GPT-6 Astra vs Gemini 3.1 Pro: Brains vs Context

If you need raw reasoning power, GPT-6 Astra wins. If you live inside giant codebases or 1,000-page docs, Gemini 3.1 Pro's 1M token window changes the game. It's not a blowout either way; it's a genuine fork in the road.

Benchmarks

Astra's numbers are the headline: 74.1% on GPQA and 73.8% on DeepSWE for coding and agentic work. Gemini answers back on APEX-Agents with 33.5%, which is a strong agentic score of its own. They're both leading their neighborhoods, but they lead different neighborhoods. Pick based on the actual task, not the hype.

Context and price

Here's where the fork appears. Gemini 3.1 Pro gives you a 1M token context for $2/M input. That's a real advantage for swallowing entire repos or long legal docs at once. Astra charges $10/M input and $50/M output. No contest on price for huge input jobs. Astra isn't cheap, and the output price stings if you generate a lot.

For short, hard problems—agentic reasoning, complex math, tricky coding—Astra's raw benchmark lead justifies the premium for some teams. For batch processing, retrieval-heavy work, or anything that needs the whole document in view, Gemini's context window is the practical winner.

Verdict: which one should you pick?

Teams with tough reasoning workloads and a budget that tolerates $10/M input should grab GPT-6 Astra. Teams processing large corpora, doing heavy RAG, or running agent workflows across long histories should pick Gemini 3.1 Pro. If you're cost-sensitive and don't need 1M tokens, wait—there are cheaper mid-tier options. But this head-to-head isn't about cheap. It's about what you're actually trying to solve.