// head_to_head
Claude Opus 4.1 vs GPT-6.1 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-6.1 Sol is the cheaper of the two; neither can be ranked on quality here.
Claude Opus 4.1 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.
anthropic
Claude Opus 4.1
- Blended / 1M
- $30.00
- Context
- 200K
- Released
- Aug 5, 2025
- Overall score
- Not evaluated
openai
GPT-6.1 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 29, 2026
- Overall score
- 84.1
Specs and pricing
| Metric | Claude Opus 4.1 | GPT-6.1 Sol |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 84.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0076 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $30.00 | $4.00win |
| Input price / 1M | $15.00 | $2.00win |
| Output price / 1M | $75.00 | $10.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $1.50 | $0.100win |
| Context window | 200K | 1.1Mwin |
| Max output tokens | 32K | 128Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Opus 4.1 on top, GPT-6.1 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 | Claude Opus 4.1 | GPT-6.1 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $8,628/mo | $1,143/mo |
| RAG assistant 8K in / 600 out × 100K requests | $11,100/mo | $1,440/mo |
| Coding agent 40K in / 4K out × 20K requests | $10,440/mo | $1,336/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $19,950/mo | $2,655/mo |
| Bulk classification 500 in / 20 out × 5M requests | $38,250/mo | $5,050/mo |
Which should you pick?
You need to fit large documents in one call
GPT-6.1 Sol
Wider context window — 1.1M against 200K.
Claude Opus 4.1 vs GPT-6.1 Sol FAQ
Which is better, Claude Opus 4.1 or GPT-6.1 Sol?
GPT-6.1 Sol is the cheaper of the two; neither can be ranked on quality here. Claude Opus 4.1 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 Claude Opus 4.1 cheaper than GPT-6.1 Sol?
GPT-6.1 Sol is cheaper. On a 3:1 input:output blend, Claude Opus 4.1 lists at $30.00 per million tokens and GPT-6.1 Sol at $4.00 — GPT-6.1 Sol is 7.5× cheaper. Input and output are priced separately — Claude Opus 4.1 charges $15.00 in and $75.00 out, GPT-6.1 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Claude Opus 4.1 or GPT-6.1 Sol have a bigger context window?
GPT-6.1 Sol has the larger context window: 200K for Claude Opus 4.1 against 1.1M for GPT-6.1 Sol. 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 Claude Opus 4.1 and GPT-6.1 Sol support prompt caching?
Both publish a cached-input rate: $1.50 per million for Claude Opus 4.1 and $0.100 for GPT-6.1 Sol, against full input rates of $15.00 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 2026-10-25, 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.