// head_to_head
MiniMax-01 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.
MiniMax-01 is the cheaper of the two; neither can be ranked on quality here.
MiniMax-01 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.
minimax
MiniMax-01
- Blended / 1M
- $0.425
- Context
- 1.0M
- Released
- Jan 15, 2025
- Overall score
- Not evaluated
openai
GPT-5.6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 81.1
Specs and pricing
| Metric | MiniMax-01 | GPT-5.6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 81.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.2870 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.425win | $4.00 |
| Input price / 1M | $0.200win | $2.00 |
| Output price / 1M | $1.10win | $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 | 900Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax-01 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 | MiniMax-01 | GPT-5.6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $136.00/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $226.00/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $248.00/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $282.50/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $610.00/mo | $5,100/mo |
Which should you pick?
You are cost-constrained
MiniMax-01
Cheaper on blended list price at $0.425 per million tokens.
MiniMax-01 vs GPT-5.6 Sol FAQ
Which is better, MiniMax-01 or GPT-5.6 Sol?
MiniMax-01 is the cheaper of the two; neither can be ranked on quality here. MiniMax-01 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 MiniMax-01 cheaper than GPT-5.6 Sol?
MiniMax-01 is cheaper. On a 3:1 input:output blend, MiniMax-01 lists at $0.425 per million tokens and GPT-5.6 Sol at $4.00 — MiniMax-01 is 9.4× cheaper. Input and output are priced separately — MiniMax-01 charges $0.200 in and $1.10 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.
Does MiniMax-01 or GPT-5.6 Sol have a bigger context window?
They are effectively the same — 1.0M for MiniMax-01 and 1.1M for GPT-5.6 Sol.
Do MiniMax-01 and GPT-5.6 Sol support prompt caching?
GPT-5.6 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 MiniMax-01, 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.