Gemini 3.1 Flash-Lite vs Claude 4 Sonnet
Head-to-head specifications
| Metric | Gemini 3.1 Flash-Lite | Claude 4 Sonnet | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 29.0 | -3.4% |
| Coding Index | 34.7 | 37.6 | -7.7% |
| Agentic Index | 6.2 | 16.6 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.22 | $1.20 | -81.7% |
| Access | Proprietary API | Proprietary API | — |
- Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 28.0).
- Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.5× cheaper.
Verdict: Gemini 3.1 Flash-Lite or Claude 4 Sonnet?
Gemini 3.1 Flash-Lite advantages
- Affordability (+82%)
Claude 4 Sonnet advantages
- Coding ability (+8%)
- Agentic task performance (+63%)
Which should you choose?
- Choose the Gemini 3.1 Flash-Lite if you want the lowest cost per token at scale.
- Choose the Claude 4 Sonnet if coding and software development are your main workload.
Value for money
Gemini 3.1 Flash-Lite offers more intelligence per dollar (5.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 3.1 Flash-Lite vs Claude 4 Sonnet: which should you choose?
Gemini 3.1 Flash-Lite — Google multimodal model with an Intelligence Index of 28, a 1M-token context window and a blended price of $0.22/1M 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 Flash-Lite vs Claude 4 Sonnet: Claude 4 Sonnet scores higher on the Intelligence Index. Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude 4 Sonnet scores 29.0 versus 28.0. For software development, the Coding Index puts Claude 4 Sonnet ahead (37.6 vs 34.7). On agentic, multi-step tool-use tasks, Claude 4 Sonnet measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 3.1 Flash-Lite 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 Flash-Lite is the cheaper model to run ($0.22 vs $1.20 per 1M tokens). Gemini 3.1 Flash-Lite 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 Flash-Lite better than the Claude 4 Sonnet?
Claude 4 Sonnet takes the overall edge, though Gemini 3.1 Flash-Lite wins in specific areas worth weighing. Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 28.0).
What is the main difference between the Gemini 3.1 Flash-Lite and the Claude 4 Sonnet?
Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.5× cheaper.
Which is better value?
Gemini 3.1 Flash-Lite offers more intelligence per dollar (5.3× 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 Flash-Lite if you want the lowest cost per token at scale. Choose the Claude 4 Sonnet 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.