DeepSeek V4 Pro vs Gemini 3.1 Pro Preview

Choose DeepSeek V4 Pro if you want…

• ✓Lower price ($2.17 / 1M blended)

Choose Gemini 3.1 Pro Preview if you want…

• ✓Higher intelligence (Artificial Analysis index 57)
• ✓Faster output (~132.6 tokens/sec)

DeepSeek V4 Pro

DeepSeek V4 Pro makes a compelling case that frontier-class coding performance and a one-million-token context window do not have to cost frontier-class money. At roughly $0.18 per million tokens blended, it runs 10x cheaper on input and 30x cheaper on output than comparable models, while posting an 80.6% score on SWE-Bench Verified — the highest reported among open-weight models at launch. Users consistently praise its agentic coding ability, noting it competes with or beats larger closed models on multi-step coding tasks, and its hybrid attention architecture handles full-codebase analysis without collapsing under the token budget. The MIT license is a genuine differentiator: weights are freely available for self-hosting, fine-tuning, and commercial integration. The honest caveat: V4 Pro is verbose. It can generate four to five times more output tokens than comparable models on the same prompt, which erodes the per-token savings and makes cost estimation harder than it first appears. Still in preview as of mid-2026, with all benchmark scores currently vendor-reported, it is best suited for teams comfortable with that tradeoff.

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.