Claude Opus 4.7 vs GPT-5.1

Across the 12 tests in our suite, Claude Opus 4.7 wins 4, GPT-5.1 wins 2, and they tie on 6. Here's what that looks like test by test.

Where Opus 4.7 wins:

• Tool calling (5 vs 4): Opus 4.7 scores 5/5, tied for 1st among 55 tested models — though it shares that position with 17 others. GPT-5.1 scores 4/5, ranking 19th of 55. For agentic pipelines where function selection accuracy and argument sequencing matter, this gap is meaningful. A wrong tool call in an automated workflow can cascade into hard-to-debug failures.

• Agentic planning (5 vs 4): Opus 4.7 scores 5/5, tied for 1st among 55 models (with 15 others). GPT-5.1 scores 4/5, ranking 17th of 55. Agentic planning measures goal decomposition and failure recovery — exactly the capabilities that separate capable autonomous agents from ones that stall when plans break down.

• Creative problem solving (5 vs 4): Opus 4.7 scores 5/5, tied for 1st among 55 models with 8 others — a tighter group at the top. GPT-5.1 scores 4/5, ranking 10th. This test measures non-obvious, specific, and feasible ideas, which matters for brainstorming, product ideation, and research tasks.

• Safety calibration (3 vs 2): Opus 4.7 scores 3/5, ranking 10th of 56 models (shared with 2 others). GPT-5.1 scores 2/5, ranking 13th of 56. Neither model scores exceptionally here — the median across all 53 active models is just 2/5, so Opus 4.7's 3 puts it notably above average while GPT-5.1 sits at the median. This test measures whether a model correctly refuses harmful requests while permitting legitimate ones — important for consumer-facing deployments.

Where GPT-5.1 wins:

• Classification (4 vs 3): GPT-5.1 scores 4/5, tied for 1st among 54 models tested (with 29 others). Opus 4.7 scores 3/5, ranking 31st of 54. This is the clearest single-benchmark win for GPT-5.1 in terms of relative standing. For routing pipelines, content moderation, tagging, and categorization workflows, GPT-5.1 has a real edge.

• Multilingual (5 vs 4): GPT-5.1 scores 5/5, tied for 1st among 56 models with 34 others. Opus 4.7 scores 4/5, ranking 36th of 56. Both models produce quality multilingual output, but GPT-5.1 reaches the ceiling. Teams building products for non-English markets should take note.

Where they tie:

Structured output (4/4), strategic analysis (5/5), constrained rewriting (4/4), faithfulness (5/5), long context (5/5), and persona consistency (5/5) are identical. Both models max out on faithfulness, long context, and persona consistency — all three tied for 1st in their respective categories across the full model set. For most document Q&A, summarization, and long-form retrieval tasks, you won't find a meaningful difference between the two.

External benchmarks (Epoch AI):

GPT-5.1 has scored external benchmarks that Opus 4.7 does not have data for in our current dataset. On SWE-bench Verified — which tests real GitHub issue resolution — GPT-5.1 scores 68%, ranking 7th of 12 models with external scores. The median across models with SWE-bench scores is 70.8%, placing GPT-5.1 slightly below the midpoint of that group. On AIME 2025 (math olympiad), GPT-5.1 scores 88.6%, ranking 7th of 23 models — above the median of 83.9% for that group. These external scores give useful signal about GPT-5.1's coding and math abilities, but Opus 4.7 has no comparable external benchmark data in this dataset, so a direct head-to-head comparison on those dimensions isn't possible.

The cost gap here is substantial. Claude Opus 4.7 runs at $5 per million input tokens and $25 per million output tokens. GPT-5.1 costs $1.25 per million input tokens and $10 per million output tokens — 4x cheaper on input and 2.5x cheaper on output.

At 1 million output tokens per month, that difference is $15 — barely noticeable. At 10 million output tokens, you're paying $250 versus $100, a gap of $150 per month. At 100 million output tokens — realistic for a production application — Opus 4.7 costs $2,500 versus $1,000 for GPT-5.1, a delta of $1,500 every month.

Who should care? Any team running high-volume pipelines: document processing, customer-facing chatbots, batch classification, or content generation. The pricing gap also matters for developers experimenting and iterating — GPT-5.1's lower cost lowers the barrier to prototyping. Opus 4.7's premium is easier to absorb in low-volume, high-stakes use cases where per-query accuracy outweighs throughput costs, such as agentic workflows that run a handful of complex tasks per day.

Choose Claude Opus 4.7 if:

• You're building autonomous agents where tool calling accuracy and multi-step planning are load-bearing. Opus 4.7 scores 5/5 on both tool calling and agentic planning in our tests, versus 4/5 for GPT-5.1.
• Your use case demands creative problem solving — non-obvious ideation, research synthesis, or open-ended reasoning — where Opus 4.7's 5/5 vs. GPT-5.1's 4/5 represents a genuine capability difference.
• Safety calibration matters for your deployment context. Opus 4.7 scores 3/5 versus GPT-5.1's 2/5, placing it above the field median while GPT-5.1 sits at it.
• Volume is low enough that the $15 per million output token premium doesn't compound into a budget problem.

Choose GPT-5.1 if:

• You're running classification, routing, or tagging pipelines. GPT-5.1 scores 4/5 versus Opus 4.7's 3/5, and ranks tied for 1st among 54 tested models on that benchmark.
• You're building for multilingual audiences. GPT-5.1 reaches the 5/5 ceiling; Opus 4.7 scores 4/5 and ranks 36th of 56 on multilingual quality.
• You're operating at scale. At 100 million output tokens per month, GPT-5.1 saves $1,500 versus Opus 4.7 while still matching it on six of twelve benchmarks.
• You need file input support alongside text and images — GPT-5.1's modalities include files, while Opus 4.7 handles text and images.
• You want documented API parameter control: GPT-5.1 explicitly supports structured outputs, tool choice, reasoning, seed, and response format parameters.