// head_to_head
Kimi K2.6 vs GPT-5.4 Mini
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.
Kimi K2.6 wins outright — it scores higher and costs less.
Kimi K2.6 leads by 4.2 points overall while listing 1.7× 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. Kimi K2.6 also leads on measured cost per point of capability, at $0.0918 per point.
moonshotai
Kimi K2.6
- Blended / 1M
- $1.01
- Context
- 262K
- Released
- Apr 20, 2026
- Overall score
- 70.5
openai
GPT-5.4 Mini
- Blended / 1M
- $1.69
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 66.4
Specs and pricing
| Metric | Kimi K2.6 | GPT-5.4 Mini |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 70.5win | 66.4 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0918win | $0.1871 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.01win | $1.69 |
| Input price / 1M | $0.560win | $0.750 |
| Output price / 1M | $2.36win | $4.50 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.094 | $0.075win |
| Context window | 262K | 400Kwin |
| Max output tokens | 262Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K2.6 on top, GPT-5.4 Mini 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 K2.6 | GPT-5.4 Mini |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $289.76/mo | $491.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $403.56/mo | $600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $376.18/mo | $582.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $714.19/mo | $1053.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1404.20/mo | $1987.50/mo |
Which should you pick?
You are running this at volume
Kimi K2.6
Lowest measured cost per point of capability at $0.0918 per point — the gap compounds with every request.
Quality matters more than the bill
Kimi K2.6
Highest overall LiveBench score of the two at 70.5.
The workload is coding or agentic work
Kimi K2.6
Leads on agentic coding — 46.9 against 41.7.
You need to fit large documents in one call
GPT-5.4 Mini
Wider context window — 400K against 262K.
Kimi K2.6 vs GPT-5.4 Mini FAQ
Which is better, Kimi K2.6 or GPT-5.4 Mini?
Kimi K2.6 wins outright — it scores higher and costs less. Kimi K2.6 leads by 4.2 points overall while listing 1.7× 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. Kimi K2.6 also leads on measured cost per point of capability, at $0.0918 per point.
Is Kimi K2.6 cheaper than GPT-5.4 Mini?
Kimi K2.6 is cheaper. On a 3:1 input:output blend, Kimi K2.6 lists at $1.01 per million tokens and GPT-5.4 Mini at $1.69 — Kimi K2.6 is 1.7× cheaper. Input and output are priced separately — Kimi K2.6 charges $0.560 in and $2.36 out, GPT-5.4 Mini charges $0.750 and $4.50 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Kimi K2.6 vs GPT-5.4 Mini: which scores higher on benchmarks?
Kimi K2.6 scores 70.5 and GPT-5.4 Mini scores 66.4 overall on LiveBench, the mean of its seven categories. That is a 4.2-point lead for Kimi K2.6. 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, Kimi K2.6 or GPT-5.4 Mini?
Kimi K2.6. 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. Kimi K2.6 works out at $0.0918 per point and GPT-5.4 Mini at $0.1871.
Does Kimi K2.6 or GPT-5.4 Mini have a bigger context window?
GPT-5.4 Mini has the larger context window: 262K for Kimi K2.6 against 400K for GPT-5.4 Mini. 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 K2.6 and GPT-5.4 Mini support prompt caching?
Both publish a cached-input rate: $0.094 per million for Kimi K2.6 and $0.075 for GPT-5.4 Mini, against full input rates of $0.560 and $0.750. 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.