By skyforbes 
There's been a Lot of benchmarks around these two latest released models on the Internet.
I am a dev & interested in using the models for generating Python code.
here's the Benchmark Breakdown:
| Benchmark | GPT-5.1-Codex-Max | Gemini 3 Pro | Winner |
|---|---|---|---|
| SWE-Bench Verified (Bug Fixing) | 77.9% (xhigh effort) | 76.2% | Codex-Max |
| LiveCodeBench Elo (Algorithmic) | ~2,240 | 2,439 | Gemini 3 |
| Terminal Bench 2.0 (CLI Agent) | 58.1% | 54.2% | Codex-Max |
| AIME 2025 (Math, with tools) | 100% | 100% | Tie |
| ARC-AGI-2 (Novel Reasoning) | Not disclosed | 45.1% (Deep Think) | Gemini 3 |
| MathArena Apex | ~1-2% | 23.4% | Gemini 3 |
| Context Window | 128K+ tokens (with compaction) | 1,000,000 tokens | Gemini 3 |
Gemini 3 Pro has the The 1-Million Token Advantage. I am looking forward to that. As it would be easier for devs to add more context in big repos.
best part is that it can load complete documentation for frameworks like Django, Flask, or TensorFlow directly into the conversation.
Used Both the models Side by Side in my Multi Agent AI setup with Anannas LLM Provider & the results were interesting.
Gemini 3 Pro Produced more thoroughly documented Python code with advanced type hints.
here's the Key Take Away:
- GPT-5.1-Codex-Max achieves 77.9% on SWE-Bench Verified.
- Gemini 3 Pro dominates with 2,439 Elo on LiveCodeBench (200 points ahead) and 45.1% on ARC-AGI-2, representing a 20× improvement over predecessors
- For Python specifically, Gemini 3 generates cleaner data processing scripts 2× faster (12 seconds vs 25 seconds for 50-line scripts)
- Cost efficiency differs significantly: OpenAI's pricing is 60% cheaper at $1.25/$10 per million tokens vs. Gemini's context-tiered premium structure
sometimes models behaves different than what the actual benchmark suggest. so would like to know what you use for coding.