Claude Opus 4.7 led coding by SWE-Bench Pro (64.3%) when it launched and was the premium pick for agentic engineering work; it has since been replaced by Opus 4.8, Opus 5 and, in September 2026, Opus 5.5. Gemini 3.1 Pro wins on research depth and price ($2 vs $5/1M input); both read about 1M tokens. If your work is primarily coding or high-stakes reasoning, the Opus line is worth the premium — today that means Opus 5.5. For research-heavy and high-volume analytical work, Gemini 3.1 Pro is the cheaper buy.
AnthropicPremium
Claude Opus 4.7
Previous Opus flagship, now superseded by Claude Opus 4.8 at the same price.
VS
GooglePremium
Gemini 3.1 Pro
Best for research and deep document analysis — 2M context at the best premium price.
At a glance
Claude Opus 4.7
Gemini 3.1 Pro
Input cost / 1M tokens
$$5.00/1M
$$2.00/1M
Output cost / 1M tokens
$$25.00/1M
$$12.00/1M
Context window
1M tokens
2M tokens
Speed
Deliberate
Balanced
Price tier
Premium
Premium
Benchmarks
SWE-bench (coding)
87.6%
80.6%
Arena Elo
1,800
1,380
MMLU
92%
90%
How they compare
Which model wins for each use case — and why.
CodingClaude Opus 4.7 wins
Claude Opus 4.7 leads SWE-Bench Pro at 64.3% — the highest public score on the hardest coding benchmark. For autonomous engineering agents, nothing currently beats it.
ResearchGemini 3.1 Pro wins
Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 at launch and costs less per input token. For research-heavy document synthesis, Gemini is the stronger value.
Context WindowTie
Both support about 1M tokens. Earlier versions of this page said Gemini 3.1 Pro had 2M; Google's documentation lists 1M.
PriceGemini 3.1 Pro wins
Gemini 3.1 Pro at $2/$12 per 1M tokens vs Claude Opus 4.7's $5/$25. Gemini is 2.5× cheaper on input — a significant difference for high-volume work.
VisionClaude Opus 4.7 wins
Claude Opus 4.7 has strong vision capabilities with a 98.5% accuracy improvement in the latest generation. Gemini Pro also handles vision well but Claude's recent improvements give it the edge.
Which should you pick?
Pick Claude Opus 4.7 if…
You're running premium coding agents that need the highest SWE-Bench Pro score
High-stakes engineering work where quality ceiling matters more than cost
Against Gemini 3.1 Pro it costs about 53% more per token.
Anthropic's previous Opus flagship, now superseded by Opus 4.8. Still the second-best coding model publicly available at the same $5/$25 price.
Input
$5.00/1M
Output
$25.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Delegating difficult multi-file engineering work that needs careful verification
Running premium coding agents and autonomous PR review workflows
Reading large codebases, research corpora, or design references with 1M context
Where it wins
64.3% on SWE-Bench Pro, ahead of GPT-5.5 and GPT-5.4 in current public comparisons
1M context window for large codebases and document-heavy workflows
Strong vision and agentic consistency improvements over Opus 4.6
Where it falls down
Premium pricing is expensive for high-volume workloads
GPT-5.5 has stronger OpenAI ecosystem fit and faster Codex availability for some teams
Skip it if
You need cheaper high-volume throughput, image generation, or a workflow that must stay inside OpenAI tooling.
Our verdict
Superseded by Opus 4.8 (May 27, 2026) which scores 69.2% SWE-Bench Pro vs 64.3% here — at the same price. For existing pinned integrations Opus 4.7 still works well, but new deployments should use Opus 4.8.
Against Claude Opus 4.7 it costs about 53% less per token, takes 2x the context and answers faster.
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.
Claude Opus 4.7 leads on coding (SWE-Bench Pro 64.3%). Gemini 3.1 Pro leads on research (ARC-AGI-2) and is 2.5× cheaper on input. Both have since been followed by newer models; for coding today, compare Claude Opus 5.5.
Which is more affordable?
Gemini 3.1 Pro is significantly more affordable at $2/$12 per 1M tokens vs Claude Opus 4.7's $5/$25. For high-volume research work, Gemini is the cost-efficient choice.
Which has the bigger context window?
Neither — both support about 1M tokens. Earlier versions of this page said Gemini 3.1 Pro had 2M; Google's documentation lists 1M.