Claude Opus 4.7 vs GPT-5 Mini

Across our 12-test suite, Claude Opus 4.7 wins 3 benchmarks outright, GPT-5 Mini wins 3, and the two tie on the remaining 6.

Where Opus 4.7 leads:

• Tool calling: 5/5 vs 3/5. Opus 4.7 ties for 1st among 55 tested models (with 17 others); GPT-5 Mini ranks 48th of 55. This gap is meaningful — tool calling covers function selection, argument accuracy, and sequencing, all critical for agentic apps. A 3/5 at rank 48 suggests GPT-5 Mini struggles with complex function-chaining.
• Agentic planning: 5/5 vs 4/5. Opus 4.7 ties for 1st (with 15 others); GPT-5 Mini ranks 17th. Goal decomposition and failure recovery are where this difference shows up in practice — multi-step autonomous tasks will generally go more smoothly with Opus 4.7.
• Creative problem solving: 5/5 vs 4/5. Opus 4.7 ties for 1st (with 8 others, a tighter group than most top-tier ties); GPT-5 Mini ranks 10th. The benchmark tests non-obvious, specific, feasible ideation — Opus 4.7 produces sharper answers here.

Where GPT-5 Mini leads:

• Structured output: 5/5 vs 4/5. GPT-5 Mini ties for 1st (with 24 others); Opus 4.7 sits at rank 26. For JSON schema compliance and format adherence — central to API integrations and data pipelines — GPT-5 Mini is the stronger bet.
• Classification: 4/5 vs 3/5. GPT-5 Mini ties for 1st (with 29 others); Opus 4.7 ranks 31st. Accurate categorization and routing matters for triage systems, content moderation, and intake flows.
• Multilingual: 5/5 vs 4/5. GPT-5 Mini ties for 1st (with 34 others); Opus 4.7 ranks 36th. For non-English output quality, GPT-5 Mini has a clear edge.

Ties (6 benchmarks): Strategic analysis, constrained rewriting, faithfulness, long context, safety calibration, and persona consistency all score identically. Both reach 5/5 on faithfulness and long context, 4/5 on constrained rewriting, 3/5 on safety calibration, and 5/5 on persona consistency — with rankings reflecting broad field-wide clusters rather than individual distinction.

External benchmarks (Epoch AI): GPT-5 Mini carries third-party scores that Opus 4.7 lacks in this dataset. On SWE-bench Verified, GPT-5 Mini scores 64.7% (rank 8 of 12 models tested), sitting just above the field median of ~70.8% but not at the top. On MATH Level 5 competition problems, it scores 97.8% (rank 2 of 14, tied with 2 others) — an exceptional result that places it among the strongest math models tracked by Epoch AI. On AIME 2025, it scores 86.7% (rank 9 of 23). These scores reinforce GPT-5 Mini's strength in structured reasoning and math, even at its compact size. No external benchmark data is available for Opus 4.7 in this dataset.

The cost gap here is substantial. Claude Opus 4.7 runs $5 per million input tokens and $25 per million output tokens. GPT-5 Mini runs $0.25 per million input tokens and $2 per million output tokens — making it 20x cheaper on inputs and 12.5x cheaper on outputs.

At 1 million output tokens per month, Opus 4.7 costs $25 versus GPT-5 Mini's $2 — a $23 difference that's easy to absorb. At 10 million output tokens, that gap becomes $250 vs $20, or $230/month. At 100 million output tokens — typical for a production app with moderate traffic — you're looking at $2,500 vs $200, a $2,300 monthly difference.

Who should care: developers building high-volume applications, customer service pipelines, or document-processing workflows will feel this gap acutely. Researchers or teams running occasional deep-analysis tasks may find Opus 4.7's agentic and creative capabilities worth the premium. For anyone routing to this model at scale, the six benchmarks where the two models tie are an argument to default to GPT-5 Mini unless a specific capability gap forces the upgrade.

Choose Claude Opus 4.7 if:

• Your workflow depends on reliable tool calling and multi-step agentic execution — its 5/5 score vs GPT-5 Mini's 3/5 is a genuine operational difference for function-chaining pipelines.
• You need strong agentic planning for autonomous task completion with failure recovery.
• Creative ideation quality matters and you want the model with the narrowest tie group at the top score.
• Cost is secondary and you're running low-volume, high-stakes workloads where per-call quality matters more than per-token price.

Choose GPT-5 Mini if:

• You're building structured output pipelines, API integrations, or any system that relies on strict JSON schema compliance — it scores 5/5 vs Opus 4.7's 4/5 and ranks in the top tier.
• Classification, routing, or content triage are core to your application.
• You serve a multilingual user base and need consistent non-English output quality.
• You're processing more than a few million tokens per month — at 100M output tokens, GPT-5 Mini saves $2,300/month versus Opus 4.7.
• Math-heavy reasoning is in scope: GPT-5 Mini's 97.8% on MATH Level 5 (Epoch AI) is among the strongest results in the tracked field.
• The six tied benchmarks cover your primary use case, making the 12.5x output cost premium for Opus 4.7 hard to justify.