Gemini 3.1 Flash-Lite vs o3
Head-to-head specifications
| Metric | Gemini 3.1 Flash-Lite | o3 | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 31.0 | -9.7% |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.22 | $0.90 | -75.6% |
| Output speed (tokens/s) | 278 | 115 | +141.7% |
| Access | Proprietary API | Proprietary API | — |
- o3 leads overall capability (Intelligence Index 31.0 vs 28.0).
- Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 4.1× cheaper.
- Gemini 3.1 Flash-Lite offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Gemini 3.1 Flash-Lite or o3?
Gemini 3.1 Flash-Lite advantages
- Context window (+74%)
- Affordability (+76%)
- Output speed (+59%)
o3 advantages
- General intelligence (+10%)
Which should you choose?
- Choose the Gemini 3.1 Flash-Lite if you work with long documents or large codebases.
- Choose the o3 if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 3.1 Flash-Lite if you want the lowest cost per token at scale.
Value for money
Gemini 3.1 Flash-Lite offers more intelligence per dollar (3.7× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 3.1 Flash-Lite vs o3: 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.
o3 — OpenAI multimodal model with an Intelligence Index of 31, a 256K-token context window and a blended price of $0.9/1M tokens.
Gemini 3.1 Flash-Lite vs o3: o3 scores higher on the Intelligence Index. o3 leads overall capability (Intelligence Index 31.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 4.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the o3 scores 31.0 versus 28.0. 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. In measured throughput, Gemini 3.1 Flash-Lite generates faster (278 vs 115 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Gemini 3.1 Flash-Lite is the cheaper model to run ($0.22 vs $0.90 per 1M tokens). Gemini 3.1 Flash-Lite is proprietary api and o3 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 o3?
Gemini 3.1 Flash-Lite takes the overall edge, though o3 wins in specific areas worth weighing. o3 leads overall capability (Intelligence Index 31.0 vs 28.0).
What is the main difference between the Gemini 3.1 Flash-Lite and the o3?
o3 leads overall capability (Intelligence Index 31.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 4.1× cheaper.
Which is better value?
Gemini 3.1 Flash-Lite offers more intelligence per dollar (3.7× 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 work with long documents or large codebases. Choose the o3 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.