Comparing the three flagship models — GPT-5.5, Claude Opus 4.7, and Gemini 3.1 Pro — is the most frequently asked question among AI practitioners in 2026. Synthesizing data from multiple benchmarks and community tests, each model’s strength zones have become clear.
Benchmark Comparison
| Dimension | Claude Opus 4.7 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|
| Arena Text | 1493 ±7 | 1488 ±10 | 1493 ±5 |
| Arena Code | 1565 | 1500 (Codex) | Not in Top 10 |
| SWE-bench Pro | 64.3% | 58.6% | Not published |
| HLE | 46.9% | 41.4% | Not published |
| MRCR @ 1M Context | 32.2% | 74% | Not published |
| Terminal-Bench 2.0 | ~70% | 82.7% | Not published |
Where Each Model Excels
Claude Opus 4.7: Code and Complex Reasoning
Claude Opus 4.7 is the most outstanding in code-related metrics. Arena code score of 1565 far exceeds all competitors, with SWE-bench Pro at 64.3% and HLE at 46.9% — both the highest among published data.
Best for: Complex code development, large codebase refactoring, technical design requiring multi-step reasoning.
GPT-5.5: Long Context and Terminal Workflows
GPT-5.5’s unique advantages are in two areas:
Million-level context handling. MRCR test shows 74%, far exceeding Claude’s 32.2%.
Terminal automation. Terminal-Bench 2.0 score of 82.7%, leading Claude Opus 4.7 by about 13 points. GPT-5.5 can complete 1000+ consecutive tool calls in real software engineering tasks.
Best for: Long document analysis, terminal automation, multi-step Agent workflows.
Gemini 3.1 Pro: The Cost-Effective Route
Gemini 3.1 Pro ties Claude Opus 4.7 at 1493 in Arena text (±5 error range), meaning the gap in general conversation experience is minimal. But its pricing is significantly lower — community data shows Gemini’s API price is about 1/15 of GPT-5.5 Pro.
Best for: Budget-sensitive large-scale calls, general Q&A and text processing.
Selection Advice
- Individual developers / small teams: Claude Opus 4.7 for code tasks, GPT-5.5 for long context or Agent building.
- Enterprise applications: Gemini 3.1 Pro for cost-sensitive, large-scale scenarios.
- Multi-model strategy: Use GPT-5.5 for planning, Claude for code, Gemini for bulk low-cost processing.
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