// head_to_head
Lyria 3 Clip Preview vs GPT-6 Sol
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.
Lyria 3 Clip Preview is the cheaper of the two; neither can be ranked on quality here.
Neither model has 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.
Lyria 3 Clip Preview
- Blended / 1M
- Free
- Context
- 1.0M
- Released
- Mar 30, 2026
- Overall score
- Not evaluated
openai
GPT-6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Lyria 3 Clip Preview | GPT-6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | Freewin | $4.00 |
| Input price / 1M | Freewin | $2.00 |
| Output price / 1M | Freewin | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.200 |
| Context window | 1.0M | 1.1M |
| Max output tokens | 66K | 128Kwin |
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 | Lyria 3 Clip Preview | GPT-6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $0/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $0/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $0/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $0/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $0/mo | $5,100/mo |
Which should you pick?
You are cost-constrained
Lyria 3 Clip Preview
Cheaper on blended list price at Free per million tokens.
Lyria 3 Clip Preview vs GPT-6 Sol FAQ
Which is better, Lyria 3 Clip Preview or GPT-6 Sol?
Lyria 3 Clip Preview is the cheaper of the two; neither can be ranked on quality here. Neither model has 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 Lyria 3 Clip Preview cheaper than GPT-6 Sol?
Lyria 3 Clip Preview is cheaper. On a 3:1 input:output blend, Lyria 3 Clip Preview lists at Free per million tokens and GPT-6 Sol at $4.00. Input and output are priced separately — Lyria 3 Clip Preview charges Free in and Free out, GPT-6 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Lyria 3 Clip Preview or GPT-6 Sol have a bigger context window?
They are effectively the same — 1.0M for Lyria 3 Clip Preview and 1.1M for GPT-6 Sol.
Do Lyria 3 Clip Preview and GPT-6 Sol support prompt caching?
GPT-6 Sol publishes a cached-input rate of $0.200 per million tokens against a full input rate of $2.00. The catalogue lists no separate cached rate for Lyria 3 Clip Preview, 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.