Claude Opus 4.6 vs Gemini 3.1 Pro Preview

Choose Claude Opus 4.6 if you want…

• ✓A comparable all-rounder — they trade blows on the headline metrics.

Choose Gemini 3.1 Pro Preview if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Faster output (~132.6 tokens/sec)
• ✓Lower price ($4.5 / 1M blended)
• ✓Larger context window (1.0M)

Claude Opus 4.6

Opus 4.6 is the model researchers and engineers reach for when the problem genuinely cannot be chunked — loading an entire codebase, a year's worth of literature, or a complex multi-part investigation into a single session of up to 750,000 words. It tops Terminal-Bench 2.0 among frontier models for agentic coding tasks and leads BrowseComp for hard-to-locate information retrieval, reflecting a design philosophy built around sustained, autonomous work rather than quick exchanges. Scientists have noted roughly double the accuracy on computational biology and structural chemistry tasks versus its predecessor. The tradeoff is speed: at 38.8 tokens per second, it feels noticeably slower than alternatives during interactive back-and-forth. The 1M-token window is also still in beta, and users report meaningful performance degradation well before hitting its ceiling. Best suited to high-stakes tasks where depth matters more than pace.

Gemini 3.1 Pro Preview

At the top of the Artificial Analysis Intelligence Index — ahead of every other model evaluated — Gemini 3.1 Pro Preview earns its ranking not just on raw ability but on the economics of getting there. It ran a full benchmark suite at less than half the cost of comparable frontier models, which makes it the clearest answer to the question of whether top-tier intelligence requires top-tier spend. A 38-percentage-point drop in hallucination rate over its predecessor and a 94.3% GPQA Diamond score in graduate-level scientific reasoning make it a serious tool for complex research, deep software engineering, and agentic workflows that need to get things right. Its 1-million-token context window handles entire codebases or lengthy document sets without batching. The honest caveat: time-to-first-token averages nearly 25 seconds, well above the median for comparable models, and some developers report extended waits or reliability issues under high API load. If your work is iterative and deep rather than real-time and conversational, the trade-off is usually worth it.