Gemini 3.1 Pro Preview vs Claude 4 Sonnet
Head-to-head specifications
| Metric | Gemini 3.1 Pro Preview | Claude 4 Sonnet | Difference |
|---|---|---|---|
| Intelligence Index | 46.0 | 29.0 | +58.6% |
| Coding Index | 68.8 | 37.6 | +83.0% |
| Agentic Index | 21.4 | 16.6 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.91 | $1.20 | -24.2% |
| Access | Proprietary API | Proprietary 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: Gemini 3.1 Pro Preview or Claude 4 Sonnet?
Gemini 3.1 Pro Preview advantages
- General intelligence (+37%)
- Coding ability (+45%)
- Agentic task performance (+22%)
- Affordability (+24%)
Claude 4 Sonnet advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the Gemini 3.1 Pro Preview if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 3.1 Pro Preview if coding and software development are your main workload.
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.
Gemini 3.1 Pro Preview vs Claude 4 Sonnet: which should you choose?
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 — 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 vs Claude 4 Sonnet: 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 Gemini 3.1 Pro Preview 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). Gemini 3.1 Pro Preview is proprietary api and Claude 4 Sonnet 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 Gemini 3.1 Pro Preview better than the Claude 4 Sonnet?
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 Gemini 3.1 Pro Preview and the Claude 4 Sonnet?
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. Choose the Gemini 3.1 Pro Preview if coding and software development are your main workload.
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.