Claude Opus 4.7 vs GPT-5.4 Mini

Across our 12-test suite, Claude Opus 4.7 wins 4 benchmarks outright, GPT-5.4 Mini wins 3, and the two tie on 5.

Where Claude Opus 4.7 leads:

• Tool calling (5 vs 4): Opus 4.7 ties for 1st among 55 models; GPT-5.4 Mini ranks 19th. For function selection, argument accuracy, and sequencing across multi-step calls, Opus 4.7 has a clear edge — meaningful for any application chaining multiple API calls or tool invocations.
• Agentic planning (5 vs 4): Opus 4.7 ties for 1st among 55 models; GPT-5.4 Mini ranks 17th. Goal decomposition and failure recovery favor Opus 4.7, which matters when building autonomous agents that need to recover from unexpected states.
• Creative problem solving (5 vs 4): Opus 4.7 ties for 1st among 55 models; GPT-5.4 Mini ranks 10th. This measures non-obvious, specific, and feasible ideas — relevant for brainstorming, product strategy, and open-ended reasoning tasks.
• Safety calibration (3 vs 2): Opus 4.7 ranks 10th of 56 models; GPT-5.4 Mini ranks 13th. Both models score below the field median of 2 on this benchmark — but note the median itself is low (p50 = 2), so Opus 4.7's score of 3 actually places it meaningfully above average. This test measures accurate refusals: blocking harmful requests while permitting legitimate ones.

Where GPT-5.4 Mini leads:

• Structured output (5 vs 4): GPT-5.4 Mini ties for 1st among 55 models; Opus 4.7 ranks 26th. JSON schema compliance and format adherence are stronger in GPT-5.4 Mini — a significant practical advantage for developers relying on structured responses in production pipelines.
• Classification (4 vs 3): GPT-5.4 Mini ties for 1st among 54 models; Opus 4.7 ranks 31st. Accurate categorization and routing is substantially better in GPT-5.4 Mini. This is one of the most common production use cases, and GPT-5.4 Mini's top-tier performance here is a genuine differentiator.
• Multilingual (5 vs 4): GPT-5.4 Mini ties for 1st among 56 models; Opus 4.7 ranks 36th. For non-English output quality, GPT-5.4 Mini is the clear winner.

Ties (both models perform equally):

Strategic analysis, constrained rewriting, faithfulness, long context, and persona consistency are all tied — both models score at the top tier on faithfulness, long context retrieval at 30K+ tokens, and persona consistency. Neither has an advantage here.

One notable context window difference: Claude Opus 4.7 supports up to 1 million tokens of context; GPT-5.4 Mini supports 400,000 tokens. Despite this, both score 5/5 on our long context benchmark, so for most applications the practical difference may be limited to very large document tasks.

The cost difference between these two models is substantial. Claude Opus 4.7 runs $5.00 per million input tokens and $25.00 per million output tokens. GPT-5.4 Mini runs $0.75 per million input tokens and $4.50 per million output tokens.

At 1 million output tokens per month, that's $25 vs $4.50 — a $20.50 difference. Scale to 10 million output tokens and you're looking at $250 vs $45, a gap of $205. At 100 million output tokens, the difference reaches $2,050 per month.

Developers running high-throughput pipelines — classification, document processing, translation, summarization — should default to GPT-5.4 Mini given it actually wins or ties on the benchmarks most relevant to those tasks. Claude Opus 4.7's premium is justified only when you're building agentic systems, complex multi-tool workflows, or applications where creative problem solving is the bottleneck. Paying 5.5x more for a model that scores lower on structured output and classification is hard to defend at scale.

Choose Claude Opus 4.7 if:

• You're building agentic systems that require multi-step tool use, goal decomposition, or failure recovery — it scores 5/5 on both tool calling and agentic planning, ranking in the top tier of 55 models on each.
• Creative problem solving is central to your product (brainstorming, open-ended strategy, novel solution generation).
• You need to process documents or inputs exceeding 400,000 tokens — Opus 4.7's 1 million token context window is the only option between these two for very large contexts.
• Budget is not the primary constraint and the quality gap on agentic tasks justifies the premium.

Choose GPT-5.4 Mini if:

• You're running classification, routing, or categorization workloads — it ranks 1st of 54 models on classification in our tests, while Opus 4.7 ranks 31st.
• Your application depends on reliable structured output and JSON schema compliance — GPT-5.4 Mini ranks 1st of 55 models; Opus 4.7 ranks 26th.
• You're serving multilingual users — GPT-5.4 Mini ranks 1st of 56 models; Opus 4.7 ranks 36th.
• You're operating at scale. At 10M output tokens/month, GPT-5.4 Mini saves $205 vs Opus 4.7 — while actually outperforming it on three of the most common production task types.
• You want access to explicit parameter controls like structured outputs, seed, and tool choice — these are confirmed supported parameters for GPT-5.4 Mini.