// head_to_head
Muse Wonder vs GPT-6.1 Terra
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-6.1 Terra wins outright — it scores higher and costs less.
GPT-6.1 Terra leads by 3.6 points overall while listing 4.0× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. GPT-6.1 Terra also leads on measured cost per point of capability, at $0.0067 per point.
meta
Muse Wonder
- Blended / 1M
- $8.00
- Context
- 1.0M
- Released
- Oct 12, 2026
- Overall score
- 74.2
openai
GPT-6.1 Terra
- Blended / 1M
- $2.00
- Context
- 1.1M
- Released
- Oct 26, 2026
- Overall score
- 77.8
Specs and pricing
| Metric | Muse Wonder | GPT-6.1 Terra |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.2 | 77.8win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0201 | $0.0067win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $8.00 | $2.00win |
| Input price / 1M | $4.00 | $1.00win |
| Output price / 1M | $20.00 | $5.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.050win |
| Context window | 1.0M | 1.1M |
| Max output tokens | 944Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Wonder on top, GPT-6.1 Terra 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 Wonder | GPT-6.1 Terra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $2,286/mo | $571.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2,880/mo | $720.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $2,672/mo | $668.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $5,310/mo | $1,328/mo |
| Bulk classification 500 in / 20 out × 5M requests | $10,100/mo | $2,525/mo |
Which should you pick?
You are running this at volume
GPT-6.1 Terra
Lowest measured cost per point of capability at $0.0067 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-6.1 Terra
Highest overall LiveBench score of the two at 77.8.
The workload is coding or agentic work
Muse Wonder
Leads on long-horizon agentic — 52.6 against 48.6.
Muse Wonder vs GPT-6.1 Terra FAQ
Which is better, Muse Wonder or GPT-6.1 Terra?
GPT-6.1 Terra wins outright — it scores higher and costs less. GPT-6.1 Terra leads by 3.6 points overall while listing 4.0× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. GPT-6.1 Terra also leads on measured cost per point of capability, at $0.0067 per point.
Is Muse Wonder cheaper than GPT-6.1 Terra?
GPT-6.1 Terra is cheaper. On a 3:1 input:output blend, Muse Wonder lists at $8.00 per million tokens and GPT-6.1 Terra at $2.00 — GPT-6.1 Terra is 4.0× cheaper. Input and output are priced separately — Muse Wonder charges $4.00 in and $20.00 out, GPT-6.1 Terra charges $1.00 and $5.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Muse Wonder vs GPT-6.1 Terra: which scores higher on benchmarks?
Muse Wonder scores 74.2 and GPT-6.1 Terra scores 77.8 overall on LiveBench, the weighted mean of its seven categories. That is a 3.6-point lead for GPT-6.1 Terra. 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 Wonder or GPT-6.1 Terra?
GPT-6.1 Terra. 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 Wonder works out at $0.0201 per point and GPT-6.1 Terra at $0.0067.
Does Muse Wonder or GPT-6.1 Terra have a bigger context window?
They are effectively the same — 1.0M for Muse Wonder and 1.1M for GPT-6.1 Terra.
Do Muse Wonder and GPT-6.1 Terra support prompt caching?
Both publish a cached-input rate: $0.200 per million for Muse Wonder and $0.050 for GPT-6.1 Terra, against full input rates of $4.00 and $1.00. 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 2026-10-25, 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.