// head_to_head
Kimi K3.1 Pro vs GPT-5
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 is the cheaper of the two; neither can be ranked on quality here.
GPT-5 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.
moonshotai
Kimi K3.1 Pro
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 23, 2026
- Overall score
- 71.3
openai
GPT-5
- Blended / 1M
- $3.44
- Context
- 400K
- Released
- Aug 7, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Kimi K3.1 Pro | GPT-5 |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 71.3 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0104 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $3.44win |
| Input price / 1M | $2.00 | $1.25win |
| Output price / 1M | $10.00 | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.125win |
| Context window | 1.0Mwin | 400K |
| Max output tokens | 944Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K3.1 Pro on top, GPT-5 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 | Kimi K3.1 Pro | GPT-5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,150/mo | $1,019/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,480/mo | $1,150/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,392/mo | $1,170/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,660/mo | $1,944/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,100/mo | $3,563/mo |
Which should you pick?
You need to fit large documents in one call
Kimi K3.1 Pro
Wider context window — 1.0M against 400K.
You are cost-constrained
GPT-5
Cheaper on blended list price at $3.44 per million tokens.
Kimi K3.1 Pro vs GPT-5 FAQ
Which is better, Kimi K3.1 Pro or GPT-5?
GPT-5 is the cheaper of the two; neither can be ranked on quality here. GPT-5 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 Kimi K3.1 Pro cheaper than GPT-5?
GPT-5 is cheaper. On a 3:1 input:output blend, Kimi K3.1 Pro lists at $4.00 per million tokens and GPT-5 at $3.44 — GPT-5 is 16% cheaper. Input and output are priced separately — Kimi K3.1 Pro charges $2.00 in and $10.00 out, GPT-5 charges $1.25 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Kimi K3.1 Pro or GPT-5 have a bigger context window?
Kimi K3.1 Pro has the larger context window: 1.0M for Kimi K3.1 Pro against 400K for GPT-5. 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 Kimi K3.1 Pro and GPT-5 support prompt caching?
Both publish a cached-input rate: $0.200 per million for Kimi K3.1 Pro and $0.125 for GPT-5, against full input rates of $2.00 and $1.25. 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 2026-10-25, 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.