GPT-5.1 Codex mini vs Gemini 2.5 Pro (May)
Head-to-head specifications
| Metric | GPT-5.1 Codex mini | Gemini 2.5 Pro (May) | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 27.0 | +18.5% |
| Context window | 922K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.37 | $0.86 | -57.0% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5.1 Codex mini leads overall capability (Intelligence Index 32.0 vs 27.0).
- GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 2.3× cheaper.
- Gemini 2.5 Pro (May) offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.1 Codex mini or Gemini 2.5 Pro (May)?
GPT-5.1 Codex mini advantages
- General intelligence (+16%)
- Affordability (+57%)
Gemini 2.5 Pro (May) advantages
- Context window (+8%)
Which should you choose?
- Choose the GPT-5.1 Codex mini if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 2.5 Pro (May) if you work with long documents or large codebases.
- Choose the GPT-5.1 Codex mini if you want the lowest cost per token at scale.
Value for money
GPT-5.1 Codex mini 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-5.1 Codex mini vs Gemini 2.5 Pro (May): which should you choose?
GPT-5.1 Codex mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.37/1M tokens.
Gemini 2.5 Pro (May) — Google multimodal model with an Intelligence Index of 27, a 1M-token context window and a blended price of $0.86/1M tokens.
GPT-5.1 Codex mini vs Gemini 2.5 Pro (May): GPT-5.1 Codex mini scores higher on the Intelligence Index. GPT-5.1 Codex mini leads overall capability (Intelligence Index 32.0 vs 27.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 2.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.1 Codex mini scores 32.0 versus 27.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 2.5 Pro (May) 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, GPT-5.1 Codex mini is the cheaper model to run ($0.37 vs $0.86 per 1M tokens). GPT-5.1 Codex mini is proprietary api and Gemini 2.5 Pro (May) 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-5.1 Codex mini better than the Gemini 2.5 Pro (May)?
GPT-5.1 Codex mini takes the overall edge, though Gemini 2.5 Pro (May) wins in specific areas worth weighing. GPT-5.1 Codex mini leads overall capability (Intelligence Index 32.0 vs 27.0).
What is the main difference between the GPT-5.1 Codex mini and the Gemini 2.5 Pro (May)?
GPT-5.1 Codex mini leads overall capability (Intelligence Index 32.0 vs 27.0). GPT-5.1 Codex mini is the cheaper model to run at $0.37/1M blended tokens — about 2.3× cheaper.
Which is better value?
GPT-5.1 Codex mini 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-5.1 Codex mini if you need the strongest overall reasoning and accuracy. Choose the Gemini 2.5 Pro (May) 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.