// head_to_head
GPT-5.2 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 is the better model, at roughly the same price.
GPT-5.6 Terra leads by 3.3 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. GPT-5.2 also leads on measured cost per point of capability, at $0.1289 per point.
openai
GPT-5.2
- Blended / 1M
- $4.81
- Context
- 400K
- Released
- Dec 10, 2025
- Overall score
- 74.6
openai
GPT-5.6 Terra
- Blended / 1M
- $4.50
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 77.9
Specs and pricing
| Metric | GPT-5.2 | GPT-5.6 Terra |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.6 | 77.9win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1289win | $0.1939 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.81 | $4.50 |
| Input price / 1M | $1.75win | $2.00 |
| Output price / 1M | $14.00 | $12.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.175win | $0.200 |
| Context window | 400K | 1.1Mwin |
| Max output tokens | 128K | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.2 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 | GPT-5.2 | GPT-5.6 Terra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1426.60/mo | $1310.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1610.00/mo | $1600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1638.00/mo | $1552.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2721.25/mo | $2810.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4987.50/mo | $5300.00/mo |
Which should you pick?
You are running this at volume
GPT-5.2
Lowest measured cost per point of capability at $0.1289 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 50.3.
You need to fit large documents in one call
GPT-5.6 Terra
Wider context window — 1.1M against 400K.
GPT-5.2 vs GPT-5.6 Terra FAQ
Which is better, GPT-5.2 or GPT-5.6 Terra?
GPT-5.6 Terra is the better model, at roughly the same price. GPT-5.6 Terra leads by 3.3 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. GPT-5.2 also leads on measured cost per point of capability, at $0.1289 per point.
Is GPT-5.2 cheaper than GPT-5.6 Terra?
They cost about the same. Both land near $4.81 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
GPT-5.2 vs GPT-5.6 Terra: which scores higher on benchmarks?
GPT-5.2 scores 74.6 and GPT-5.6 Terra scores 77.9 overall on LiveBench, the mean of its seven categories. That is a 3.3-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, GPT-5.2 or GPT-5.6 Terra?
GPT-5.2. 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. GPT-5.2 works out at $0.1289 per point and GPT-5.6 Terra at $0.1939.
Does GPT-5.2 or GPT-5.6 Terra have a bigger context window?
GPT-5.6 Terra has the larger context window: 400K for GPT-5.2 against 1.1M for GPT-5.6 Terra. 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 GPT-5.2 and GPT-5.6 Terra support prompt caching?
Both publish a cached-input rate: $0.175 per million for GPT-5.2 and $0.200 for GPT-5.6 Terra, against full input rates of $1.75 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.