GPT-5 Codex vs Gemini 3.1 Flash-Lite
Head-to-head specifications
| Metric | GPT-5 Codex | Gemini 3.1 Flash-Lite | Difference |
|---|---|---|---|
| Intelligence Index | 36.0 | 28.0 | +28.6% |
| Context window | 922K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.78 | $0.22 | +254.5% |
| Output speed (tokens/s) | 191 | 278 | -31.3% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5 Codex leads overall capability (Intelligence Index 36.0 vs 28.0).
- Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 3.5× cheaper.
- Gemini 3.1 Flash-Lite offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5 Codex or Gemini 3.1 Flash-Lite?
GPT-5 Codex advantages
- General intelligence (+22%)
Gemini 3.1 Flash-Lite advantages
- Context window (+8%)
- Affordability (+72%)
- Output speed (+31%)
Which should you choose?
- Choose the GPT-5 Codex if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 3.1 Flash-Lite if you work with long documents or large codebases.
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 Codex vs Gemini 3.1 Flash-Lite: which should you choose?
GPT-5 Codex — OpenAI multimodal model with an Intelligence Index of 36, a 922K-token context window and a blended price of $0.78/1M 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 Codex vs Gemini 3.1 Flash-Lite: GPT-5 Codex scores higher on the Intelligence Index. GPT-5 Codex leads overall capability (Intelligence Index 36.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 3.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 Codex scores 36.0 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 191 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 $0.78 per 1M tokens). GPT-5 Codex 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 Codex better than the Gemini 3.1 Flash-Lite?
Gemini 3.1 Flash-Lite takes the overall edge, though GPT-5 Codex wins in specific areas worth weighing. GPT-5 Codex leads overall capability (Intelligence Index 36.0 vs 28.0).
What is the main difference between the GPT-5 Codex and the Gemini 3.1 Flash-Lite?
GPT-5 Codex leads overall capability (Intelligence Index 36.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 3.5× 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 Codex if you need the strongest overall reasoning and accuracy. Choose the Gemini 3.1 Flash-Lite if you work with long documents or large codebases.
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.