Claude Opus 4.7 vs GPT-5.2

Across our 12-test internal benchmark suite, GPT-5.2 wins 3 tests outright, Claude Opus 4.7 wins 1, and the remaining 8 are ties.

Where Claude Opus 4.7 wins:

• Tool calling (5 vs 4): Opus 4.7 scores 5/5, tied for 1st among 55 tested models. GPT-5.2 scores 4/5, ranking 19th of 55. For agents that chain function calls or require precise argument construction, this one-point gap matters — it represents the difference between the top tier and mid-upper range on this test.

Where GPT-5.2 wins:

• Safety calibration (5 vs 3): GPT-5.2 scores 5/5, tied for 1st among 56 models. Opus 4.7 scores 3/5, ranking 10th. Safety calibration measures whether a model correctly refuses harmful requests while permitting legitimate ones — not just refusal rate, but accuracy of judgment. A 2-point gap here is the largest spread between these two models and is notable for any deployment with public-facing access or compliance requirements.
• Multilingual (5 vs 4): GPT-5.2 scores 5/5, tied for 1st among 56 models. Opus 4.7 scores 4/5, ranking 36th. If you're serving non-English speakers, GPT-5.2 delivers meaningfully stronger output quality across languages in our testing.
• Classification (4 vs 3): GPT-5.2 scores 4/5, tied for 1st among 54 models. Opus 4.7 scores 3/5, ranking 31st. For routing, tagging, or categorization tasks, GPT-5.2 is clearly the better choice.

Where they tie: Both models score identically on strategic analysis (5/5), structured output (4/5), constrained rewriting (4/5), creative problem solving (5/5), faithfulness (5/5), long context (5/5), persona consistency (5/5), and agentic planning (5/5). On the top-scoring benchmarks, there is no practical difference between these two models.

External benchmarks (Epoch AI): GPT-5.2 has third-party benchmark data available. On AIME 2025, GPT-5.2 scores 96.1%, ranking 1st of 23 models tested — the sole holder of that score. On SWE-bench Verified, GPT-5.2 scores 73.8%, ranking 5th of 12 models tested with that score (above the 50th percentile of 70.8% for models in our dataset). Claude Opus 4.7 does not have external benchmark scores in our dataset, so direct comparison on those dimensions isn't possible. The AIME 2025 result is a strong signal for GPT-5.2's mathematical reasoning at the olympiad level.

Claude Opus 4.7 costs $5 per million input tokens and $25 per million output tokens. GPT-5.2 costs $1.75 per million input tokens and $14 per million output tokens — making it 65% cheaper on inputs and 44% cheaper on outputs.

At realistic usage volumes, that gap compounds fast. At 1 million output tokens per month, Opus 4.7 costs $25 versus GPT-5.2's $14 — an $11 difference you might absorb without noticing. At 10 million output tokens, the gap widens to $110 per month. At 100 million output tokens — typical for a production application with moderate traffic — you're looking at $2,500 versus $1,400, a $1,100 monthly difference.

For individual developers or low-volume teams, the cost gap is manageable. For companies running high-throughput pipelines, the $11 per million output token premium for Opus 4.7 needs to be justified by clear task-specific advantages. Given that GPT-5.2 matches or beats Opus 4.7 on 11 of 12 internal benchmarks, the default should be GPT-5.2 unless tool calling is a critical bottleneck.

GPT-5.2 also accepts a broader input modality — text, images, and files — compared to Opus 4.7's text and images, which may reduce preprocessing costs in document-heavy workflows. GPT-5.2's 400,000-token context window handles most real-world use cases, though Opus 4.7's 1,000,000-token window is a meaningful advantage for extremely long document analysis.

Choose Claude Opus 4.7 if:

• Your application is built around heavy tool use — multi-step function calling, complex API orchestration, or agentic systems where argument accuracy is critical. Opus 4.7's 5/5 versus GPT-5.2's 4/5 on tool calling is the only benchmark where it holds a clear edge.
• You need to process extremely long documents: Opus 4.7's 1,000,000-token context window is 2.5x GPT-5.2's 400,000-token limit. For full-book analysis, massive codebases, or regulatory document review, this headroom is real.
• Your budget allows the premium and tool calling is a production bottleneck you've actually measured.

Choose GPT-5.2 if:

• You're building anything public-facing or regulated: its 5/5 safety calibration (1st of 56 models) significantly outperforms Opus 4.7's 3/5 on correctly navigating harmful vs. legitimate request boundaries.
• Your users are multilingual: GPT-5.2 scores 5/5 on multilingual output versus Opus 4.7's 4/5, ranking 1st of 56 models in our testing.
• You're doing classification, routing, or tagging at scale: GPT-5.2 scores 4/5 (1st of 54) versus Opus 4.7's 3/5 (31st of 54).
• Cost matters at volume: $14 versus $25 per million output tokens is a 44% savings with no performance penalty on 11 of 12 benchmarks.
• You need strong mathematical reasoning: GPT-5.2's 96.1% on AIME 2025 (ranked 1st of 23 models, per Epoch AI) signals elite quantitative performance.