OpenAI: GPT-4.1 Nano
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
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Verdict
Best for
- High-volume document summarization
- Cost-sensitive chatbot backends
- Bulk code review and refactoring
- Long-context research synthesis
- Affordable vision tasks at scale
Strengths
The standout feature is the 1M+ token context window paired with aggressive pricing — you can feed entire codebases or multi-document archives without chunking. Vision support at this price point makes it viable for screenshot analysis or receipt parsing in production. Input cost is 33% lower than GPT-4o Mini, which adds up fast when you're processing millions of tokens daily. The model handles structured output and function calling, so it slots into existing OpenAI workflows without retooling.
Trade-offs
No public benchmarks means you're flying blind on reasoning quality relative to peers. Anecdotal reports suggest it trails GPT-4o Mini on complex logic and nuanced instruction-following — fine for summarization, less reliable for multi-step reasoning or creative writing. Output cost is higher than input, so chatty responses or code generation tasks eat into the savings. Vision quality likely lags GPT-4o; expect it to miss fine details in dense diagrams or low-contrast images.
Specifications
- Provider
- openai
- Category
- llm
- Context length
- 1,047,576 tokens
- Max output
- 32,768 tokens
- Modalities
- image, text, file
- License
- proprietary
- Released
- 2025-04-14
Pricing
- Input
- $0.10/Mtok
- Output
- $0.40/Mtok
- Model ID
openai/gpt-4.1-nano
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| openai | 1048k | $0.10/Mtok | $0.40/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Summarize Large Codebase
Review this codebase and produce a 500-word summary covering: main architecture patterns, key dependencies, areas of technical debt, and recommended refactoring priorities.Open in a Space →
Batch Support Ticket Triage
Classify each support ticket below by urgency (low/medium/high) and category (billing/technical/account). Return a JSON array with ticket_id, urgency, category, and a one-sentence summary.Open in a Space →
Extract Invoice Data
Extract all line items, totals, vendor name, and invoice date from this image. Return valid JSON with fields: vendor, date, line_items (array of {description, quantity, unit_price}), subtotal, tax, total.Open in a Space →Multi-Document Research Synthesis
Synthesize the key findings from these research papers into a 300-word executive summary. Highlight consensus views, conflicting results, and gaps in the literature.Open in a Space →
Affordable Code Refactoring
Refactor this code for readability and maintainability. Suggest variable renames, extract repeated logic into functions, and add docstrings. Preserve all functionality.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this Python function for edge cases. It's supposed to parse ISO timestamps but users report crashes on certain inputs.
The function assumes all timestamps include milliseconds, but ISO 8601 allows omission. When `datetime.fromisoformat()` receives '2024-01-15T10:30:00Z', it raises ValueError because the 'Z' suffix isn't handled. Add `.replace('Z', '+00:00')` before parsing. Also check for None inputs—currently no guard exists. The timezone-naive path at line 12 will fail if mixed with aware datetimes downstream. Consider enforcing UTC throughout or documenting the assumption clearly.
GPT-4.1 Nano's 1M+ token context window means it can hold entire codebases for review, catching integration issues across files that smaller models miss. The $0.10 input pricing makes exhaustive scans economical. Trade-off: without public benchmarks, teams can't compare its code-reasoning accuracy against alternatives like Claude or Gemini.
Summarise these 47 customer support tickets into themes, then draft a one-page memo for our product team explaining what to prioritise.
Three themes dominate: (1) Export failures when datasets exceed 50k rows—mentioned in 18 tickets, all enterprise accounts. (2) Confusion about the difference between 'archive' and 'delete'—12 tickets, mostly new users within first week. (3) Mobile app crashes on Android 12 when uploading images—9 tickets, started after our March release. Recommend prioritising the export bug (revenue risk), then clarifying UI copy (onboarding friction), then Android stability (affects 8% of mobile users but growing).
The massive context window handles dozens of tickets in one prompt without truncation or chunking, preserving cross-ticket patterns. At $0.40/Mtok output, long summaries stay affordable. Trade-off: image+file modality support is listed, but without benchmarks we can't verify how well it parses screenshots or CSV attachments compared to GPT-4o.
I'm attaching a 200-page RFP PDF. Extract all technical requirements, map them to our product capabilities, and flag gaps we'd need custom dev for.
The RFP lists 34 technical requirements across sections 4-7. Your product natively covers 28: SSO (req 4.1), audit logs (4.3), role-based access (5.2), and API rate-limiting (6.1) all match. Six gaps identified: (1) FIPS 140-2 encryption (req 4.7)—you use AES-256 but lack certification. (2) On-premise deployment (5.8)—cloud-only currently. (3) Custom SLA tiers below 99.9% (7.2)—your standard is 99.95%. (4) SAML attribute mapping beyond email/name (4.9). (5) Bi-directional Salesforce sync (6.5)—you only push data. (6) Multi-region data residency (7.6)—US/EU only. Estimated custom dev: 4-6 months for items 1, 2, 5.
