AI Model Comparison

Claude 4 Sonnet vs Gemini 3.1 Pro Preview

Verdict
Claude 4 Sonnet vs Gemini 3.1 Pro Preview: Gemini 3.1 Pro Preview scores higher on the Intelligence Index

Head-to-head specifications

MetricClaude 4 SonnetGemini 3.1 Pro PreviewDifference
Intelligence Index29.046.0-37.0%
Coding Index37.668.8-45.3%
Agentic Index16.621.4
Context window1M tokens1M tokens
Blended price ($/1M tokens)$1.20$0.91+31.9%
AccessProprietary APIProprietary API
  • Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 29.0).
  • Gemini 3.1 Pro Preview is the cheaper model to run at $0.91/1M blended tokens — about 1.3× cheaper.

Verdict: Claude 4 Sonnet or Gemini 3.1 Pro Preview?

Our recommendation
Gemini 3.1 Pro Preview is the clearly stronger overall choice, winning most of the dimensions that matter.

Claude 4 Sonnet advantages

  • No decisive advantage on the tracked metrics.

Gemini 3.1 Pro Preview advantages

  • General intelligence (+37%)
  • Coding ability (+45%)
  • Agentic task performance (+22%)
  • Affordability (+24%)

Which should you choose?

  • Choose the Gemini 3.1 Pro Preview if you need the strongest overall reasoning and accuracy.

Value for money

Gemini 3.1 Pro Preview offers more intelligence per dollar (2.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

Claude 4 Sonnet vs Gemini 3.1 Pro Preview: which should you choose?

Claude 4 Sonnet — Anthropic multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $1.2/1M tokens.

Gemini 3.1 Pro Preview — Google multimodal model with an Intelligence Index of 46, a 1M-token context window and a blended price of $0.91/1M tokens.

Claude 4 Sonnet vs Gemini 3.1 Pro Preview: Gemini 3.1 Pro Preview scores higher on the Intelligence Index. Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 29.0). Gemini 3.1 Pro Preview is the cheaper model to run at $0.91/1M blended tokens — about 1.3× cheaper.

Capability: intelligence, coding and agentic work

On the composite Intelligence Index the Gemini 3.1 Pro Preview scores 46.0 versus 29.0. For software development, the Coding Index puts Gemini 3.1 Pro Preview ahead (68.8 vs 37.6). On agentic, multi-step tool-use tasks, Gemini 3.1 Pro Preview measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.

Context window and speed

The Claude 4 Sonnet accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.

Pricing and access

At blended per-token rates, Gemini 3.1 Pro Preview is the cheaper model to run ($0.91 vs $1.20 per 1M tokens). Claude 4 Sonnet is proprietary api and Gemini 3.1 Pro Preview is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.

The verdict

Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.

Frequently asked questions

Is the Claude 4 Sonnet better than the Gemini 3.1 Pro Preview?

Gemini 3.1 Pro Preview is the clearly stronger overall choice, winning most of the dimensions that matter. Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 29.0).

What is the main difference between the Claude 4 Sonnet and the Gemini 3.1 Pro Preview?

Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 29.0). Gemini 3.1 Pro Preview is the cheaper model to run at $0.91/1M blended tokens — about 1.3× cheaper.

Which is better value?

Gemini 3.1 Pro Preview offers more intelligence per dollar (2.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

Which should I choose?

Choose the Gemini 3.1 Pro Preview if you need the strongest overall reasoning and accuracy.

Methodology

Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.

MC
Marcus Chen
Hardware & Product Analyst

Marcus benchmarks processors, GPUs, phones and vehicles and maintains normalized performance databases.

MSc Computer Engineering10+ years review experience
✓ Reviewed by Priya Nair, Data Quality Reviewer.
Last updated 2026-07-01
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