Gemini 3.1 Pro wins on coding (80 vs 58) and writing quality and price ($2 vs $2.5/1M input) and context window (2M vs 128K). For most workflows, Gemini 3.1 Pro is the stronger default — best for research and deep document analysis — 2m context at the best premium price.
The safest GPT-4o vs Gemini 3.1 Pro default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Gemini 3.1 Pro is the strongest answer here for GPT-4o vs Gemini 3.1 Pro — pick it when quality of output matters more than the $2.00/1M/1M input you pay for it.
GPT-4o handles the same job for about 11% less per token. Start here and only move up if the output is not good enough.
GPT-4o is the fastest of these for GPT-4o vs Gemini 3.1 Pro — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Gemini 3.1 Pro leads on coding with a score of 80 vs 58 for GPT-4o.
Gemini 3.1 Pro has the larger context window: 2M vs 128K for GPT-4o.
Gemini 3.1 Pro is cheaper at $2/1M input tokens vs $2.5/1M for GPT-4o.
Choose Gemini 3.1 Pro for research, deep document analysis, and long-context reasoning at competitive pricing. Its research and long context scores are what carry the recommendation here.
Choose GPT-4o when your work is mostly multimodal tasks and image-adjacent workflows — that is the workload it was tuned for.
Both models serve different primary workflows — Gemini 3.1 Pro for research and deep document analysis, GPT-4o for multimodal tasks and image-adjacent workflows — 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-4o 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-4o vs Gemini 3.1 Pro recommendation shifts once you weigh price or latency differently.
Best all-around pick for image-heavy and multimodal workflows.
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 |
|---|---|---|---|---|---|---|---|---|
| Gemini 3.1 ProGoogle | $2.00/1M | $12.00/1M | $44 | 2M tokens | Balanced | 80 | 82 | 99 |
| GPT-4oOpenAI | $2.50/1M | $10.00/1M | $45 | 128k tokens | Fast | 58 | 76 | 65 |
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-4o vs Gemini 3.1 Pro, what it is genuinely good at, and where we would steer you away from it.
The default answer for GPT-4o vs Gemini 3.1 Pro — 99/100 on the research axis, and the model we would start with unless the price below rules it out.
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.
The value option for GPT-4o vs Gemini 3.1 Pro: about 11% less per token than Gemini 3.1 Pro, at 65/100 on research. Worth starting here and moving up only if the output disappoints.
Versatile multimodal model that handles image-related workflows and mixed-media prompts well.
You need the latest reasoning or coding performance — GPT-5.4 replaces it for serious work.
Still a reliable multimodal pick, especially for teams already in the OpenAI ecosystem.
Full pricing, benchmark table and release notes on the GPT-4o page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Gemini 3.1 Pro wins on more of the categories we score — research, long context, reasoning — so it is the better default of the two. GPT-4o is the better pick when your work is mostly multimodal tasks and image-adjacent workflows. Neither is universally "better": Gemini 3.1 Pro is aimed at research and deep document analysis, GPT-4o at multimodal tasks and image-adjacent workflows.
Gemini 3.1 Pro is cheaper at $2/1M input and $12/1M output. GPT-4o costs $2.5/1M input and $10/1M output.
Gemini 3.1 Pro has the larger context window at 2M tokens vs GPT-4o's 128K. For large document analysis, Gemini 3.1 Pro is the stronger pick.
Gemini 3.1 Pro is better for coding with a score of 80 vs GPT-4o's 58 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
GPT-4o is faster with a fast speed rating (score: 4) vs Gemini 3.1 Pro's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the Gemini 3.1 Pro latency penalty is usually invisible.
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. That is the main case for looking at GPT-4o instead.
Outclassed by newer models on pure coding and reasoning. Gemini 3.1 Pro now handles multimodal at a lower price. Avoid it if you need the latest reasoning or coding performance — GPT-5.4 replaces it for serious work. Against Gemini 3.1 Pro specifically, the gap shows up most on coding (80 vs 58).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-4o runs $45.00 (at $2.5/1M in and $10/1M out); Gemini 3.1 Pro runs $44.00 (at $2/1M in and $12/1M out). That is a $1.00/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 Gemini 3.1 Pro for research and deep document analysis, with GPT-4o handling multimodal tasks and image-adjacent workflows. Since Gemini 3.1 Pro is both the stronger and the cheaper option here, a split mainly makes sense if GPT-4o covers a capability you specifically need.