GPT-5.4 leads on coding benchmarks and has unique desktop computer-use capabilities. Gemini 3.1 Pro counters with a 1M-token context window (almost four times GPT-5.4's 272K), lower input pricing ($2 vs $2.50/1M) and stronger research performance. The choice comes down to your primary task: code and agent workflows favour GPT-5.4; research, large documents and cost efficiency favour Gemini 3.1 Pro.
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 74.9% on SWE-bench and has desktop computer-use for agentic coding. Gemini handles code but trails on the key coding 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 processing large research corpora in a single pass.
Context WindowGemini 3.1 Pro wins
Gemini 3.1 Pro supports about 1M tokens against GPT-5.4's 272K — almost four times the capacity for large document and codebase analysis.
PriceGemini 3.1 Pro wins
Gemini 3.1 Pro costs $2/1M input vs GPT-5.4's $2.50/1M, and $12 vs $15/1M output — meaningfully cheaper at scale.
Agentic TasksGPT-5.4 wins
GPT-5.4 is the only frontier model with desktop computer-use via API. Gemini has no equivalent agentic capability for software automation.
Which should you pick?
Pick GPT-5.4 if…
You're building agentic workflows that need desktop or browser control via API
Coding quality is your priority and you're already in the OpenAI ecosystem
You need the full OpenAI toolset: Assistants, plugins, function calling
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 wins for coding and agentic workflows. Gemini 3.1 Pro wins for research, large documents, and cost efficiency. Neither dominates across all use cases.
Which has the bigger context window?
Gemini 3.1 Pro: about 1M tokens, almost four times GPT-5.4's 272K. For large document analysis this is a decisive advantage.
Which is cheaper?
Gemini 3.1 Pro is cheaper: $2/1M input and $12/1M output vs GPT-5.4's $2.50/1M and $15/1M. At high volume, Gemini saves meaningful money.