// head_to_head
Muse Spark 1.1 vs GPT-5.6 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.
GPT-5.6 Sol scores higher, Muse Spark 1.1 costs less — it depends on your workload.
GPT-5.6 Sol is ahead by 5.8 points overall, and Muse Spark 1.1 lists 2.0× cheaper per blended million tokens. Whether 5.8 points is worth that depends on how much a wrong answer costs you. Muse Spark 1.1 also leads on measured cost per point of capability, at $0.1139 per point.
meta
Muse Spark 1.1
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Jul 16, 2026
- Overall score
- 75.3
openai
GPT-5.6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 81.1
Specs and pricing
| Metric | Muse Spark 1.1 | GPT-5.6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 75.3 | 81.1win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1139win | $0.2870 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.00win | $4.00 |
| Input price / 1M | $1.25win | $2.00 |
| Output price / 1M | $4.25win | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.150win | $0.200 |
| Context window | 1.0M | 1.1M |
| Max output tokens | — | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Spark 1.1 on top, GPT-5.6 Sol 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 | Muse Spark 1.1 | GPT-5.6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $560.80/mo | $1150.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $815.00/mo | $1480.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $724.00/mo | $1392.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1513.75/mo | $2660.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3000.00/mo | $5100.00/mo |
Which should you pick?
You are running this at volume
Muse Spark 1.1
Lowest measured cost per point of capability at $0.1139 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-5.6 Sol
Highest overall LiveBench score of the two at 81.1.
The workload is coding or agentic work
Muse Spark 1.1
Leads on agentic coding — 58.5 against 56.2.
Muse Spark 1.1 vs GPT-5.6 Sol FAQ
Which is better, Muse Spark 1.1 or GPT-5.6 Sol?
GPT-5.6 Sol scores higher, Muse Spark 1.1 costs less — it depends on your workload. GPT-5.6 Sol is ahead by 5.8 points overall, and Muse Spark 1.1 lists 2.0× cheaper per blended million tokens. Whether 5.8 points is worth that depends on how much a wrong answer costs you. Muse Spark 1.1 also leads on measured cost per point of capability, at $0.1139 per point.
Is Muse Spark 1.1 cheaper than GPT-5.6 Sol?
Muse Spark 1.1 is cheaper. On a 3:1 input:output blend, Muse Spark 1.1 lists at $2.00 per million tokens and GPT-5.6 Sol at $4.00 — Muse Spark 1.1 is 2.0× cheaper. Input and output are priced separately — Muse Spark 1.1 charges $1.25 in and $4.25 out, GPT-5.6 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Muse Spark 1.1 vs GPT-5.6 Sol: which scores higher on benchmarks?
Muse Spark 1.1 scores 75.3 and GPT-5.6 Sol scores 81.1 overall on LiveBench, the mean of its seven categories. That is a 5.8-point lead for GPT-5.6 Sol. 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, Muse Spark 1.1 or GPT-5.6 Sol?
Muse Spark 1.1. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Muse Spark 1.1 works out at $0.1139 per point and GPT-5.6 Sol at $0.2870.
Does Muse Spark 1.1 or GPT-5.6 Sol have a bigger context window?
They are effectively the same — 1.0M for Muse Spark 1.1 and 1.1M for GPT-5.6 Sol.
Do Muse Spark 1.1 and GPT-5.6 Sol support prompt caching?
Both publish a cached-input rate: $0.150 per million for Muse Spark 1.1 and $0.200 for GPT-5.6 Sol, against full input rates of $1.25 and $2.00. 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.