// head_to_head
MiniMax M3 vs GPT-5.6 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-5.6 Terra scores higher, MiniMax M3 costs less — it depends on your workload.
GPT-5.6 Terra is ahead by 10.7 points overall, and MiniMax M3 lists 8.6× cheaper per blended million tokens. Whether 10.7 points is worth that depends on how much a wrong answer costs you. MiniMax M3 also leads on measured cost per point of capability, at $0.0339 per point.
minimax
MiniMax M3
- Blended / 1M
- $0.525
- Context
- 1.0M
- Released
- May 31, 2026
- Overall score
- 67.3
openai
GPT-5.6 Terra
- Blended / 1M
- $4.50
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 77.9
Specs and pricing
| Metric | MiniMax M3 | GPT-5.6 Terra |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 67.3 | 77.9win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0339win | $0.1939 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.525win | $4.50 |
| Input price / 1M | $0.300win | $2.00 |
| Output price / 1M | $1.20win | $12.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.060win | $0.200 |
| Context window | 1.0M | 1.1M |
| Max output tokens | 512Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax M3 on top, GPT-5.6 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 | MiniMax M3 | GPT-5.6 Terra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $150.72/mo | $1310.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $216.00/mo | $1600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $201.60/mo | $1552.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $378.00/mo | $2810.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $750.00/mo | $5300.00/mo |
Which should you pick?
You are running this at volume
MiniMax M3
Lowest measured cost per point of capability at $0.0339 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-5.6 Terra
Highest overall LiveBench score of the two at 77.9.
The workload is coding or agentic work
GPT-5.6 Terra
Leads on agentic coding — 54.9 against 40.7.
MiniMax M3 vs GPT-5.6 Terra FAQ
Which is better, MiniMax M3 or GPT-5.6 Terra?
GPT-5.6 Terra scores higher, MiniMax M3 costs less — it depends on your workload. GPT-5.6 Terra is ahead by 10.7 points overall, and MiniMax M3 lists 8.6× cheaper per blended million tokens. Whether 10.7 points is worth that depends on how much a wrong answer costs you. MiniMax M3 also leads on measured cost per point of capability, at $0.0339 per point.
Is MiniMax M3 cheaper than GPT-5.6 Terra?
MiniMax M3 is cheaper. On a 3:1 input:output blend, MiniMax M3 lists at $0.525 per million tokens and GPT-5.6 Terra at $4.50 — MiniMax M3 is 8.6× cheaper. Input and output are priced separately — MiniMax M3 charges $0.300 in and $1.20 out, GPT-5.6 Terra charges $2.00 and $12.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
MiniMax M3 vs GPT-5.6 Terra: which scores higher on benchmarks?
MiniMax M3 scores 67.3 and GPT-5.6 Terra scores 77.9 overall on LiveBench, the mean of its seven categories. That is a 10.7-point lead for GPT-5.6 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, MiniMax M3 or GPT-5.6 Terra?
MiniMax M3. 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. MiniMax M3 works out at $0.0339 per point and GPT-5.6 Terra at $0.1939.
Does MiniMax M3 or GPT-5.6 Terra have a bigger context window?
They are effectively the same — 1.0M for MiniMax M3 and 1.1M for GPT-5.6 Terra.
Do MiniMax M3 and GPT-5.6 Terra support prompt caching?
Both publish a cached-input rate: $0.060 per million for MiniMax M3 and $0.200 for GPT-5.6 Terra, against full input rates of $0.300 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.