GPT-5.5 is the stronger OpenAI coding and agentic workflow pick. Gemini 3.1 Pro is the cheaper research option, with lower input pricing; both have a 1M-token context window. Choose GPT-5.5 for coding agents and OpenAI-native workflows. Choose Gemini 3.1 Pro for cost-sensitive research and document synthesis, especially inside Google tools.
OpenAIPremium
GPT-5.5
Best OpenAI flagship for agentic coding, research, and computer-use work.
VS
GooglePremium
Gemini 3.1 Pro
Best for research and deep document analysis — 2M context at the best premium price.
At a glance
GPT-5.5
Gemini 3.1 Pro
Input cost / 1M tokens
$$5.00/1M
$$2.00/1M
Output cost / 1M tokens
$$30.00/1M
$$12.00/1M
Context window
1M tokens
2M tokens
Speed
Balanced
Balanced
Price tier
Premium
Premium
Benchmarks
SWE-bench (coding)
—
80.6%
Arena Elo
—
1,380
MMLU
—
90%
How they compare
Which model wins for each use case — and why.
Coding agentsGPT-5.5 wins
GPT-5.5 is OpenAI's newest premium coding and agentic model, with strong SWE-Bench Pro and Terminal-Bench results.
Research contextTie
Both models have a 1M-token context window, so neither has a capacity advantage for very large corpora.
PriceGemini 3.1 Pro wins
Gemini 3.1 Pro is cheaper per input token in the catalog, making it easier to justify for high-volume research workloads.
EcosystemTie
GPT-5.5 wins for OpenAI/Codex workflows. Gemini wins for Google Workspace and long-context Google-native use cases.
Against Gemini 3.1 Pro it costs about 60% more per token.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
Input
$5.00/1M
Output
$30.00/1M
Context
1M tokens
Speed
Balanced
What people actually use it for
Running multi-file implementation and debugging loops in Codex
Building agents that research, operate tools, and verify work over long tasks
Analyzing large business, scientific, or technical documents with 1M context
Where it wins
58.6% on SWE-Bench Pro, ahead of GPT-5.4 on the same public coding benchmark
82.7% on Terminal-Bench 2.0 for complex command-line workflows
1M token API context window for large-codebase and document-heavy workflows
Where it falls down
Claude Opus 4.7 leads GPT-5.5 on SWE-Bench Pro for pure coding ceiling
Premium API pricing makes it less attractive for high-volume low-risk work
Skip it if
You only care about the highest public coding benchmark score or need a cheaper high-volume model.
Our verdict
The strongest OpenAI pick for agentic coding and knowledge work. Claude Opus 4.7 still wins on the public SWE-Bench Pro coding number, but GPT-5.5 is the better OpenAI default when ecosystem, Codex, or computer-use workflows matter.
Full pricing, benchmark table and release notes on the GPT-5.5 page.
Against GPT-5.5 it costs about 60% less per token and takes 2x 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-5.5 is better for OpenAI-native coding and agent workflows. Gemini 3.1 Pro is better for very large research inputs and lower-cost long-context processing.
Which has the larger context window?
Neither — both are listed at about 1M tokens. Earlier versions of this page said Gemini 3.1 Pro had 2M; Google's documentation lists 1M.
Which is better for coding?
GPT-5.5 is the better choice for coding agents and OpenAI/Codex workflows. Gemini can code, but this pairing favors GPT-5.5 for engineering work.