// head_to_head
GPT-5.4 Nano vs ReMM SLERP 13B
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.
GPT-5.4 Nano and ReMM SLERP 13B are priced within ~10% of each other.
ReMM SLERP 13B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
openai
GPT-5.4 Nano
- Blended / 1M
- $0.463
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 69.6
Specs and pricing
| Metric | GPT-5.4 Nano | ReMM SLERP 13B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 69.6 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0500 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.463 | $0.425 |
| Input price / 1M | $0.200win | $0.350 |
| Output price / 1M | $1.25 | $0.650win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020 | — |
| Context window | 400Kwin | 6K |
| Max output tokens | 128Kwin | 6K |
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, ReMM SLERP 13B 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 | ReMM SLERP 13B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $135.04/mo | $136.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $163.00/mo | $319.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $159.20/mo | $332.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $284.75/mo | $398.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $535.00/mo | $940.00/mo |
Which should you pick?
You need to fit large documents in one call
GPT-5.4 Nano
Wider context window — 400K against 6K.
GPT-5.4 Nano vs ReMM SLERP 13B FAQ
Which is better, GPT-5.4 Nano or ReMM SLERP 13B?
GPT-5.4 Nano and ReMM SLERP 13B are priced within ~10% of each other. ReMM SLERP 13B does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is GPT-5.4 Nano cheaper than ReMM SLERP 13B?
They cost about the same. Both land near $0.463 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does GPT-5.4 Nano or ReMM SLERP 13B have a bigger context window?
GPT-5.4 Nano has the larger context window: 400K for GPT-5.4 Nano against 6K for ReMM SLERP 13B. 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 ReMM SLERP 13B support prompt caching?
GPT-5.4 Nano publishes a cached-input rate of $0.020 per million tokens against a full input rate of $0.200. The catalogue lists no separate cached rate for ReMM SLERP 13B, which means the provider does not price it separately here — not that caching is unavailable.
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.