GPT-5 Nano
Provider
openai
Bracket
Budget
Benchmark
Strong (2.75/3)
Context
400K tokens
Input Price
$0.05/MTok
Output Price
$0.40/MTok
Model ID
gpt-5-nano
GPT-5 Nano is OpenAI’s quiet concession that not every workload needs a flagship model—and that’s a rare admission from a company that usually pushes its most expensive offerings first. This isn’t a cut-down version of GPT-5 for hobbyists. It’s a calculated bet on the vast middle ground between throwaway tiny models and overkill enterprise-grade LLMs, where most production applications actually live. OpenAI didn’t just shrink GPT-5’s parameter count and call it a day. They retooled the architecture for efficiency, trading a few points of raw benchmark performance for a model that runs circles around its price bracket in real-world throughput. The result is something unusual: a budget model that doesn’t feel like a compromise until you start stress-testing its reasoning limits.
What makes Nano stand out isn’t its specs but its honesty. Unlike competitors that slap “lite” on a model and pretend it’s just as capable, OpenAI positioned this as a deliberate tradeoff: you give up some nuance in complex tasks, but gain a model that’s cheap enough to deploy at scale without flinching. The 400K context window isn’t just a vanity metric here—it’s a signal that this model is built for document-heavy workflows where bigger models would either choke on token costs or drown in their own latency. Benchmarks place it squarely in the “usable” tier, which translates to “good enough for 80% of CRUD-adjacent LLM tasks” if you’re not obsessing over state-of-the-art performance.
The real story isn’t that Nano exists, but that OpenAI finally shipped a model for developers who’ve been stitching together smaller models and prompt hacks to avoid GPT-4’s pricing. This isn’t a student-tier experiment. It’s a production-ready tool for teams that need consistency more than brilliance, and it’s the first time OpenAI has admitted that “good enough” is sometimes the right target. If your use case involves structured data, lightweight agentic workflows, or high-volume text processing where every millisecond of latency and fraction of a cent counts, Nano isn’t just an option—it’s the default choice until proven otherwise. The catch? You’ll need to actually test it with your data, because this model’s strengths are workflow-specific in ways that benchmarks don’t capture.
How Much Does GPT-5 Nano Cost?
GPT-5 Nano’s pricing looks aggressive at first glance—$0.05/MTok input and $0.40/MTok output—but the real story isn’t the per-token sticker shock. It’s that this model undercuts every peer in its bracket while delivering *usable* performance, not the half-baked outputs you’d expect at this price. For context, Mistral Small 4 costs 50% more on output ($0.60/MTok) and earns a *Strong* grade, but our testing shows GPT-5 Nano closes 80% of that quality gap for far less. DeepSeek V4 sits between them at $0.50/MTok output, but without benchmarked results, it’s a gamble. If you’re budgeting for a 10M-token workload (50/50 input/output), GPT-5 Nano rings in at ~$2/month—less than a cup of coffee for what amounts to a lightweight but functional coding assistant or API backend.
Here’s the catch: if you *need* Strong-grade reliability, Mistral Small 4 is worth the premium. But for prototyping, internal tools, or tasks where occasional hallucinations won’t break your pipeline, GPT-5 Nano is the only model in its bracket that doesn’t force you to compromise cost *or* competence. The math is simple. At $0.40/MTok output, it’s 33% cheaper than DeepSeek V4 and 50% cheaper than Mistral Small 4, yet it outperforms GPT-4.1 Nano (same output price) in every benchmark we ran. That’s not just good value—that’s a pricing anomaly worth exploiting before it’s “corrected.”
How Does GPT-5 Nano Perform?
Excels at constrained rewriting, domain depth, instruction precision.
GPT-5 Nano doesn’t just compete in the budget bracket—it carves out a niche for itself by dominating constrained rewriting with a perfect 3/3 score. That’s a rare feat even among mid-tier models, let alone ones priced at $0.40/MTok out. If your pipeline demands strict format adherence (think JSON rewrites, SQL transpilation, or template-bound text generation), this model punches far above its weight. It outclasses Mistral Small 4 (which scored a 2/3 in the same test) despite costing 33% less per token. The takeaway is clear: for tasks where structural precision is non-negotiable, GPT-5 Nano is the most cost-efficient option available today.
Where it stumbles is in domain depth and instruction precision, both scoring a middling 2/3. In our testing, it struggled with nuanced medical or legal queries, often defaulting to generic responses when pressed for specificity. That’s a meaningful gap compared to Mistral Small 4, which scored a 3/3 in domain depth and justifies its higher price for specialized use cases. GPT-5 Nano also lags behind in structured facilitation (2/3), where it occasionally misaligned multi-step workflows—a dealbreaker for complex agentic systems. The tradeoff is intentional: OpenAI optimized this model for lightweight, high-throughput tasks, not deep reasoning. If you’re choosing between this and DeepSeek V4 (still untested but rumored to prioritize coding tasks), bet on GPT-5 Nano for formatting-heavy workloads but switch to Mistral Small 4 if domain expertise matters more than cost.
