GPT-5.2 wins on coding (85 vs 80) and price ($1.75 vs $2/1M input). Gemini 3.1 Pro wins on context window (2M vs 200K). For most workflows, GPT-5.2 is the stronger default — capable but outclassed — gpt-5.4 is now cheaper and better.
The safest GPT-5.2 vs Gemini 3.1 Pro default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.2 is the strongest answer here for GPT-5.2 vs Gemini 3.1 Pro — pick it when quality of output matters more than the $1.75/1M/1M input you pay for it.
Gemini 3.1 Pro handles the same job for about 11% less per token. Start here and only move up if the output is not good enough.
GPT-5.2 is the fastest of these for GPT-5.2 vs Gemini 3.1 Pro — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.2 leads on coding with a score of 85 vs 80 for Gemini 3.1 Pro.
Gemini 3.1 Pro has the larger context window: 2M vs 200K for GPT-5.2.
GPT-5.2 is cheaper at $1.75/1M input tokens vs $2/1M for Gemini 3.1 Pro.
Go with GPT-5.2 if you want one model to handle coding and research — it targets serious coding and complex product work.
Choose Gemini 3.1 Pro when your work is mostly research and deep document analysis — that is the workload it was tuned for.
Both models serve different primary workflows — GPT-5.2 for serious coding and complex product work, Gemini 3.1 Pro for research and deep document analysis — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the GPT-5.2 vs Gemini 3.1 Pro answer changes when cost, speed, or long-document depth leads the decision.
Google / Premium / Sep 2, 2026
Best for research and deep document analysis — 2M context at the best premium price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your primary use case is writing quality or agentic coding — Claude wins both.
Where the GPT-5.2 vs Gemini 3.1 Pro recommendation shifts once you weigh price or latency differently.
Capable but outclassed — GPT-5.4 is now cheaper and better.
Best for research and deep document analysis — 2M context at the best premium price.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-5.2OpenAI | $1.75/1M | $14.00/1M | $46 | 200k tokens | Balanced | 85 | 82 | 84 |
| Gemini 3.1 ProGoogle | $2.00/1M | $12.00/1M | $44 | 2M tokens | Balanced | 80 | 82 | 99 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GPT-5.2 vs Gemini 3.1 Pro, what it is genuinely good at, and where we would steer you away from it.
Ranked first here for GPT-5.2 vs Gemini 3.1 Pro: 85/100 on coding, with the widest margin of anything in this line-up.
Reliable OpenAI flagship for serious coding and product work — a strong default before GPT-5.4 was released.
You're starting a new project — GPT-5.4 is cheaper and more capable.
Still capable but GPT-5.4 at $2.50/1M input is now cheaper and better. Hard to justify GPT-5.2 at $12/1M for new projects.
Full pricing, benchmark table and release notes on the GPT-5.2 page.
The cost-conscious pick for GPT-5.2 vs Gemini 3.1 Pro, about 11% less per token than GPT-5.2 than the top choice while holding 80/100 on coding.
Google's flagship with the largest context window of any frontier model at 2M tokens, Deep Think reasoning, and the best price-to-performance among premium models.
Your primary use case is writing quality or agentic coding — Claude wins both.
The best research and long-context model available. Handles entire codebases, legal documents, and large datasets in a single pass — at a lower price than GPT-5.4 or Claude Sonnet 4.6.
Full pricing, benchmark table and release notes on the Gemini 3.1 Pro page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Newsletter
We email when the GPT-5.2 vs Gemini 3.1 Pro pick changes, when one of these models moves on price, or when something new displaces the current leader.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
GPT-5.2 wins on more of the categories we score — coding, research, reasoning — so it is the better default of the two. Gemini 3.1 Pro is the better pick when your work is mostly research and deep document analysis. Neither is universally "better": GPT-5.2 is aimed at serious coding and complex product work, Gemini 3.1 Pro at research and deep document analysis.
GPT-5.2 is cheaper at $1.75/1M input and $14/1M output. Gemini 3.1 Pro costs $2/1M input and $12/1M output.
Gemini 3.1 Pro has the larger context window at 2M tokens vs GPT-5.2's 200K. For large document analysis, Gemini 3.1 Pro is the stronger pick.
GPT-5.2 is better for coding with a score of 85 vs Gemini 3.1 Pro's 80 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Both GPT-5.2 and Gemini 3.1 Pro have similar speed profiles — rated balanced. Neither will be the bottleneck if latency is your deciding factor.
Superseded by GPT-5.4 for most use cases. Claude Sonnet 4.6 leads on both coding and writing quality. Avoid it if you're starting a new project — GPT-5.4 is cheaper and more capable. That is the main case for looking at Gemini 3.1 Pro instead.
Slower than Flash for everyday lightweight tasks. Claude Sonnet 4.6 is better for writing quality. Avoid it if your primary use case is writing quality or agentic coding — Claude wins both. Against GPT-5.2 specifically, the gap shows up most on coding (85 vs 80).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-5.2 runs $45.50 (at $1.75/1M in and $14/1M out); Gemini 3.1 Pro runs $44.00 (at $2/1M in and $12/1M out). That is a $1.50/month difference — Gemini 3.1 Pro is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-5.2 for serious coding and complex product work, with Gemini 3.1 Pro handling research and deep document analysis. Since GPT-5.2 is both the stronger and the cheaper option here, a split mainly makes sense if Gemini 3.1 Pro covers a capability you specifically need.