Claude Opus 4.7 vs GPT-5

Across the 12 tests in our benchmark suite, GPT-5 wins 3, Claude Opus 4.7 wins 2, and they tie on 7. Neither model dominates — but the wins and ties each model holds tell different stories about where to deploy them.

Where GPT-5 wins:

• Structured output (5 vs 4): GPT-5 scores 5/5, tied for 1st among 55 models tested. Opus 4.7 scores 4/5, landing at rank 26 of 55. For applications requiring strict JSON schema compliance, GPT-5 is the safer choice. GPT-5 also explicitly supports structured outputs as a parameter, which provides additional reliability guarantees.

• Classification (4 vs 3): GPT-5 scores 4/5, tied for 1st among 54 models. Opus 4.7 scores 3/5, ranking 31st out of 54 — below the median. For routing, categorization, and triage tasks, this is a meaningful gap. A 3 here means Opus 4.7 is performing below the 50th percentile on this test.

• Multilingual (5 vs 4): GPT-5 scores 5/5, tied for 1st among 56 models. Opus 4.7 scores 4/5, ranking 36th out of 56 — in the bottom half. For non-English language applications, GPT-5 has a clear edge.

Where Claude Opus 4.7 wins:

• Creative problem solving (5 vs 4): Opus 4.7 scores 5/5, tied for 1st with 8 other models among 55 tested. GPT-5 scores 4/5, ranking 10th. This test measures non-obvious, specific, feasible ideas — the kind of lateral thinking that matters for brainstorming, strategy, and open-ended design tasks.

• Safety calibration (3 vs 2): Opus 4.7 scores 3/5, ranking 10th of 56 models (only 3 models share this score). GPT-5 scores 2/5, ranking 13th of 56. Both models score below the median on this test — the 50th percentile sits at 2, so a 3 is above average but not a top-tier result. For applications where granular refusal behavior matters (refusing harmful requests while permitting legitimate ones), Opus 4.7 is the better calibrated model in our testing.

Where they tie (7 of 12 tests): Both models score identically on tool calling (5/5), agentic planning (5/5), faithfulness (5/5), strategic analysis (5/5), long context (5/5), persona consistency (5/5), and constrained rewriting (4/4). These aren't participation trophies — both models are genuinely at or near the top on these dimensions. Tied for 1st on tool calling, agentic planning, and long context among 55+ models tested.

External benchmarks (Epoch AI): GPT-5 has external benchmark scores on file. It scores 98.1% on MATH Level 5 competition problems — rank 1 of 14 models tested, the top score in that set. On AIME 2025 math olympiad problems, it scores 91.4%, ranking 6th of 23 models tested. On SWE-bench Verified (real GitHub issue resolution), it scores 73.6%, ranking 6th of 12 models. No equivalent external benchmark scores are available for Claude Opus 4.7 in our data, so direct external comparison isn't possible — but GPT-5's math performance in particular is exceptional by any measure.

The price gap here is substantial and widens sharply at scale. Claude Opus 4.7 runs $5.00 per million input tokens and $25.00 per million output tokens. GPT-5 runs $1.25 input and $10.00 output — 4× cheaper on inputs and 2.5× cheaper on outputs.

At 1 million output tokens per month, you're paying roughly $25 for Opus 4.7 versus $10 for GPT-5 — a $15 difference that barely registers. At 10 million output tokens, that gap becomes $150/month. At 100 million output tokens — typical for a production application — you're looking at $2,500/month for Opus 4.7 versus $1,000/month for GPT-5, a $1,500 monthly delta.

Note that GPT-5 uses reasoning tokens (a quirk of its architecture), which can inflate effective token counts depending on how heavily it engages its reasoning process. Factor that into cost projections for complex tasks.

Who should care: individual developers and small teams can comfortably evaluate both. Teams running high-volume pipelines — document processing, classification at scale, multilingual workflows — should weight the 2.5× output cost ratio heavily. The cost difference only justifies Opus 4.7 if you specifically need its creative problem solving or safety calibration advantages.

Choose Claude Opus 4.7 if:

• Your use case depends on creative problem solving — generating non-obvious, high-quality ideas (scored 5/5 in our testing vs GPT-5's 4/5)
• Safety calibration is a product requirement and you need more nuanced refusal behavior (3/5 vs 2/5)
• Cost is not a constraint and you want the model that slightly edges out GPT-5 on open-ended reasoning tasks
• You're processing inputs up to 1 million tokens (Opus 4.7's context window is 1M tokens vs GPT-5's 400K)

Choose GPT-5 if:

• You're building pipelines that require structured JSON output reliably (5/5, tied for 1st vs Opus 4.7's 4/5 at rank 26)
• Your application involves classification, routing, or categorization (4/5 at 1st vs Opus 4.7's 3/5 at 31st)
• You need strong multilingual output quality (5/5 vs 4/5, and Opus 4.7 ranks 36th of 56 on this test)
• Math-intensive tasks are in scope — GPT-5's 98.1% on MATH Level 5 and 91.4% on AIME 2025 (Epoch AI) make it the clear choice here
• You're operating at any meaningful scale — the 2.5× output cost difference ($10 vs $25 per million tokens) compounds quickly in production
• You want explicit API support for reasoning, seed control, and structured output parameters