Claude Opus 4.6 vs GPT-4.1 Mini

Across our 12-test internal benchmark suite, Claude Opus 4.6 wins 6 tests outright, GPT-4.1 Mini wins 1, and 5 are tied.

Where Opus 4.6 wins decisively:

• Strategic analysis: 5 vs 4. Opus 4.6 ties for 1st among 54 models; GPT-4.1 Mini ranks 27th. For nuanced tradeoff reasoning with real numbers, Opus 4.6 is in a different tier.
• Creative problem solving: 5 vs 3. Opus 4.6 ties for 1st among 8 models; GPT-4.1 Mini ranks 30th of 54. That 2-point gap is material — in our testing it reflects the difference between obvious suggestions and genuinely novel, feasible ideas.
• Tool calling: 5 vs 4. Opus 4.6 ties for 1st among 17 models; GPT-4.1 Mini ranks 18th of 54. For agentic workflows where function selection and argument accuracy determine whether a pipeline succeeds or silently fails, this gap matters.
• Agentic planning: 5 vs 4. Opus 4.6 ties for 1st among 15 models; GPT-4.1 Mini ranks 16th. Goal decomposition and failure recovery — the backbone of multi-step agents — favor Opus 4.6.
• Faithfulness: 5 vs 4. Opus 4.6 ties for 1st among 33 models; GPT-4.1 Mini ranks 34th of 55. Sticking to source material without hallucinating is critical for RAG and document summarization tasks.
• Safety calibration: 5 vs 2. This is the largest gap in the comparison. Opus 4.6 ties for 1st among only 5 models out of 55 tested. GPT-4.1 Mini ranks 12th but scores 2 — below the 75th percentile of 2 in our distribution, meaning it falls in the bottom half of tested models on refusing harmful requests while permitting legitimate ones.

Where GPT-4.1 Mini wins:

• Constrained rewriting: 4 vs 3. GPT-4.1 Mini ranks 6th of 53; Opus 4.6 ranks 31st. Compression within hard character limits is the one area where the smaller model outperforms the larger one in our testing.

Ties (both models perform identically):

• Structured output (both 4/5, both rank ~26th of 54)
• Classification (both 3/5, both rank ~31st of 53)
• Long context (both 5/5, tied for 1st among 37 models)
• Persona consistency (both 5/5, tied for 1st among 37 models)
• Multilingual (both 5/5, tied for 1st among 35 models)

On external benchmarks (Epoch AI), the coding and math data sharpen the picture. Claude Opus 4.6 scores 78.7% on SWE-bench Verified — rank 1 of 12 models with this score — versus no SWE-bench data for GPT-4.1 Mini. On AIME 2025, Opus 4.6 scores 94.4% (rank 4 of 23) compared to GPT-4.1 Mini's 44.7% (rank 18 of 23) — a 49.7-percentage-point gap on competition math. GPT-4.1 Mini does have a MATH Level 5 score of 87.3% (rank 9 of 14 models tested), which places it below the field median of 94.15% by that external measure, though Opus 4.6 has no MATH Level 5 score in the payload for direct comparison.

The output cost difference between these models is significant in absolute terms: Claude Opus 4.6 at $25.00/M output tokens versus GPT-4.1 Mini at $1.60/M output tokens — a 15.6x ratio. At 1M output tokens/month, that's $25 vs $1.60 — negligible either way. At 10M tokens/month, you're paying $250 vs $16 — still manageable for a business application. At 100M tokens/month, the gap becomes $2,500 vs $160 per month — a $2,340 monthly difference that demands justification. Input costs follow the same ratio: $5.00 vs $0.40 per million tokens. Developers running classification pipelines, document routing, or constrained rewriting at scale — tasks where the two models tie or GPT-4.1 Mini actually wins — should default to GPT-4.1 Mini. The cost case for Opus 4.6 is strongest in low-volume, high-value agentic workflows where a single model failure costs more than the monthly API bill.

Choose Claude Opus 4.6 if:

• You're building multi-step agents where tool calling accuracy, agentic planning, and failure recovery are mission-critical — Opus 4.6 scores 5/5 vs GPT-4.1 Mini's 4/5 on both dimensions in our testing.
• Your application is safety-sensitive: Opus 4.6 scored 5/5 on safety calibration (top 5 of 55 models); GPT-4.1 Mini scored 2/5.
• You need strong coding pipeline performance — Opus 4.6 scores 78.7% on SWE-bench Verified (rank 1 of 12, per Epoch AI).
• You need advanced math or reasoning: Opus 4.6 scores 94.4% on AIME 2025 vs GPT-4.1 Mini's 44.7% (Epoch AI).
• Creative problem solving and strategic analysis are core to the task, not incidental.
• Volume is low enough that paying $25/M output tokens is justifiable against the quality uplift.

Choose GPT-4.1 Mini if:

• You're running high-volume, cost-sensitive workloads where the benchmarks tie: structured output, classification, long context, persona consistency, multilingual tasks — all score identically.
• Constrained rewriting (headlines, summaries with character limits) is a primary use case — GPT-4.1 Mini outperforms Opus 4.6 there.
• You're building consumer-facing features where the 15.6x cost difference at scale (e.g., $160 vs $2,500/month at 100M output tokens) determines product economics.
• Your use case accepts a 4/5 on tool calling and agentic planning rather than requiring a 5/5.
• You need file input support alongside text and image — GPT-4.1 Mini's modality spec includes file inputs per the payload; Opus 4.6 does not list this.