Claude Opus 4.6 vs GPT-5 Mini

Summary of head-to-heads from our 12-test suite (scores are from our testing and external benchmarks where provided):

• Tool calling: Claude Opus 4.6 scores 5 vs GPT-5 Mini 3. Opus ranks tied for 1st of 54 (tied with 16 others); GPT-5 Mini ranks 47 of 54. This means Opus is materially better at selecting functions, sequencing calls, and building agent flows in our tests.
• Safety calibration: Opus 5 vs GPT-5 Mini 3. Opus is tied for 1st of 55 on safety calibration; GPT-5 Mini ranks 10 of 55. For apps that must refuse harmful requests or carefully discriminate allowed actions, Opus showed stronger behavior.
• Agentic planning: Opus 5 vs GPT-5 Mini 4. Opus is tied for 1st of 54; GPT-5 Mini sits at rank 16. Opus demonstrated superior goal decomposition and failure recovery in our evaluations.
• Creative problem solving: Opus 5 vs GPT-5 Mini 4 — Opus tied for 1st (shows better non-obvious feasible ideas in our tests).
• Structured output (JSON/schema): GPT-5 Mini 5 vs Opus 4. GPT-5 Mini is tied for 1st of 54 on structured output, so it’s the safer choice when you need strict schema compliance and format adherence.
• Constrained rewriting (compression / strict limits): GPT-5 Mini 4 vs Opus 3. GPT-5 Mini ranks 6 of 53 vs Opus rank 31, so GPT-5 Mini handles hard character limits and dense compression better in practice.
• Classification: GPT-5 Mini 4 vs Opus 3. GPT-5 Mini is tied for 1st of 53 on classification; Opus ranks 31. Use GPT-5 Mini when routing or categorization accuracy matters.
• Ties (no clear winner): strategic analysis, faithfulness, long context, persona consistency, multilingual — both models score 5 on many of these and often tie at top ranks. For example, both tie for 1st in strategic analysis and faithfulness in our rankings. External third-party benchmarks (Epoch AI):
• SWE-bench Verified (Epoch AI): Claude Opus 4.6 scores 78.7% (rank 1 of 12 in that external benchmark); GPT-5 Mini scores 64.7% (rank 8 of 12). This supports Opus’s superiority on real-world code/issue-resolution tasks in that dataset.
• MATH Level 5 (Epoch AI): GPT-5 Mini scores 97.8% (rank 2 of 14). Opus does not report a math_level_5 score in the payload. GPT-5 Mini’s high math score indicates strong performance on competition-style math problems in that external benchmark.
• AIME 2025 (Epoch AI): Opus 94.4% (rank 4 of 23) vs GPT-5 Mini 86.7% (rank 9 of 23). Opus leads on this math olympiad test in our comparative data. What this means for real tasks: choose Opus when you need reliable tool orchestration, agentic planning, and a safety-first model for workflow automation or coding agents; choose GPT-5 Mini when you need exact JSON outputs, tight-character compression, fast/classification workloads, or to minimize recurring inference costs.

The payload lists Claude Opus 4.6 at $5 input / $25 output per mTok and GPT-5 Mini at $0.25 input / $2 output per mTok (output price ratio = 12.5×). Interpreting those rates across realistic volumes (assuming the per-mTok unit in the payload and symmetrical input/output volume):

• 1M input tokens + 1M output tokens: Claude ≈ $30,000 (1M × [$5+$25] per mTok), GPT-5 Mini ≈ $2,250 (1M × [$0.25+$2]).
• 10M in + 10M out: Claude ≈ $300,000; GPT-5 Mini ≈ $22,500.
• 100M in + 100M out: Claude ≈ $3,000,000; GPT-5 Mini ≈ $225,000. Who should care: high-volume production services, multi-tenant APIs, and cost-sensitive startups must account for the 12–13× effective cost gap in budget planning. Teams prototyping, building chat UIs, or running heavy classification/JSON tasks may prefer GPT-5 Mini to reduce run costs. Teams needing best-in-class tool orchestration, safety calibration, and agentic planning should budget for Opus’s substantially higher price.

Choose Claude Opus 4.6 if you build agentic systems, orchestration platforms, or safety-sensitive, long-context professional workflows that rely on accurate tool-calling, agentic planning and refusal behavior — Opus wins tool-calling (5 vs 3), safety calibration (5 vs 3) and agentic planning (5 vs 4). Budget accordingly: Opus is far more expensive ($5/$25 input/output per payload). Choose GPT-5 Mini if you need strict structured output, classification, constrained rewriting, or are running high-volume/low-latency production where cost matters — GPT-5 Mini wins structured output (5 vs 4), constrained rewriting (4 vs 3) and classification (4 vs 3) while costing $0.25/$2 per the payload.