// head_to_head
GPT-5.4 Nano vs Qwen3.8 27B
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Qwen3.8 27B scores higher, GPT-5.4 Nano costs less — it depends on your workload.
Qwen3.8 27B is ahead by 5.7 points overall, and GPT-5.4 Nano lists 2.5× cheaper per blended million tokens. Whether 5.7 points is worth that depends on how much a wrong answer costs you.
openai
GPT-5.4 Nano
- Blended / 1M
- $0.463
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 69.6
qwen
Qwen3.8 27B
- Blended / 1M
- $1.14
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 75.3
Specs and pricing
| Metric | GPT-5.4 Nano | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 69.6 | 75.3win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0500 | $0.0556 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.463win | $1.14 |
| Input price / 1M | $0.200win | $0.450 |
| Output price / 1M | $1.25win | $3.20 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.050 |
| Context window | 400K | 1Mwin |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.4 Nano on top, Qwen3.8 27B below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | GPT-5.4 Nano | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $135.04/mo | $335.20/mo |
| RAG assistant 8K in / 600 out × 100K requests | $163.00/mo | $392.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $159.20/mo | $392.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $284.75/mo | $670.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $535.00/mo | $1245.00/mo |
Which should you pick?
Quality matters more than the bill
Qwen3.8 27B
Highest overall LiveBench score of the two at 75.3.
The workload is coding or agentic work
Qwen3.8 27B
Leads on agentic coding — 61.4 against 46.8.
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1M against 400K.
You are cost-constrained
GPT-5.4 Nano
Cheaper on blended list price at $0.463 per million tokens.
GPT-5.4 Nano vs Qwen3.8 27B FAQ
Which is better, GPT-5.4 Nano or Qwen3.8 27B?
Qwen3.8 27B scores higher, GPT-5.4 Nano costs less — it depends on your workload. Qwen3.8 27B is ahead by 5.7 points overall, and GPT-5.4 Nano lists 2.5× cheaper per blended million tokens. Whether 5.7 points is worth that depends on how much a wrong answer costs you.
Is GPT-5.4 Nano cheaper than Qwen3.8 27B?
GPT-5.4 Nano is cheaper. On a 3:1 input:output blend, GPT-5.4 Nano lists at $0.463 per million tokens and Qwen3.8 27B at $1.14 — GPT-5.4 Nano is 2.5× cheaper. Input and output are priced separately — GPT-5.4 Nano charges $0.200 in and $1.25 out, Qwen3.8 27B charges $0.450 and $3.20 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-5.4 Nano vs Qwen3.8 27B: which scores higher on benchmarks?
GPT-5.4 Nano scores 69.6 and Qwen3.8 27B scores 75.3 overall on LiveBench, the mean of its seven categories. That is a 5.7-point lead for Qwen3.8 27B. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, GPT-5.4 Nano or Qwen3.8 27B?
They are close. GPT-5.4 Nano costs $0.0500 per point of overall capability and Qwen3.8 27B costs $0.0556, a difference small enough that workload shape will matter more than the rate.
Does GPT-5.4 Nano or Qwen3.8 27B have a bigger context window?
Qwen3.8 27B has the larger context window: 400K for GPT-5.4 Nano against 1M for Qwen3.8 27B. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do GPT-5.4 Nano and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.020 per million for GPT-5.4 Nano and $0.050 for Qwen3.8 27B, against full input rates of $0.200 and $0.450. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.