Claude Opus 4.7 vs GPT-4o

Across our 12-test benchmark suite, Claude Opus 4.7 wins 8 tests, GPT-4o wins 1, and 3 tests end in a tie. Here is the full breakdown:

Where Opus 4.7 leads clearly:

• Strategic analysis: Opus 4.7 scores 5, GPT-4o scores 2. This is the widest gap in the comparison. Opus 4.7 ties for 1st among 55 models; GPT-4o ranks 45th of 55. For nuanced tradeoff reasoning with real numbers — financial analysis, product strategy, scenario planning — Opus 4.7 is in a different tier.

• Creative problem solving: Opus 4.7 scores 5, GPT-4o scores 3. Opus 4.7 ties for 1st among 55 models; GPT-4o ranks 31st. When tasks require non-obvious, feasible ideas rather than generic suggestions, Opus 4.7 pulls away.

• Tool calling: Opus 4.7 scores 5, GPT-4o scores 4. Opus 4.7 ties for 1st among 55 models; GPT-4o ranks 19th. For function selection, argument accuracy, and sequencing in agentic systems, Opus 4.7 is the stronger foundation.

• Agentic planning: Opus 4.7 scores 5, GPT-4o scores 4. Opus 4.7 ties for 1st among 55 models; GPT-4o ranks 17th. Goal decomposition and failure recovery — the backbone of autonomous workflows — favors Opus 4.7.

• Long context: Opus 4.7 scores 5, GPT-4o scores 4. Opus 4.7 ties for 1st among 56 models; GPT-4o ranks 39th. Opus 4.7 also carries a 1,000,000-token context window versus GPT-4o's 128,000 tokens, making this gap practically significant for document-heavy tasks.

• Safety calibration: Opus 4.7 scores 3, GPT-4o scores 1. Opus 4.7 ranks 10th of 56; GPT-4o ranks 33rd. Safety calibration measures both refusals of harmful requests and permitting legitimate ones — GPT-4o's score of 1 places it near the bottom of all tested models on this dimension.

• Faithfulness: Opus 4.7 scores 5, GPT-4o scores 4. Opus 4.7 ties for 1st among 56 models; GPT-4o ranks 35th. Sticking to source material without hallucinating is critical for RAG systems and document-grounded tasks.

• Constrained rewriting: Opus 4.7 scores 4, GPT-4o scores 3. Opus 4.7 ranks 6th of 55; GPT-4o ranks 32nd.

Where GPT-4o leads:

• Classification: GPT-4o scores 4, Opus 4.7 scores 3. GPT-4o ties for 1st among 54 models; Opus 4.7 ranks 31st. This is GPT-4o's clearest win — for routing, categorization, and labeling pipelines, it outperforms Opus 4.7 in our testing.

Tests that are tied:

• Structured output: Both score 4, both rank 26th of 55.
• Persona consistency: Both score 5, both tie for 1st among 55 models.
• Multilingual: Both score 4, both rank 36th of 56.

External benchmarks (GPT-4o only): Our data includes third-party benchmark scores for GPT-4o from Epoch AI. GPT-4o scores 31% on SWE-bench Verified (real GitHub issue resolution), ranking 12th of 12 models with that score — the lowest among tracked models. On MATH Level 5 competition problems, it scores 53.3%, ranking 12th of 14. On AIME 2025, it scores 6.4%, ranking 22nd of 23. These external results reinforce that GPT-4o is not a strong choice for demanding math or software engineering tasks. Claude Opus 4.7 does not have external benchmark scores in our current data.

Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens — exactly half the input price and 40% of the output price. In practice, output tokens drive most API spend, so the output cost gap is what matters most.

At 1 million output tokens per month, Opus 4.7 costs $25 versus GPT-4o's $10 — a $15 difference that most teams won't notice. At 10 million output tokens, that gap widens to $150 per month. At 100 million output tokens — a realistic scale for production applications — Opus 4.7 runs $2,500 versus GPT-4o's $1,000, a $1,500 monthly premium.

Who should care: developers running high-volume, output-heavy pipelines (summarization, drafting, agentic loops with long outputs) will feel the cost gap acutely. For applications where Claude Opus 4.7's advantages in strategic analysis, tool calling, and agentic planning translate directly to fewer retries, better task completion, or reduced human review, the premium may pay for itself. For straightforward classification or routing tasks — where GPT-4o actually scores higher in our testing — there is no reason to pay the Opus 4.7 premium at all.

Choose Claude Opus 4.7 if:

• You are building agentic systems that require reliable tool calling, multi-step planning, and failure recovery — Opus 4.7 scores 5 on both tool calling and agentic planning in our tests.
• Your application involves long documents or large context windows — Opus 4.7 supports up to 1,000,000 tokens versus GPT-4o's 128,000.
• Strategic analysis or complex reasoning is central to your use case — the 5 vs 2 gap on strategic analysis is the largest in this comparison.
• You need strong faithfulness to source material in RAG or summarization pipelines.
• Safety calibration matters: Opus 4.7 scores 3 vs GPT-4o's 1 in our testing, placing it significantly higher among all models evaluated.
• Output quality justifies the cost: at $25 per million output tokens, Opus 4.7 is only worth it when quality differences translate to real savings downstream.

Choose GPT-4o if:

• Classification and routing are your primary workload — GPT-4o ties for 1st among 54 models on classification while Opus 4.7 ranks 31st.
• Cost is a constraint and your task is well within GPT-4o's capabilities: at $10 per million output tokens versus $25, GPT-4o saves $1,500/month at 100M output tokens.
• Your context window needs fall within 128,000 tokens and you do not need Opus 4.7's extended reasoning advantages.
• You are building high-volume, output-heavy pipelines where the 2.5x cost difference compounds quickly and benchmark parity is sufficient.