GPT-5.4 (ChatGPT) and Gemini 3.1 Pro both sit in the premium tier but serve different strengths. GPT-5.4 wins on coding and has unique desktop-control capabilities for agentic workflows. Gemini 3.1 Pro wins on research depth, context window (1M vs 272K tokens) and price ($2 vs $2.50/1M input). If you write more code than documents, go GPT. If you analyse more documents than you write code, go Gemini.
OpenAIPremium
GPT-5.4
Best for agentic automation and desktop control workflows.
VS
GooglePremium
Gemini 3.1 Pro
Google's Pro model — 1M context and native multimodal input at $2/$12.
At a glance
GPT-5.4
Gemini 3.1 Pro
Input cost / 1M tokens
$$2.50/1M
$$2.00/1M
Output cost / 1M tokens
$$15.00/1M
$$12.00/1M
Context window
272k tokens
1M tokens
Speed
Balanced
Balanced
Price tier
Premium
Premium
Benchmarks
SWE-bench (coding)
74.9%
80.6%
Arena Elo
1,355
1,380
MMLU
91%
90%
How they compare
Which model wins for each use case — and why.
CodingGPT-5.4 wins
GPT-5.4 scores higher on coding benchmarks and has unique computer-use API capabilities for agentic coding workflows. Gemini 3.1 Pro handles code but doesn't lead on benchmarks.
ResearchGemini 3.1 Pro wins
Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 at launch and has a 1M-token context window for large document synthesis. GPT-5.4's 272K context limits research depth significantly.
Context WindowGemini 3.1 Pro wins
Gemini 3.1 Pro's 1M-token window is almost four times GPT-5.4's 272K. For processing large codebases, legal corpora or research documents in one pass, Gemini wins clearly.
Agentic TasksGPT-5.4 wins
GPT-5.4 is the only frontier model that can control a desktop via API — clicking, typing, and navigating software. This makes it uniquely suited for agentic automation workflows.
PriceGemini 3.1 Pro wins
Gemini 3.1 Pro costs $2/1M input vs GPT-5.4's $2.50/1M. Output is also cheaper at $12/1M vs $15/1M. At scale, Gemini is the more cost-efficient premium model.
Which should you pick?
Pick GPT-5.4 if…
You need agentic workflows that control a desktop or browser via API
Coding is your primary use case and you want the strongest benchmark scores
You're already using OpenAI's API and ecosystem tools
You need multimodal capabilities including image analysis and generation
Against Gemini 3.1 Pro it costs about 20% more per token.
OpenAI's March 2026 flagship with desktop-control capabilities — it can see your screen, click, and navigate apps via the API. Since followed by GPT-5.5, the GPT-5.6 family and GPT-6.
Input
$2.50/1M
Output
$15.00/1M
Context
272k tokens
Speed
Balanced
What people actually use it for
Building agents that browse the web and operate desktop software autonomously via the API
Complex multi-step reasoning for financial modeling and decision analysis
Autonomous test-run-debug loops for coding with computer-use control
Where it wins
Only frontier model that can control a desktop via API (click, type, navigate)
Strong at multi-step agentic tasks and autonomous workflows
Competitive coding performance with 74.9% SWE-bench score
Where it falls down
Newer models — Claude Opus 5.5, GPT-6.1 Sol — outperform it on coding benchmarks
Smaller context window (272K) than current 1M-token models
Skip it if
You need the highest current coding benchmark scores — Claude Opus 4.7 and GPT-5.5 are newer premium picks.
Our verdict
Still capable for desktop automation at its $2.50/$15 price, but superseded: GPT-6.1 Sol is stronger at $2/$10, and Claude Opus 5.5 leads coding.
Full pricing, benchmark table and release notes on the GPT-5.4 page.
Against GPT-5.4 it costs about 20% less per token and takes 4x the context.
Google's Pro-tier Gemini 3 model, released in preview on February 19, 2026, with a 1M-token context window, Deep Think reasoning and $2/$12 pricing.
Input
$2.00/1M
Output
$12.00/1M
Context
1M tokens
Speed
Balanced
What people actually use it for
Analyzing long contracts, codebases, or research papers in a single 1M-token prompt
Due diligence synthesis across large sets of financial documents or legal agreements
Multi-step reasoning across dense technical specifications with Deep Think mode
Where it wins
1M-token context window with native image, audio, video and PDF input
Scored 77.1% on ARC-AGI-2 at its February 2026 launch
$2/$12 per 1M tokens — cheaper output than most premium models
Where it falls down
Slower than Flash for everyday lightweight tasks
Claude Sonnet 5.5 is better for writing quality, at a lower $2/$10
Skip it if
Your primary use case is writing quality or agentic coding — Claude wins both.
Our verdict
A capable research model and still Google's current Pro tier, but no longer the long-context leader: its 1M-token window matches Claude Opus 5.5 and is slightly below the GPT-6 family's 1.05M. Choose it for Google Workspace integration and multimodal input; for most research, Claude Opus 5.5 or GPT-6.1 Sol are stronger at a similar price.
GPT-5.4 is better for coding and agentic automation. Gemini 3.1 Pro is better for research, large documents, and cost-efficiency. Neither is objectively better — it depends on your use case.
Which is better for coding — ChatGPT or Gemini?
GPT-5.4 leads on coding benchmarks and uniquely supports desktop computer-use via API. For coding-first workflows, ChatGPT is the stronger pick.
Which is cheaper — ChatGPT or Gemini?
Gemini 3.1 Pro is cheaper: $2/1M input and $12/1M output vs GPT-5.4's $2.50/1M input and $15/1M output. Gemini is meaningfully cheaper at high volume.
Which AI has a bigger context window — ChatGPT or Gemini?
Gemini 3.1 Pro has a 1M-token context window against GPT-5.4's 272K — almost four times larger. For large document analysis, Gemini is the better option of the two.
What does ChatGPT do that Gemini can't?
GPT-5.4 can control a desktop computer via API — it's the only frontier model with this capability. For agentic automation that needs to interact with software, nothing else competes.