for many production tasks. The math benchmark results from Epoch AI (6.9% on AIME 2025, 52.6% on MATH Level 5) are a caution flag for any numerically intensive workload.