// head_to_head
Gemini 3.7 Flash 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.
Effectively the same quality — Gemini 3.7 Flash is the cheaper way to get it.
The two are within 0.9 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Gemini 3.7 Flash lists 6.0× cheaper per blended million tokens. When quality ties, cost is the whole decision. Gemini 3.7 Flash also leads on measured cost per point of capability, at $0.0875 per point.
Gemini 3.7 Flash
- Blended / 1M
- $0.750
- Context
- 1.0M
- Released
- Aug 13, 2026
- Overall score
- 78.8
openai
GPT-5.6 Terra
- Blended / 1M
- $4.50
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 77.9
Specs and pricing
| Metric | Gemini 3.7 Flash | GPT-5.6 Terra |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 78.8 | 77.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0875win | $0.1939 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.750win | $4.50 |
| Input price / 1M | $0.375win | $2.00 |
| Output price / 1M | $1.88win | $12.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.037win | $0.200 |
| Context window | 1.0M | 1.1M |
| Max output tokens | 66K | 128Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.7 Flash 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 | Gemini 3.7 Flash | GPT-5.6 Terra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $215.70/mo | $1310.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $277.50/mo | $1600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $261.00/mo | $1552.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $498.75/mo | $2810.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $956.25/mo | $5300.00/mo |
Which should you pick?
You are running this at volume
Gemini 3.7 Flash
Lowest measured cost per point of capability at $0.0875 per point — the gap compounds with every request.
The workload is coding or agentic work
Gemini 3.7 Flash
Leads on agentic coding — 58.3 against 54.9.
Gemini 3.7 Flash vs GPT-5.6 Terra FAQ
Which is better, Gemini 3.7 Flash or GPT-5.6 Terra?
Effectively the same quality — Gemini 3.7 Flash is the cheaper way to get it. The two are within 0.9 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Gemini 3.7 Flash lists 6.0× cheaper per blended million tokens. When quality ties, cost is the whole decision. Gemini 3.7 Flash also leads on measured cost per point of capability, at $0.0875 per point.
Is Gemini 3.7 Flash cheaper than GPT-5.6 Terra?
Gemini 3.7 Flash is cheaper. On a 3:1 input:output blend, Gemini 3.7 Flash lists at $0.750 per million tokens and GPT-5.6 Terra at $4.50 — Gemini 3.7 Flash is 6.0× cheaper. Input and output are priced separately — Gemini 3.7 Flash charges $0.375 in and $1.88 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.
Gemini 3.7 Flash vs GPT-5.6 Terra: which scores higher on benchmarks?
Gemini 3.7 Flash scores 78.8 and GPT-5.6 Terra scores 77.9 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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, Gemini 3.7 Flash or GPT-5.6 Terra?
Gemini 3.7 Flash. 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. Gemini 3.7 Flash works out at $0.0875 per point and GPT-5.6 Terra at $0.1939.
Does Gemini 3.7 Flash or GPT-5.6 Terra have a bigger context window?
They are effectively the same — 1.0M for Gemini 3.7 Flash and 1.1M for GPT-5.6 Terra.
Do Gemini 3.7 Flash and GPT-5.6 Terra support prompt caching?
Both publish a cached-input rate: $0.037 per million for Gemini 3.7 Flash and $0.200 for GPT-5.6 Terra, against full input rates of $0.375 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.