GPT-5 mini vs Claude 4.1 Opus
Head-to-head specifications
| Metric | GPT-5 mini | Claude 4.1 Opus | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 30.0 | +6.7% |
| Context window | 922K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.26 | $1.68 | -84.5% |
| Output speed (tokens/s) | 93 | 30 | +210.0% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 30.0).
- GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 6.5× cheaper.
- GPT-5 mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5 mini or Claude 4.1 Opus?
GPT-5 mini advantages
- General intelligence (+6%)
- Context window (+72%)
- Affordability (+85%)
- Output speed (+68%)
Claude 4.1 Opus advantages
- No decisive advantage on the tracked metrics.
Which should you choose?
- Choose the GPT-5 mini if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5 mini if you work with long documents or large codebases.
Value for money
GPT-5 mini offers more intelligence per dollar (6.9× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5 mini vs Claude 4.1 Opus: which should you choose?
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.
Claude 4.1 Opus — Anthropic multimodal model with an Intelligence Index of 30, a 262K-token context window and a blended price of $1.68/1M tokens.
GPT-5 mini vs Claude 4.1 Opus: GPT-5 mini scores higher on the Intelligence Index. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 30.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 6.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 mini scores 32.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 mini 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 mini generates faster (93 vs 30 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 $1.68 per 1M tokens). GPT-5 mini is proprietary api and Claude 4.1 Opus 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 mini better than the Claude 4.1 Opus?
GPT-5 mini is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 30.0).
What is the main difference between the GPT-5 mini and the Claude 4.1 Opus?
GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 30.0). GPT-5 mini is the cheaper model to run at $0.26/1M blended tokens — about 6.5× cheaper.
Which is better value?
GPT-5 mini offers more intelligence per dollar (6.9× 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 mini if you need the strongest overall reasoning and accuracy. Choose the GPT-5 mini 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.