GPT-5 nano vs Claude 4.1 Opus
Head-to-head specifications
| Metric | GPT-5 nano | Claude 4.1 Opus | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 30.0 | -16.7% |
| Context window | 922K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.05 | $1.68 | -97.0% |
| Output speed (tokens/s) | 155 | 30 | +416.7% |
| Access | Proprietary API | Proprietary API | — |
- Claude 4.1 Opus leads overall capability (Intelligence Index 30.0 vs 25.0).
- GPT-5 nano is the cheaper model to run at $0.05/1M blended tokens — about 33.6× cheaper.
- GPT-5 nano offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5 nano or Claude 4.1 Opus?
GPT-5 nano advantages
- Context window (+72%)
- Affordability (+97%)
- Output speed (+81%)
Claude 4.1 Opus advantages
- General intelligence (+17%)
Which should you choose?
- Choose the GPT-5 nano if you work with long documents or large codebases.
- Choose the Claude 4.1 Opus if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5 nano if you want the lowest cost per token at scale.
Value for money
GPT-5 nano offers more intelligence per dollar (28.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5 nano vs Claude 4.1 Opus: which should you choose?
GPT-5 nano — OpenAI multimodal model with an Intelligence Index of 25, a 922K-token context window and a blended price of $0.05/1M 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 nano vs Claude 4.1 Opus: Claude 4.1 Opus scores higher on the Intelligence Index. Claude 4.1 Opus leads overall capability (Intelligence Index 30.0 vs 25.0). GPT-5 nano is the cheaper model to run at $0.05/1M blended tokens — about 33.6× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude 4.1 Opus scores 30.0 versus 25.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 nano 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 nano generates faster (155 vs 30 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5 nano is the cheaper model to run ($0.05 vs $1.68 per 1M tokens). GPT-5 nano 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 nano better than the Claude 4.1 Opus?
GPT-5 nano takes the overall edge, though Claude 4.1 Opus wins in specific areas worth weighing. Claude 4.1 Opus leads overall capability (Intelligence Index 30.0 vs 25.0).
What is the main difference between the GPT-5 nano and the Claude 4.1 Opus?
Claude 4.1 Opus leads overall capability (Intelligence Index 30.0 vs 25.0). GPT-5 nano is the cheaper model to run at $0.05/1M blended tokens — about 33.6× cheaper.
Which is better value?
GPT-5 nano offers more intelligence per dollar (28.0× 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 nano if you work with long documents or large codebases. Choose the Claude 4.1 Opus 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.