// head_to_head
GPT-5.6 Sol 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 Sol wins outright — it scores higher and costs less.
GPT-5.6 Sol leads by 3.1 points overall while listing 13% 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. The two cost measures disagree here, which is worth knowing: GPT-5.6 Sol has the lower sticker price, but GPT-5.6 Terra earns each point of capability for less — $0.1939 against $0.2870 — because per-token rates do not predict how many tokens a model actually spends on a task.
openai
GPT-5.6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 81.1
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-5.6 Sol | GPT-5.6 Terra |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 81.1win | 77.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.2870 | $0.1939win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00win | $4.50 |
| Input price / 1M | $2.00 | $2.00 |
| Output price / 1M | $10.00win | $12.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.200 |
| Context window | 1.1M | 1.1M |
| 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-5.6 Sol 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-5.6 Sol | GPT-5.6 Terra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1150.40/mo | $1310.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1480.00/mo | $1600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1392.00/mo | $1552.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2660.00/mo | $2810.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5100.00/mo | $5300.00/mo |
Which should you pick?
You are running this at volume
GPT-5.6 Terra
Lowest measured cost per point of capability at $0.1939 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-5.6 Sol
Highest overall LiveBench score of the two at 81.1.
The workload is coding or agentic work
GPT-5.6 Sol
Leads on agentic coding — 56.2 against 54.9.
GPT-5.6 Sol vs GPT-5.6 Terra FAQ
Which is better, GPT-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 Sol wins outright — it scores higher and costs less. GPT-5.6 Sol leads by 3.1 points overall while listing 13% 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. The two cost measures disagree here, which is worth knowing: GPT-5.6 Sol has the lower sticker price, but GPT-5.6 Terra earns each point of capability for less — $0.1939 against $0.2870 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is GPT-5.6 Sol cheaper than GPT-5.6 Terra?
GPT-5.6 Sol is cheaper. On a 3:1 input:output blend, GPT-5.6 Sol lists at $4.00 per million tokens and GPT-5.6 Terra at $4.50 — GPT-5.6 Sol is 13% cheaper. Input and output are priced separately — GPT-5.6 Sol charges $2.00 in and $10.00 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.
GPT-5.6 Sol vs GPT-5.6 Terra: which scores higher on benchmarks?
GPT-5.6 Sol scores 81.1 and GPT-5.6 Terra scores 77.9 overall on LiveBench, the mean of its seven categories. That is a 3.1-point lead for GPT-5.6 Sol. 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-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 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. GPT-5.6 Sol works out at $0.2870 per point and GPT-5.6 Terra at $0.1939.
Does GPT-5.6 Sol or GPT-5.6 Terra have a bigger context window?
They are effectively the same — 1.1M for GPT-5.6 Sol and 1.1M for GPT-5.6 Terra.
Do GPT-5.6 Sol and GPT-5.6 Terra support prompt caching?
Both publish a cached-input rate: $0.200 per million for GPT-5.6 Sol and $0.200 for GPT-5.6 Terra, against full input rates of $2.00 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 2026-06-25, 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.