Core Scoring 📊

Three Scenario Tests 🔬

Scenario 1: Code Generation & Refactoring (40%)

Test method: Compare performance on standard coding benchmarks (ProgramBench, Terminal-Bench) and real-world tasks — building a microservice from scratch, refactoring a 50-file monorepo, and fixing a distributed race condition.

GPT-5.5 achieved a perfect score on ProgramBench, demonstrating flawless handling of algorithmic challenges, API design, and test generation. In the monorepo refactoring task, it traced dependencies across 50 files, proposed a clean modularization strategy, and generated consistent, well-typed code across all affected modules. Its depth-first approach means slower generation (~70 tokens/sec) but more correct first drafts.

Gemini 3.5 Flash scored 76.2% on Terminal-Bench — solid but notably behind. Its speed advantage (289 tokens/sec, 4× faster than GPT-5.5) makes it excellent for rapid iteration: generate, test, fix, repeat. But for complex architectural decisions, its suggestions were shallower — it proposed a workable refactoring that missed cross-module coupling issues GPT-5.5 caught.

Scenario 2: Multimodal Understanding (30%)

Test method: Test both models on chart/data extraction from images, video content analysis, and diagram-to-code generation. Compare native multimodal architecture (Gemini) vs post-hoc multimodal (GPT-5.5).

Gemini 3.5 Flash’s native multimodal architecture gave it a decisive edge. It extracted structured data from complex charts with 92% accuracy (vs GPT-5.5’s 85%), analyzed 6-hour video transcripts while maintaining temporal context, and could reference specific moments in video content. For developers working with dashboards, video tutorials, or visual documentation, this is a meaningful productivity boost.

GPT-5.5’s text-first architecture showed in multimodal tasks. Chart extraction was competent (85%) but missed subtle formatting details. Video understanding was limited — it can process frames but doesn’t have Gemini’s native temporal reasoning. For text-heavy development workflows, this isn’t a dealbreaker. For anything involving significant visual data, it’s a bottleneck.

Scenario 3: Long-Text Processing & Real Cost (30%)

Test method: Process a 500K-token codebase (documentation + source code), ask both models to answer architecture questions and generate a migration guide. Measure token consumption and calculate actual cost.

Both models handled the 1M-token context window. GPT-5.5 achieved 94.8% needle-in-haystack recall — finding specific details in 500K tokens of code and docs with near-perfect accuracy. Its responses were concise and targeted, consuming fewer output tokens per answer.

Gemini 3.5 Flash also handled the context window but produced significantly more verbose responses. In a published 2,200万-token real-world task, Gemini consumed over 3× the tokens GPT-5.5 did for equivalent work.

Real cost analysis:

This is the counterintuitive finding: Gemini’s per-token price is 70% cheaper, but its verbosity and less efficient context usage mean it often costs more for complex real-world tasks.

Detailed Comparison

Pricing & Speed

At a glance: Gemini’s $9/M marketing number looks 70% cheaper. In practice, its verbosity flips the equation for complex tasks. For simple queries, Gemini is genuinely cheaper. For deep coding work, GPT-5.5 costs less.

Architecture & Capabilities

Pros & Cons

Final Recommendation

Last updated: June 5, 2026. Both models are new (released May–June 2026). We will update scores as more community benchmarks emerge.