GPT-5 vs GPT-5 mini
Head-to-head specifications
| Metric | GPT-5 | GPT-5 mini | Difference |
|---|---|---|---|
| Intelligence Index | 35.0 | 32.0 | +9.4% |
| Coding Index | 37.8 | 15.6 | +142.3% |
| Agentic Index | 25.7 | 19.4 | — |
| Context window | 922K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.79 | $0.26 | +203.8% |
| Output speed (tokens/s) | 99 | 93 | +6.5% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5 leads overall capability (Intelligence Index 35.0 vs 32.0).
- GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 3.0× cheaper.
Verdict: GPT-5 or GPT-5 mini?
GPT-5 advantages
- General intelligence (+9%)
- Coding ability (+59%)
- Agentic task performance (+25%)
- Output speed (+6%)
GPT-5 mini advantages
- Affordability (+67%)
Which should you choose?
- Choose the GPT-5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5 mini if you want the lowest cost per token at scale.
- Choose the GPT-5 if coding and software development are your main workload.
Value for money
GPT-5 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 vs GPT-5 mini: which should you choose?
GPT-5 — OpenAI multimodal model with an Intelligence Index of 35, a 922K-token context window and a blended price of $0.79/1M tokens.
GPT-5 mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.26/1M tokens.
GPT-5 vs GPT-5 mini: GPT-5 scores higher on the Intelligence Index. GPT-5 leads overall capability (Intelligence Index 35.0 vs 32.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 3.0× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 scores 35.0 versus 32.0. For software development, the Coding Index puts GPT-5 ahead (37.8 vs 15.6). On agentic, multi-step tool-use tasks, GPT-5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5 generates faster (99 vs 93 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5 mini is the cheaper model to run ($0.26 vs $0.79 per 1M tokens). GPT-5 is proprietary api and GPT-5 mini 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 better than the GPT-5 mini?
GPT-5 takes the overall edge, though GPT-5 mini wins in specific areas worth weighing. GPT-5 leads overall capability (Intelligence Index 35.0 vs 32.0).
What is the main difference between the GPT-5 and the GPT-5 mini?
GPT-5 leads overall capability (Intelligence Index 35.0 vs 32.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 3.0× cheaper.
Which is better value?
GPT-5 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 if you need the strongest overall reasoning and accuracy. Choose the GPT-5 mini 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.