GPT-5.4 leads on coding benchmarks and has unique desktop computer-use capabilities. Gemini 3.1 Pro counters with a 2M token context window (7× larger), lower input pricing ($2 vs $2.50/1M), and stronger research benchmark performance. The choice comes down to your primary task: code and agent workflows favor GPT-5.4; research, large documents, and cost efficiency favor Gemini 3.1 Pro.
OpenAIPremium
GPT-5.4
Best for agentic automation and desktop control workflows.
VS
GooglePremium
Gemini 3.1 Pro
Best for research and deep document analysis — 2M context at the best premium price.
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
2M 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 leads ARC-AGI-2 at 77.1% and has a 2M token context window for processing large research corpora in a single pass.
Context WindowGemini 3.1 Pro wins
Gemini 3.1 Pro supports 2M tokens vs GPT-5.4's 272K — a 7× advantage 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 latest flagship with unique desktop-control capabilities — it can see your screen, click, and navigate apps via the API.
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
Claude Opus 4.7 and GPT-5.5 now outperform it on current premium coding benchmarks
Smaller context window (272K) vs Gemini 3.1 Pro (2M) for research
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
Best choice when you need a model that can operate software autonomously at the older GPT-5.4 price tier. For current premium coding quality, Claude Opus 4.7 leads.
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 7x the context.
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.
Input
$2.00/1M
Output
$12.00/1M
Context
2M tokens
Speed
Balanced
What people actually use it for
Analyzing entire contracts, codebases, or research corpora in a single 2M-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
2M token context window — the largest of any frontier model
Leads ARC-AGI-2 reasoning benchmark at 77.1%
Best price-to-performance among premium models at $2/$12 per 1M tokens
Where it falls down
Slower than Flash for everyday lightweight tasks
Claude Sonnet 4.6 is better for writing quality
Skip it if
Your primary use case is writing quality or agentic coding — Claude wins both.
Our verdict
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.
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 has a 2M token context window — 7× larger than 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.