// head_to_head
Kimi K3 vs GPT-5.4
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 K3 is the better model, at roughly the same price.
Kimi K3 leads by 1.2 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. Kimi K3 also leads on measured cost per point of capability, at $0.1909 per point.
moonshotai
Kimi K3
- Blended / 1M
- $6.00
- Context
- 1.0M
- Released
- Jul 16, 2026
- Overall score
- 79.2
openai
GPT-5.4
- Blended / 1M
- $5.63
- Context
- 1.1M
- Released
- Mar 5, 2026
- Overall score
- 78.0
Specs and pricing
| Metric | Kimi K3 | GPT-5.4 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 79.2win | 78.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1909win | $0.2198 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $6.00 | $5.63 |
| Input price / 1M | $3.00 | $2.50win |
| Output price / 1M | $15.00 | $15.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.300 | $0.250win |
| Context window | 1.0M | 1.1M |
| Max output tokens | — | 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 on top, GPT-5.4 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 | GPT-5.4 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1725.60/mo | $1638.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2220.00/mo | $2000.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $2088.00/mo | $1940.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $3990.00/mo | $3512.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $7650.00/mo | $6625.00/mo |
Which should you pick?
You are running this at volume
Kimi K3
Lowest measured cost per point of capability at $0.1909 per point — the gap compounds with every request.
Quality matters more than the bill
Kimi K3
Highest overall LiveBench score of the two at 79.2.
The workload is coding or agentic work
Kimi K3
Leads on agentic coding — 62.2 against 53.8.
Kimi K3 vs GPT-5.4 FAQ
Which is better, Kimi K3 or GPT-5.4?
Kimi K3 is the better model, at roughly the same price. Kimi K3 leads by 1.2 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. Kimi K3 also leads on measured cost per point of capability, at $0.1909 per point.
Is Kimi K3 cheaper than GPT-5.4?
They cost about the same. Both land near $6.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Kimi K3 vs GPT-5.4: which scores higher on benchmarks?
Kimi K3 scores 79.2 and GPT-5.4 scores 78.0 overall on LiveBench, the mean of its seven categories. That is a 1.2-point lead for Kimi K3. 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 K3 or GPT-5.4?
Kimi K3. 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 K3 works out at $0.1909 per point and GPT-5.4 at $0.2198.
Does Kimi K3 or GPT-5.4 have a bigger context window?
They are effectively the same — 1.0M for Kimi K3 and 1.1M for GPT-5.4.
Do Kimi K3 and GPT-5.4 support prompt caching?
Both publish a cached-input rate: $0.300 per million for Kimi K3 and $0.250 for GPT-5.4, against full input rates of $3.00 and $2.50. 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.