GPT-5.4 vs Gemini 3.1 Flash-Lite
Head-to-head specifications
| Metric | GPT-5.4 | Gemini 3.1 Flash-Lite | Difference |
|---|---|---|---|
| Intelligence Index | 51.0 | 28.0 | +82.1% |
| Coding Index | 71.1 | 34.7 | +104.9% |
| Agentic Index | 41.1 | 6.2 | — |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.14 | $0.22 | +418.2% |
| Output speed (tokens/s) | 151 | 278 | -45.7% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.4 leads overall capability (Intelligence Index 51.0 vs 28.0).
- Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.2× cheaper.
Verdict: GPT-5.4 or Gemini 3.1 Flash-Lite?
GPT-5.4 advantages
- General intelligence (+45%)
- Coding ability (+51%)
- Agentic task performance (+85%)
Gemini 3.1 Flash-Lite advantages
- Affordability (+81%)
- Output speed (+46%)
Which should you choose?
- Choose the GPT-5.4 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.
- Choose the GPT-5.4 if coding and software development are your main workload.
Value for money
Gemini 3.1 Flash-Lite offers more intelligence per dollar (2.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.4 vs Gemini 3.1 Flash-Lite: which should you choose?
GPT-5.4 — OpenAI multimodal model with an Intelligence Index of 51, a 1M-token context window and a blended price of $1.14/1M tokens.
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.
GPT-5.4 vs Gemini 3.1 Flash-Lite: GPT-5.4 scores higher on the Intelligence Index. GPT-5.4 leads overall capability (Intelligence Index 51.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.4 scores 51.0 versus 28.0. For software development, the Coding Index puts GPT-5.4 ahead (71.1 vs 34.7). On agentic, multi-step tool-use tasks, GPT-5.4 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.4 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 151 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 $1.14 per 1M tokens). GPT-5.4 is proprietary api and Gemini 3.1 Flash-Lite 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 GPT-5.4 better than the Gemini 3.1 Flash-Lite?
GPT-5.4 takes the overall edge, though Gemini 3.1 Flash-Lite wins in specific areas worth weighing. GPT-5.4 leads overall capability (Intelligence Index 51.0 vs 28.0).
What is the main difference between the GPT-5.4 and the Gemini 3.1 Flash-Lite?
GPT-5.4 leads overall capability (Intelligence Index 51.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 5.2× cheaper.
Which is better value?
Gemini 3.1 Flash-Lite offers more intelligence per dollar (2.8× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.4 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.
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.