OpenAI: GPT-5.4 Nano

Review this Python function for edge cases and suggest defensive improvements. The function parses user-uploaded CSV files and extracts email addresses for a mailing list.

The function assumes well-formed CSV input but doesn't handle malformed rows, missing columns, or non-UTF-8 encodings. Add try-except blocks around the CSV reader to catch parsing errors. Validate email format with a regex before appending to the list—currently any string in the email column gets through. Consider adding a max-row limit to prevent memory issues with huge uploads. Also check for duplicate emails before adding them, and strip whitespace from each address. The current implementation will silently fail on files with BOM markers or mixed line endings.

This example shows GPT-5.4 Nano's ability to spot practical failure modes in production code. The 400k token context window means you can paste entire modules for review. However, without public benchmark data, we can't verify how it compares to GPT-4 or Claude on formal code-reasoning tasks.

I have 47 pages of meeting transcripts from our Q4 planning sessions. Summarise the three most contentious decisions, who advocated for each position, and what data points were cited.

The model would process the full transcript set and return a structured summary identifying: (1) the infrastructure vendor debate, with the CTO citing latency benchmarks favouring Option A while the CFO referenced three-year TCO projections favouring Option B; (2) the headcount allocation dispute between product and engineering leads, anchored on customer churn data versus sprint velocity metrics; (3) the go-to-market timing disagreement, where sales leadership pushed for Q1 launch using pipeline figures while product management advocated Q2 using beta feedback themes. Each decision summary would include direct quotes and page references.

The 400k token window handles long document sets that would require chunking in smaller models. This example highlights analytical synthesis across dense material. The trade-off: at $1.25/Mtok output, a detailed summary of this length costs more than equivalent models with tighter context windows.

Generate a patient-friendly explanation of how mRNA vaccines work, suitable for a hospital website. Include an analogy, address common safety concerns, and keep it under 200 words.

Think of mRNA vaccines as delivering a blueprint, not the actual virus. The vaccine contains instructions (mRNA) that teach your cells to make a harmless piece of the virus—like showing your immune system a wanted poster. Your cells read the instructions, build the harmless piece, then destroy the instructions. Your immune system spots the piece, learns to recognise it, and builds defenses. If the real virus shows up later, your body already knows how to fight it. The mRNA never enters your cell's nucleus where DNA lives, so it can't change your genetic code. It breaks down naturally within days. Side effects like sore arms or mild fever mean your immune system is training—that's normal and temporary. Serious reactions are rare and monitored closely. The technology has been studied for decades; COVID vaccines were developed quickly because of unprecedented funding and global collaboration, not skipped safety steps.

This demonstrates the model's ability to simplify technical concepts while maintaining accuracy and addressing audience concerns. The multimodal support means you could include diagrams in the same prompt. The nano designation suggests this is a smaller variant, though without benchmarks we can't confirm reasoning depth versus full GPT-5 models.

When 400k context makes contract review actually practical

A 4-person legal ops team at a SaaS company needs to compare vendor contracts against their standard terms before every renewal cycle. GPT-5.4 Nano fits here because the 400k token window holds 8-12 full contracts plus the master template in a single prompt—no chunking, no retrieval overhead, no context loss between comparisons. At $0.20 input per million tokens, loading 300k tokens of contract text costs $0.06 per analysis. The model handles image uploads, so scanned signature pages and redlined PDFs go straight in. If your team reviews fewer than 40 contracts per month, the $1.25/Mtok output rate stays under $15 total. Beyond that volume, you'll want a cheaper output tier, but for contract work where accuracy matters more than speed, this is the call.

Why a 12-person agency uses this for client mockup reviews

A digital agency runs 6-8 client projects concurrently, each generating 15-30 Figma exports per sprint for review. GPT-5.4 Nano's image modality lets the team upload mockups directly and get structured feedback on brand alignment, accessibility contrast, and layout consistency against the client's style guide (loaded as a 40k token reference doc). The 400k context holds the entire style guide plus 20+ screens in one session, so feedback stays coherent across pages. At current pricing, reviewing 200 screens per month costs roughly $8 in input tokens and $25 in output—cheaper than one billable hour. The lack of public benchmarks means you're flying blind on accuracy versus GPT-4o or Claude 3.5 Sonnet, but if your agency already trusts OpenAI's vision models, this is the lowest-friction way to scale design QA.

When ticket volume justifies the output cost premium

A 10-person B2B support team handles 400 inbound tickets daily, each needing classification (billing, technical, sales handoff) and a suggested first response. GPT-5.4 Nano works here if you're already paying for a premium model elsewhere and want to consolidate: the file modality ingests CSV exports of past tickets for few-shot learning, and the 400k window holds your entire knowledge base (typically 80-120k tokens) plus the last 50 tickets for pattern matching. Output cost is the constraint—at 400 tickets/day generating 200 tokens each, you're spending $10/day or $300/month on output alone. That pencils if it replaces a $40k/year L1 hire or cuts average handle time by 90 seconds. Below 150 tickets/day, switch to a model with cheaper output. Above 150, this is defensible if accuracy on your knowledge base beats the alternatives.