Claude Opus 4.7 vs o4 Mini

Across our 12-test internal suite, Claude Opus 4.7 wins 4 categories, o4 Mini wins 3, and 5 are ties. Here's what that looks like in practice:

Where Opus 4.7 wins:

• Agentic planning (5 vs 4): Opus 4.7 ties for 1st among 55 models; o4 Mini ranks 17th. This is the biggest practical differentiator — agentic planning covers goal decomposition and failure recovery, which matters enormously in multi-step automated workflows.
• Creative problem solving (5 vs 4): Opus 4.7 ties for 1st among 55 models; o4 Mini ranks 10th. The delta here is real for open-ended brainstorming or novel solution generation.
• Constrained rewriting (4 vs 3): Opus 4.7 ranks 6th of 55; o4 Mini ranks 32nd. This test covers compression within hard character limits — copywriting, UI strings, social posts — and Opus 4.7 is clearly superior here.
• Safety calibration (3 vs 1): Opus 4.7 ranks 10th of 56; o4 Mini ranks 33rd. This measures refusal of harmful requests while permitting legitimate ones. o4 Mini's score of 1 sits at the bottom quartile across all 56 models tested — a meaningful concern for any deployment with sensitive users or compliance requirements.

Where o4 Mini wins:

• Structured output (5 vs 4): o4 Mini ties for 1st among 55 models; Opus 4.7 ranks 26th. For pipelines that depend on reliable JSON schema compliance, o4 Mini is the cleaner choice.
• Classification (4 vs 3): o4 Mini ties for 1st among 54 models; Opus 4.7 ranks 31st. Routing, tagging, and categorization tasks favor o4 Mini clearly.
• Multilingual (5 vs 4): o4 Mini ties for 1st among 56 models; Opus 4.7 ranks 36th. Non-English workloads are a genuine o4 Mini strength.

Ties (both models score equally): Strategic analysis (5/5), tool calling (5/5), faithfulness (5/5), long context (5/5), and persona consistency (5/5) are all tied. On tool calling, both rank tied for 1st of 55. On long context — retrieval accuracy at 30K+ tokens — both also share the top tier, though Opus 4.7's 1,000,000-token context window dwarfs o4 Mini's 200,000-token window, which could matter for very long document tasks even when scores are equal.

External benchmarks (Epoch AI): o4 Mini has external benchmark data not available for Opus 4.7. On MATH Level 5, o4 Mini scores 97.8%, placing it 2nd among 14 models tested (tied with two others) — above the 50th percentile of 94.15% for this benchmark. On AIME 2025, o4 Mini scores 81.7%, ranking 13th of 23 models tested — above the 50th percentile benchmark median of 83.9%. These results confirm o4 Mini as a strong quantitative reasoning model, particularly on competition-level math, though its AIME score sits just below the field median among tested models.

The cost gap between these two models is substantial. Claude Opus 4.7 runs $5.00 per million input tokens and $25.00 per million output tokens. o4 Mini comes in at $1.10 per million input tokens and $4.40 per million output tokens — making it roughly 4.5x cheaper on input and nearly 5.7x cheaper on output.

At 1 million output tokens per month, Opus 4.7 costs $25 vs o4 Mini's $4.40 — a difference of $20.60. Scale to 10 million output tokens and that gap becomes $206. At 100 million output tokens monthly, you're looking at $2,500 for o4 Mini versus $25,000 for Opus 4.7 — a $22,500 monthly delta.

Who should care? Any developer running high-throughput pipelines — document processing, batch classification, multilingual content generation — should strongly weigh o4 Mini. The benchmarks show it matches or beats Opus 4.7 on several of those exact tasks (classification: 4 vs 3, multilingual: 5 vs 4, structured output: 5 vs 4). Spending 5.7x more for a model that underperforms on your target tasks is a difficult case to make.

Opus 4.7's pricing is more defensible in low-volume, high-stakes agentic workflows where its stronger agentic planning score (5 vs 4) can prevent costly failures — failures that, in an automated pipeline, may cost more than the token price difference.

Choose Claude Opus 4.7 if:

• You're building agentic systems where goal decomposition, multi-step planning, and failure recovery are critical — Opus 4.7 scores 5 vs o4 Mini's 4 on our agentic planning benchmark.
• Your use case requires constrained rewriting — ad copy, UI strings, character-limited formats — where Opus 4.7 ranks 6th vs o4 Mini's 32nd of 55 models.
• Safety calibration is a deployment requirement. Opus 4.7 scores 3 vs o4 Mini's 1, and o4 Mini sits in the bottom third of all 56 models tested on this dimension.
• You need a 1,000,000-token context window. Opus 4.7's context capacity is 5x larger than o4 Mini's 200,000-token limit.
• Volume is low enough that the $25 vs $4.40 per million output token gap doesn't dominate your budget.

Choose o4 Mini if:

• Your pipelines depend on structured output — JSON schema compliance, API response formatting — where o4 Mini ties for 1st of 55 vs Opus 4.7's rank of 26th.
• You run classification or routing tasks at scale. o4 Mini ties for 1st of 54 models; Opus 4.7 ranks 31st.
• Your users or content are non-English. o4 Mini ties for 1st of 56 models on multilingual output; Opus 4.7 ranks 36th.
• You need competition-level math reasoning. o4 Mini scores 97.8% on MATH Level 5 (Epoch AI), ranking 2nd among 14 models tested.
• Cost efficiency matters. At 10M output tokens/month, o4 Mini saves roughly $206 vs Opus 4.7. At 100M tokens, that's $22,500/month.