Gemini 3.1 Pro Preview vs GLM 5.1

Choose Gemini 3.1 Pro Preview if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Faster output (~132.6 tokens/sec)
• ✓Larger context window (1.0M)

Choose GLM 5.1 if you want…

• ✓Lower price ($2.15 / 1M blended)

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.

GLM 5.1

GLM-5.1 from Z.ai is built for one thing above all else: software engineering that runs on its own. A 754-billion parameter Mixture-of-Experts model, it tops the SWE-Bench Pro leaderboard at 58.4%, edging out both GPT-5.4 and Claude Opus 4.6 on real-world coding tasks. What sets it apart in practice is stamina — it can pursue a single engineering goal autonomously for up to eight hours, sustaining hundreds of iterations and thousands of tool calls without human intervention. Users consistently praise this long-horizon execution for agent-based workflows where other models stall. It also delivers fast responses, with a time-to-first-token of 1.33 seconds against a class median of 2.37 seconds. The honest trade-off: GLM-5.1 accepts text only, with no image input, making it a poor fit for visual debugging or UI-centric tasks. It also tends toward verbosity in practice, which can inflate token costs. For teams building autonomous coding pipelines, though, it earns its place at the top of the leaderboard.