Gemini 3 Flash vs GPT-5.5
Head-to-head specifications
| Metric | Gemini 3 Flash | GPT-5.5 | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 55.0 | -45.5% |
| Context window | 1M tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.39 | $1.54 | -74.7% |
| Output speed (tokens/s) | 179 | 67 | +167.2% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 30.0).
- Gemini 3 Flash is the cheaper model to run at $0.39/1M blended tokens — about 3.9× cheaper.
Verdict: Gemini 3 Flash or GPT-5.5?
Gemini 3 Flash advantages
- Affordability (+75%)
- Output speed (+63%)
GPT-5.5 advantages
- General intelligence (+45%)
Which should you choose?
- Choose the Gemini 3 Flash if you want the lowest cost per token at scale.
- Choose the GPT-5.5 if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 3 Flash if low latency and fast generation matter for your application.
Value for money
Gemini 3 Flash offers more intelligence per dollar (2.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 3 Flash vs GPT-5.5: which should you choose?
Gemini 3 Flash — Google multimodal model with an Intelligence Index of 30, a 1M-token context window and a blended price of $0.39/1M tokens.
GPT-5.5 — OpenAI multimodal model with an Intelligence Index of 55, a 1M-token context window and a blended price of $1.54/1M tokens.
Gemini 3 Flash vs GPT-5.5: GPT-5.5 scores higher on the Intelligence Index. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 30.0). Gemini 3 Flash is the cheaper model to run at $0.39/1M blended tokens — about 3.9× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.5 scores 55.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 3 Flash 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 Flash generates faster (179 vs 67 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Gemini 3 Flash is the cheaper model to run ($0.39 vs $1.54 per 1M tokens). Gemini 3 Flash is proprietary api and GPT-5.5 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 Flash better than the GPT-5.5?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 30.0).
What is the main difference between the Gemini 3 Flash and the GPT-5.5?
GPT-5.5 leads overall capability (Intelligence Index 55.0 vs 30.0). Gemini 3 Flash is the cheaper model to run at $0.39/1M blended tokens — about 3.9× cheaper.
Which is better value?
Gemini 3 Flash offers more intelligence per dollar (2.2× 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 Flash if you want the lowest cost per token at scale. Choose the GPT-5.5 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.