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OpenAI: GPT-4.1
GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...
Specifications
- Provider
- openai
- Category
- llm
- Context length
- 1,047,576 tokens
- Max output
- —
- Modalities
- image, text, file
- License
- LicenseRef-OpenAI-Commercial
- Released
- 2025-04-14
Pricing
- Input
- $2.00/Mtok
- Output
- $8.00/Mtok
- Model ID
openai/gpt-4.1
Team cost calculator
Estimated monthly spend
$66.88
17.6M tokens / month
5 seats · 80 msgs/day
5 seats · 80 msgs/day
Verdict
GPT-4.1 is what you reach for when context length actually matters. A million tokens is still rare in practice — and when you do need it, GPT-4.1 is the one that holds together.
Strong coding, good reasoning, competitive pricing (~$2/Mtok in). The trade-off is character: it's agreeable where Sonnet is blunt, which means you sometimes have to ask twice for the disagreement you wanted the first time.
Best for: dumping large codebases or long transcripts in and asking for the takeaway; quick production scripts where you want function-calling to just work; work that benefits from the OpenAI ecosystem (image gen in the same turn, voice via the same vendor).
Avoid for: nuanced editorial writing (Sonnet has better taste); architecture disagreements (Opus pushes back harder); anything where you need the model to tell you "no, that's the wrong approach" without being coaxed into it.
Pricing frame: at $2 in / $8 out per Mtok, a 5-person team at 200 msgs/day lands around $65/month. Among frontier models, this is the best cost per context-length-window.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| openai | 1048k | $2.00/Mtok | $8.00/Mtok | — | — | — |
Performance
Performance snapshots are collected daily. Check back after the next ingestion run.
Benchmarks
Works well with
Top MCPs
How Switchy teams use it
Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.