OpenAI's GPT-5.4 and Google's Gemini 3.1 Pro are neck-and-neck on the Artificial Analysis Intelligence Index â both scoring 57. For the first time in the AI race, there is no clear winner. The real question is which model wins for your workflow.
Here's everything you need to know, backed by independent benchmarks from March 2026.
The Headline Numbers
Full Benchmark Comparison
| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Winner |
|---|---|---|---|
| SWE-bench Verified (coding) | 71.7% | 80.6% | đĸ Gemini |
| HumanEval (code gen) | 96.2% | 94.5% | đĸ GPT |
| GPQA Diamond (science) | 92.8% | 94.3% | đĸ Gemini |
| ARC-AGI-2 (reasoning) | 73.3% | 77.1% | đĸ Gemini |
| GDPval (professional) | 83.0% | ~79% | đĸ GPT |
| OSWorld (computer use) | 75.0% | N/A | đĸ GPT |
| BrowseComp (web research) | â | 85.9% | đĸ Gemini |
| Terminal-Bench 2.0 | 75.1% | 68.5% | đĸ GPT |
Gemini 3.1 Pro dominates on reasoning and science benchmarks. GPT-5.4 leads on professional tasks and desktop automation. In coding, it depends on the benchmark â Gemini wins the harder SWE-bench while GPT edges ahead on HumanEval.
Context Window and Output
- 1.05M token input (272K default)
- 32,000 token output
- Text + image input
- Native DALL-E image generation
- Computer Use (mouse + keyboard control)
- 2M token input (Enterprise)
- 65,536 token output
- Text + image + video + audio input
- Up to 1 hour of video per prompt
- Up to 8.4 hours of audio per prompt
Gemini 3.1 Pro's 2 million token window is a genuine advantage for document-heavy and multimodal workflows. If you need to analyze an entire codebase, a 90-minute board meeting recording, or a stack of PDFs â Gemini handles it natively. GPT-5.4's default 272K context is sufficient for most tasks, but the full 1M window costs extra.
Gemini also doubles GPT on output: 65K tokens versus 32K. That matters for long-form code generation and document drafting.
Pricing Breakdown
| GPT-5.4 | Gemini 3.1 Pro | |
|---|---|---|
| Input (per 1M tokens) | $2.50 | $2.00 |
| Output (per 1M tokens) | $15.00 | $12.00 |
| Consumer plan | $20/mo (Plus) | $20/mo (Advanced) |
| Pro tier | $200/mo | $250/mo (Ultra) |
What GPT-5.4 Does That Gemini Can't
The biggest differentiator is Computer Use. GPT-5.4 can autonomously control a desktop â clicking buttons, filling forms, navigating between apps. Its 75% score on OSWorld means it outperforms the average human professional at basic computer tasks.
This isn't a gimmick. It's the beginning of AI that doesn't just generate work but does work: filing expense reports, updating CRM records, scheduling meetings across multiple tools.
GPT-5.4 also reduced factual errors by 33% compared to GPT-5.2, making it the more reliable model for high-stakes professional output.
What Gemini 3.1 Pro Does That GPT Can't
Gemini's native multimodal processing is unmatched. Upload an hour of video, 8 hours of audio, or thousands of pages of documents â and get analysis in a single prompt. No chunking, no workarounds.
Its BrowseComp score of 85.9% makes it the strongest model for web research tasks, and its reasoning advantage on ARC-AGI-2 (77.1% vs 73.3%) suggests deeper abstract thinking capabilities.
- Gemini 3.1 Pro processes up to **1 hour of video** natively
- Its 2M token context window is **double** GPT-5.4's maximum
- It scored **80.6%** on SWE-bench Verified â the harder coding benchmark
- Still in Preview; general availability expected Q2 2026
Where Does Claude 4.6 Fit?
Anthropic's Claude 4.6, released February 5, 2026, briefly topped leaderboards before both GPT-5.4 and Gemini 3.1 Pro surpassed it. Claude remains the strongest choice for creative writing and nuanced conversation, but it trails on raw benchmarks.
For a full three-way breakdown, see our AI model comparison roundup.
Which Should You Choose?
Bottom line: Use GPT-5.4 if you need an AI that can operate software autonomously or handle high-stakes professional work. Use Gemini 3.1 Pro if you work with large documents, video, or audio â or if you want the best reasoning at a lower price.
The AI race is no longer about which model is "smarter." It's about which model fits your workflow. For the first time, choosing wrong doesn't mean choosing badly â it just means leaving performance on the table.
