Claude Opus 4.7 vs GPT-4.1 Nano

Across our 12-test benchmark suite, Claude Opus 4.7 wins 7 categories, GPT-4.1 Nano wins 1, and they tie on 4.

Where Opus 4.7 dominates:

• Tool calling: Opus 4.7 scores 5/5 (tied for 1st among 55 models) vs GPT-4.1 Nano's 4/5 (rank 19 of 55). For agentic applications where function selection and argument accuracy determine whether a workflow succeeds or fails, this gap is meaningful.
• Agentic planning: 5/5 (tied for 1st of 55) vs 4/5 (rank 17 of 55). Goal decomposition and failure recovery are areas where Opus 4.7 has a clear edge — critical for multi-step AI agents.
• Strategic analysis: 5/5 (tied for 1st of 55) vs 2/5 (rank 45 of 55). This is Opus 4.7's most decisive win. GPT-4.1 Nano scores near the bottom of the field on nuanced tradeoff reasoning with real data — a significant gap for anyone using AI for analytical or advisory tasks.
• Creative problem solving: 5/5 (tied for 1st of 55, with 8 other models) vs 2/5 (rank 48 of 55). GPT-4.1 Nano scores in the bottom tier here. If your use case depends on non-obvious, feasible ideas, this spread matters.
• Long context: 5/5 (tied for 1st of 56) vs 4/5 (rank 39 of 56). Both models offer context windows over 1 million tokens, but Opus 4.7 demonstrates better retrieval accuracy at 30K+ tokens in our testing.
• Safety calibration: 3/5 (rank 10 of 56, one of only 3 models at this score) vs 2/5 (rank 13 of 56). Opus 4.7 is more reliably calibrated to refuse harmful requests while permitting legitimate ones — important for production deployments where over-refusal and under-refusal are both problems.
• Persona consistency: 5/5 (tied for 1st of 55) vs 4/5 (rank 39 of 55). Maintaining character and resisting prompt injection is a clear Opus 4.7 advantage for chatbot and assistant use cases.

Where GPT-4.1 Nano wins:

• Structured output: 5/5 (tied for 1st of 55) vs Opus 4.7's 4/5 (rank 26 of 55). This is GPT-4.1 Nano's only clear win. For applications that depend on strict JSON schema compliance — data pipelines, form extraction, API integration — GPT-4.1 Nano is the stronger choice and a fraction of the cost.

Ties (both models score identically):

• Constrained rewriting (both 4/5, rank 6 of 55), faithfulness (both 5/5, tied for 1st of 56), classification (both 3/5, rank 31 of 54), and multilingual (both 4/5, rank 36 of 56). Neither model differentiates on these tasks.

External benchmarks (Epoch AI): GPT-4.1 Nano has scores available on two third-party math benchmarks. It scores 70% on MATH Level 5 (rank 11 of 14 models tested, per Epoch AI) and 28.9% on AIME 2025 (rank 20 of 23 models tested, per Epoch AI). Both place GPT-4.1 Nano toward the lower end of the field on these external measures. Claude Opus 4.7 does not have external benchmark scores in our data for direct comparison.

The pricing difference between these two models is extreme. Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens — making Opus 4.7 50x more expensive on inputs and 62.5x more expensive on outputs.

In practice, that means:

• At 1M output tokens/month: Opus 4.7 costs $25; GPT-4.1 Nano costs $0.40. A difference of $24.60.
• At 10M output tokens/month: Opus 4.7 costs $250; GPT-4.1 Nano costs $4. A difference of $246.
• At 100M output tokens/month: Opus 4.7 costs $2,500; GPT-4.1 Nano costs $40. A difference of $2,460.

For consumer-facing applications with unpredictable scale — chatbots, classifiers, autocomplete, content moderation — GPT-4.1 Nano's pricing makes it essentially negligible in cost, while Opus 4.7 would require serious budget justification. Developers building agentic pipelines where each task involves multi-step tool calls and long outputs should model their token consumption carefully: at even moderate volume, Opus 4.7 can run $1,000–$2,500/month versus GPT-4.1 Nano's $15–$40 for the same workload. The cost gap narrows the use case for Opus 4.7 to situations where its benchmark advantages directly translate to business value.

Choose Claude Opus 4.7 if:

• You're building agentic systems where tool calling accuracy and multi-step planning directly affect task success
• Your application involves strategic analysis, business reasoning, or complex problem solving where GPT-4.1 Nano's 2/5 score is a disqualifier
• You need the strongest available persona consistency and resistance to prompt injection in a deployed assistant
• Volume is low-to-moderate (under 5M output tokens/month) and quality justifies the cost premium
• Long-context retrieval accuracy at 30K+ tokens is critical to your use case

Choose GPT-4.1 Nano if:

• JSON schema compliance and structured output reliability are your primary requirement — this is its only head-to-head win
• You're running high-volume workloads (10M+ output tokens/month) where Opus 4.7 would cost 62.5x more
• Your tasks are well-defined and don't require deep reasoning: classification, data extraction, translation, constrained rewriting
• You need to prototype or test at minimal cost before committing to a more expensive model
• You're building a latency-sensitive application and cost efficiency is a design constraint

The core tradeoff is simple: Claude Opus 4.7 is a substantially more capable model on the tasks that require real intelligence. GPT-4.1 Nano is an exceptionally cheap model that handles structured, well-defined tasks competently. They're not really competing for the same jobs.