Deep Suite Benchmark Shows GPT-5.5 Beats Claude and Gemini

Existing coding benchmarks such as SWEBench Pro often fail to reflect real‑world usage. Many rely on public GitHub commits and issues, creating a contamination risk where models have already seen the solutions. Their verifiers also produce high error rates, with 8 % false positives and 24 % false negatives, leading to misleading performance signals.

Deep Suite: A New Standard

DataCurve.ai created Deep Suite as a long‑horizon software‑engineering benchmark that eliminates contamination. Every task is handcrafted, never adapted from public commits, and includes a prompt, an executable verifier, and a reference solution. The benchmark spans 91 repositories across TypeScript, Go, Python, JavaScript, and Rust, ensuring high diversity.

Prompts are short, but solutions require 5.5 × more code and twice the output tokens, mirroring real development complexity. Verification focuses on behavioral change rather than exact code matching, driving error rates down to 0.3 % false positives and 1.1 % false negatives.

Comparative Performance Analysis

GPT‑5.5 leads the Deep Suite leaderboard with a 70 % pass rate, holding a 15‑point advantage over Claude Opus 4.7. It also proves far more cost‑efficient, costing roughly $5.80 per trial versus $16 for Claude. Token usage highlights the efficiency gap: GPT‑5.5 consumes about 47 k tokens, Claude Opus 4.7 uses 97 k, and Gemini 3.5 Flash reaches 150 k.

The wider spread of scores on Deep Suite makes it easier to distinguish model capabilities, unlike the tighter clustering observed on SWEBench Pro.

Behavioral Insights into AI Models

Claude Opus 4.7 often forgets parts of multi‑part prompts, missing parallel requirements, yet it excels at leveraging environment state—such as running git log to recover solutions. GPT‑5.5 reads prompts and repository contracts literally, resulting in the lowest rate of missing stated behaviors among all configurations.

Both models are more likely to write their own tests when prompts do not explicitly discourage it, indicating a tendency toward self‑verification.

The Role of Scaffolding and Harnesses

Deep Suite employs the “miniswe‑agent” harness uniformly across all models, ensuring that leaderboard results reflect true model capability rather than differences in scaffolding. The verification process remains implementation‑agnostic, testing whether submitted code achieves the requested behavioral change. Prompting strategy favors behavior‑focused, concise instructions, mirroring how developers interact with agents in practice.