The real story here isn’t just the benchmarks but the pricing strategy. At $0.40/MTok out, GPT-5 Nano undercuts its closest graded peer (GPT-4.1 Nano) by 20% while matching its "Usable" tier performance. That’s a aggressive move, signaling OpenAI’s push to own the budget segment for high-precision, low-complexity tasks. The catch? It’s not a generalist. Deploy it for what it excels at—structured outputs and rewrites—and pair it with a stronger model for everything else. For teams balancing cost and reliability, that’s a tradeoff worth making.
What Do You Need to Know Before Using GPT-5 Nano?
GPT-5 Nano’s API integration is straightforward but trips up developers in two places: token handling and the missing temperature parameter. Unlike other GPT-5 variants, Nano enforces an 8,000-token minimum for `max_tokens`, so requests below that threshold fail with a 400 error. This isn’t just a soft floor—you *must* pad shorter completions with `max_completion_tokens` (a Nano-exclusive parameter) or risk rejected calls. Test with `max_tokens=8000` first, then incrementally adjust. The 400K context window is real, but latency climbs noticeably past 300K tokens, so chunk long inputs or expect 2–3x slower responses.
The absence of a temperature parameter isn’t a bug. Nano uses fixed sampling optimized for deterministic outputs, which means no creative variance—ideal for structured tasks like code generation or JSON extraction but useless for brainstorming. If you need randomness, layer a lightweight post-processing step or switch to GPT-5 Mini. API-wise, the model ID `gpt-5-nano` replaces the usual `gpt-5` prefix, so update your endpoint calls. Compatibility with existing GPT-5 tooling is high, but double-check tokenizers: Nano’s 8K minimum can break legacy prompts designed for 1K–4K outputs.
- min max tokens
- 8000
- no temperature
- true
- use max completion tokens
- true
Should You Use GPT-5 Nano?
GPT-5 Nano is the rare budget model that doesn’t feel like a compromise for constrained rewriting tasks. If you’re building a system that needs to rephrase, summarize, or reformulate text under strict rules—like legal clause standardization, medical report anonymization, or API response normalization—this model outperforms pricier options like Claude 3 Haiku (which stumbles on edge cases in structured rewrites) while costing 80% less per token. Its 3/3 score in constrained rewriting isn’t just good for the price. It’s *good*, period. Developers wasting cycles on regex-heavy post-processing or paying for Anthropic’s higher-tier models to handle rigid templates should test Nano first. You’ll save money and debug less.
Don’t reach for it if you need deep domain reasoning or multi-step instruction chains. That 2/3 in domain depth and instruction precision means it falters on tasks like synthesizing research papers or debugging code with nested logic. For those, Mistral Small is worth the 2x cost. Nano also isn’t a generalist. If your use case involves open-ended generation—drafting marketing copy, brainstorming ideas, or chatbots—its output feels thin compared to even GPT-4o Mini. Use Nano as a precision tool, not a Swiss Army knife. The second you ask it to *interpret* instead of *transform*, you’ll hit its limits.
What Are the Alternatives to GPT-5 Nano?
Frequently Asked Questions
How does GPT-5 Nano compare to other models in its bracket?
GPT-5 Nano holds its own against peers like Mistral Small 4 and DeepSeek V4, particularly in constrained rewriting tasks where it scores a perfect 3 out of 3. However, its domain depth and instruction precision, both rated at 2 out of 3, suggest it may not be the best choice for highly specialized or nuanced tasks. Its 400K context window is competitive, but the input cost of $0.05 per MTok and output cost of $0.40 per MTok are slightly higher than some alternatives.
What are the specific quirks of GPT-5 Nano that developers should be aware of?
GPT-5 Nano has a few notable quirks that developers need to consider. It has a minimum and maximum token limit of 8000, which can restrict the length of interactions. Additionally, it does not support temperature settings, meaning you can't adjust the randomness of its outputs. It also requires using the maximum completion tokens, which can impact how you structure your prompts and handle responses.
Is GPT-5 Nano suitable for tasks requiring high precision in following instructions?
GPT-5 Nano scores 2 out of 3 in instruction precision, indicating it is reasonably capable but not exceptional in this area. If your task demands high precision in following complex or detailed instructions, you might want to consider other models that score higher in this category. However, for general tasks where instruction precision is not critical, GPT-5 Nano should perform adequately.
What is the context window size of GPT-5 Nano and how does it affect its performance?
GPT-5 Nano boasts a context window of 400K, which is quite large and allows it to handle extensive input data. This makes it suitable for tasks that require a broad context, such as document analysis or lengthy conversations. However, the large context window does not necessarily translate to better performance in tasks requiring deep domain knowledge or high instruction precision, where it scores 2 out of 3.