Gemini 3.1 Pro vs Claude Opus 4.6: Which is Better in 2026?
Gemini 3.1 Pro just took the top spot on the Artificial Analysis Intelligence Index. Claude Opus 4.6 held that position previously. Now that the benchmark rankings have shifted, which model you should actually use depends heavily on what you are doing — the two models have distinct strengths at the same high price tier.
Last updated: February 2026
Our Pick
Gemini 3.1 Pro
Gemini 3.1 Pro wins on benchmark intelligence (AA Index 57 vs 53), abstract reasoning (ARC-AGI-2: 77.1% vs 68.8%), agentic performance (APEX-Agents: 33.5% vs 29.8%), context window (1M vs 200K tokens), and price ($4.50 vs $10/1M blended). Claude Opus 4.6 wins on enterprise expert tasks (GDPval-AA Elo 1606 vs 1317 — a 300-point gap), long-horizon tool-assisted research (Humanity's Last Exam with tools: 53.1% vs 51.4%), and blind user preference (Chatbot Arena). If you are building agentic systems, processing large documents, or working with scientific problems, Gemini 3.1 Pro is the better and cheaper choice. If you are doing high-stakes professional work — legal, financial, or complex expert analysis — Claude Opus 4.6 still leads on the benchmarks that matter most for those tasks.
Try Gemini 3.1 ProAt a glance
| Feature | Gemini 3.1 Pro | Claude Opus 4.6 |
|---|---|---|
| Rating | 9.0 / 10 | 6.6 / 10 |
| Provider | Anthropic | |
| Context window | 1M tokens | 200K tokens |
| Input (per 1M tokens) | $2 | $5 |
| Output (per 1M tokens) | $12 | $25 |
| Multimodal | Yes | Yes |
| Open source | No | No |
Use case breakdown
AA Intelligence Index: 57 (Gemini 3.1 Pro) vs 53 (Opus 4.6). Gemini now leads in the independently measured composite of 10 standard benchmarks.
ARC-AGI-2: 77.1% vs 68.8%. Gemini 3.1 Pro leads by 8 points on novel logic puzzles — the benchmark most resistant to training contamination.
APEX-Agents: 33.5% vs 29.8%. BrowseComp: 85.9% vs 84.0%. MCP Atlas: 69.2% vs 59.5%. Gemini leads consistently across long-horizon agentic task benchmarks.
GDPval-AA Elo: 1606 (Opus 4.6) vs 1317 (Gemini 3.1 Pro) — a 289-point gap. This benchmark measures performance on real professional tasks in finance, legal, and expert domains. Claude leads by a meaningful margin.
In Chatbot Arena blind user voting, Opus 4.6 consistently outperforms Gemini 3.1 Pro on quality of prose, nuance, and instruction-following. Users prefer Claude's outputs on open-ended tasks.
$4.50/1M blended (Gemini 3.1 Pro) vs $10.00/1M (Claude Opus 4.6). Gemini is more than twice as cheap at the API tier. At scale, this difference is decisive.
1M tokens (Gemini 3.1 Pro) vs 200K (Claude Opus 4.6). A 5× difference. For large codebases, long documents, or multi-document synthesis, there is no comparison.
FAQ
Is Gemini 3.1 Pro better than Claude Opus 4.6?
On most benchmarks, yes — it scores higher on the Artificial Analysis Intelligence Index (57 vs 53), leads on abstract reasoning and agentic tasks, and costs less than half as much. But Claude Opus 4.6 leads on enterprise expert tasks (GDPval-AA) and blind user preference voting. The right answer depends on your use case.
Which is cheaper, Gemini 3.1 Pro or Claude Opus 4.6?
Gemini 3.1 Pro is significantly cheaper. At $2/$12 per 1M tokens (≤200K context), the blended cost is $4.50/1M. Claude Opus 4.6 is $5/$25 per 1M tokens — a $10/1M blended cost. Gemini is more than twice as cheap per token.
Does Gemini 3.1 Pro have a bigger context window than Claude Opus 4.6?
Yes — 1,048,576 tokens (Gemini 3.1 Pro) vs 200,000 (Claude Opus 4.6). Five times larger. For tasks involving very large documents, full codebases, or extensive conversation history, Gemini 3.1 Pro has a substantial structural advantage.
Which should I use for coding?
Gemini 3.1 Pro is strong on competitive coding (LiveCodeBench Pro Elo 2887 vs 2393 for GPT-5.2; Claude Opus not directly measured). For agentic software engineering tasks, both Gemini 3.1 Pro (SWE-Bench: 80.6%) and Opus 4.6 (SWE-Bench: 80.8%) are essentially tied. Gemini's dedicated custom-tools endpoint and 1M context window give it a practical edge for coding in large codebases.