Claude Opus 4.7 vs GPT-5 Nano

Across our 12-test suite, Claude Opus 4.7 wins 7 benchmarks, GPT-5 Nano wins 3, and they tie on 2.

Where Opus 4.7 leads:

• Tool calling: Opus 4.7 scores 5/5 (tied for 1st among 55 models) versus Nano's 4/5 (rank 19 of 55). This is a meaningful gap for agentic workflows where function selection and argument accuracy directly affect reliability.
• Agentic planning: Opus 4.7 scores 5/5 (tied for 1st among 55 models) versus Nano's 4/5 (rank 17 of 55). Better goal decomposition and failure recovery make Opus 4.7 the more dependable backbone for multi-step AI systems.
• Strategic analysis: Opus 4.7 scores 5/5 (tied for 1st among 55 models) versus Nano's 4/5 (rank 28 of 55). For nuanced tradeoff reasoning with real numbers — financial analysis, product strategy, risk assessment — Opus 4.7 has a clear edge.
• Creative problem solving: Opus 4.7 scores 5/5 (tied for 1st among 9 models out of 55) versus Nano's 3/5 (rank 31 of 55). This is one of the widest gaps in the comparison, and it matters for ideation, research, and open-ended tasks.
• Faithfulness: Opus 4.7 scores 5/5 (tied for 1st among 56 models) versus Nano's 4/5 (rank 35 of 56). Lower hallucination risk when working from source documents.
• Persona consistency: Opus 4.7 scores 5/5 (tied for 1st among 55 models) versus Nano's 4/5 (rank 39 of 55). Relevant for assistant products, roleplay, or any system maintaining a defined character.
• Constrained rewriting: Opus 4.7 scores 4/5 (rank 6 of 55) versus Nano's 3/5 (rank 32 of 55). Better compression under hard character limits — useful for copy editing, summaries, and ad copy.

Where GPT-5 Nano leads:

• Structured output: Nano scores 5/5 (tied for 1st among 55 models) versus Opus 4.7's 4/5 (rank 26 of 55). For JSON schema compliance and format adherence in production pipelines, Nano is the more reliable choice.
• Safety calibration: Nano scores 4/5 (rank 6 of 56) versus Opus 4.7's 3/5 (rank 10 of 56). Nano is better calibrated — refusing harmful requests while permitting legitimate ones. This matters in consumer-facing deployments.
• Multilingual: Nano scores 5/5 (tied for 1st among 56 models) versus Opus 4.7's 4/5 (rank 36 of 56). If your users aren't writing in English, Nano has a real advantage.

Ties:

• Classification (both 3/5, rank 31 of 54) and long context (both 5/5, tied for 1st among 56 models) are dead heats.

External benchmarks (Epoch AI): GPT-5 Nano has external math benchmark scores on file: 95.2% on MATH Level 5 (rank 7 of 14 models with this score) and 81.1% on AIME 2025 (rank 14 of 23). These are strong results on competition math — MATH Level 5 at 95.2% sits above the median of 94.15% across tested models, and 81.1% on AIME 2025 is just below the median of 83.9%. Claude Opus 4.7 does not have external benchmark scores in our current data, so no head-to-head comparison is possible on these dimensions.

The pricing gap here is not subtle. Claude Opus 4.7 runs $5.00 per million input tokens and $25.00 per million output tokens. GPT-5 Nano runs $0.05 per million input tokens and $0.40 per million output tokens — a 100x difference on input and 62.5x on output.

At 1 million output tokens per month, Opus 4.7 costs $25.00 versus Nano's $0.40 — a difference you'll barely notice. At 10 million output tokens, that gap widens to $250 vs. $4.00. At 100 million output tokens — a realistic scale for any production app with real traffic — you're looking at $2,500 vs. $40.00 per month. That's a $2,460 monthly difference on output alone.

Who should care? Developers building consumer-facing products, chatbots, document processors, or any system generating large output volumes should model this cost difference carefully before defaulting to Opus 4.7. For low-volume internal tooling, research tasks, or one-off complex analyses, the cost gap is trivial and Opus 4.7's benchmark lead is worth paying for. For anything at scale, Nano's pricing makes it a serious contender — especially given that it wins outright on structured output, which is a critical capability for many production pipelines.

Choose Claude Opus 4.7 if:

• You're building agentic systems, autonomous pipelines, or tool-calling workflows where a 5/5 vs. 4/5 gap in planning and function accuracy has real downstream consequences.
• Your tasks demand deep strategic or creative reasoning — consulting, research synthesis, competitive analysis, or complex ideation.
• Faithfulness to source material is non-negotiable (summarization, document QA, legal review).
• Output volume is low to moderate and the cost premium is acceptable against the quality gain.
• You need strong persona consistency for a branded assistant or character-based product.

Choose GPT-5 Nano if:

• You're running high-volume production systems where the 62.5x output cost difference compounds into thousands of dollars per month.
• Your pipeline depends on reliable structured output — JSON schema adherence, format compliance, API response generation.
• Your user base is multilingual and quality parity across languages is a requirement.
• Safety calibration matters for a consumer product where over-refusals or harmful outputs both create problems.
• Latency is critical and you're optimizing for speed in developer tools or rapid interaction environments.
• You need reasoning token support (GPT-5 Nano supports this; Opus 4.7's parameter support is not documented in our data).