File modality plus the 1M+ token window means the model can ingest entire RFPs, contracts, or technical specs without pre-processing. This example shows structured extraction across a long document—a task where context limits often force brittle chunking strategies. Trade-off: $0.40 output pricing adds up on verbose responses; teams should prompt for conciseness when possible.
Use-case deep-dives
Why GPT-4.1 Nano handles 500+ daily tickets without breaking budget
A 12-person SaaS support team routing 500-700 inbound tickets daily needs fast classification and draft responses that don't eat margin. GPT-4.1 Nano hits the sweet spot: $0.10/Mtok input means you can feed full ticket histories (email threads, past interactions, account context) into that 1M+ token window without watching costs spiral. Output at $0.40/Mtok stays reasonable when you're generating 200-word draft replies or routing tags. Compare this to older GPT-4 variants at $5-10/Mtok input—you'd burn $400/month just on context loading. The trade-off: if your tickets require deep reasoning (refund policy edge cases, multi-step troubleshooting), you'll want GPT-4o or Claude 3.5 Sonnet instead. But for straightforward triage, sentiment detection, and boilerplate drafting at scale, Nano's price-to-context ratio is hard to beat. Route it through Switchy and you'll see sub-$50/month costs even at this volume.
When to use GPT-4.1 Nano for cross-referencing 50-page agreements
A 4-person legal ops team needs to compare vendor MSAs against a master template, flagging deviations in liability caps, termination clauses, and data residency terms. GPT-4.1 Nano's 1M+ token context means you can load 3-4 full contracts plus your reference playbook in a single prompt—no chunking, no retrieval hacks, no context-window Tetris. At $0.10/Mtok input, a 200k-token comparison (roughly 150 pages of dense legal text) costs $0.02. You'll burn more on coffee. The catch: Nano lacks public benchmark data, so if your contracts hinge on obscure regulatory interpretation or require citation-level precision, test it against GPT-4o or Claude 3 Opus on a sample set first. For standard commercial agreements where the model is pattern-matching known clauses, Nano's combination of massive context and bottom-tier pricing makes it the default. Set up a Switchy workspace with contract templates as pinned context and let the team run comparisons in parallel.
Why GPT-4.1 Nano works for processing 200 scanned invoices weekly
A 3-person accounting firm digitizing client invoices—handwritten notes, faded receipts, multi-column tables—needs reliable OCR-plus-structuring without per-page SaaS fees. GPT-4.1 Nano's image modality and $0.40/Mtok output pricing mean you can feed a batch of 50 invoice images, extract line items into JSON, and pay under $2 for the entire run. The model handles mixed-quality scans and can cross-reference extracted totals against context you provide (client PO numbers, expected vendor lists). The boundary: if you're processing 1,000+ images daily or need sub-second latency, a specialized OCR API (Textract, Mindee) will outperform on speed and cost at that scale. But for weekly batches under 500 pages where you want flexible output formatting and the ability to tweak prompts per client, Nano's multimodal capability and low output cost beat stitching together separate OCR and LLM steps. Run it in Switchy with a shared prompt library so the team doesn't reinvent extraction logic every time.
Frequently asked
Is GPT-4.1 Nano good for general text tasks?
Yes, GPT-4.1 Nano handles everyday text work well — drafting emails, summarizing documents, answering questions. The 1M+ token context window means you can feed it entire codebases or long PDFs without chunking. It's OpenAI's smallest 4.1-series model, so expect slightly less nuanced reasoning than the full GPT-4.1, but for most business writing and research tasks it's more than capable.
Is GPT-4.1 Nano cheaper than Claude Sonnet?
Yes, significantly. At $0.10 input and $0.40 output per million tokens, GPT-4.1 Nano costs about 75% less than Claude 3.5 Sonnet ($3/$15 per Mtok). If you're processing high volumes of text or running batch jobs, the savings add up fast. The trade-off is you lose Sonnet's stronger reasoning on complex tasks, but for straightforward work the price difference is hard to ignore.
Can GPT-4.1 Nano handle image analysis?
Yes, it supports image and file inputs alongside text. You can upload screenshots, diagrams, or PDFs and ask questions about them. The vision capabilities are solid for document extraction, chart reading, and basic visual Q&A. It won't match specialized vision models for fine-grained image understanding, but for mixed-media workflows where you need text and images in one context, it works cleanly.
How does GPT-4.1 Nano compare to GPT-4o Mini?
GPT-4.1 Nano sits between GPT-4o Mini and the full GPT-4.1 in capability and price. It's more expensive than 4o Mini ($0.15/$0.60 vs $0.10/$0.40 per Mtok) but offers the newer 4.1 architecture. Without public benchmarks it's hard to quantify the gap, but if you've hit 4o Mini's ceiling on reasoning tasks and don't want to pay for full GPT-4.1, Nano is the logical step up.
Should I use GPT-4.1 Nano for customer support chatbots?
Yes, it's a strong fit. The low latency of the Nano tier keeps conversations feeling responsive, and the pricing makes high-volume chat affordable. The 1M token context means you can load entire help docs or ticket histories into each session without external RAG. For support workflows that need multimodal input — like users uploading error screenshots — the image support is a practical advantage over text-only models.