// head_to_head
Kimi K3 vs GPT-6 Sol
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 — GPT-6 Sol is the cheaper way to get it.
The two are within 0.1 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-6 Sol lists 1.5× cheaper per blended million tokens. When quality ties, cost is the whole decision. GPT-6 Sol also leads on measured cost per point of capability, at $0.1444 per point.
moonshotai
Kimi K3
- Blended / 1M
- $6.00
- Context
- 1.0M
- Released
- Jul 16, 2026
- Overall score
- 79.2
openai
GPT-6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 79.2
Specs and pricing
| Metric | Kimi K3 | GPT-6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 79.2 | 79.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1909 | $0.1444win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $6.00 | $4.00win |
| Input price / 1M | $3.00 | $2.00win |
| Output price / 1M | $15.00 | $10.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.300 | $0.200win |
| Context window | 1.0M | 1.1M |
| 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 on top, GPT-6 Sol 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-6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,726/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2,220/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $2,088/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $3,990/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $7,650/mo | $5,100/mo |
Which should you pick?
You are running this at volume
GPT-6 Sol
Lowest measured cost per point of capability at $0.1444 per point — the gap compounds with every request.
The workload is coding or agentic work
Kimi K3
Leads on agentic coding — 62.2 against 52.9.
Kimi K3 vs GPT-6 Sol FAQ
Which is better, Kimi K3 or GPT-6 Sol?
Effectively the same quality — GPT-6 Sol is the cheaper way to get it. The two are within 0.1 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-6 Sol lists 1.5× cheaper per blended million tokens. When quality ties, cost is the whole decision. GPT-6 Sol also leads on measured cost per point of capability, at $0.1444 per point.
Is Kimi K3 cheaper than GPT-6 Sol?
GPT-6 Sol is cheaper. On a 3:1 input:output blend, Kimi K3 lists at $6.00 per million tokens and GPT-6 Sol at $4.00 — GPT-6 Sol is 1.5× cheaper. Input and output are priced separately — Kimi K3 charges $3.00 in and $15.00 out, GPT-6 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Kimi K3 vs GPT-6 Sol: which scores higher on benchmarks?
Kimi K3 scores 79.2 and GPT-6 Sol scores 79.2 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, Kimi K3 or GPT-6 Sol?
GPT-6 Sol. 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-6 Sol at $0.1444.
Does Kimi K3 or GPT-6 Sol have a bigger context window?
They are effectively the same — 1.0M for Kimi K3 and 1.1M for GPT-6 Sol.
Do Kimi K3 and GPT-6 Sol support prompt caching?
Both publish a cached-input rate: $0.300 per million for Kimi K3 and $0.200 for GPT-6 Sol, against full input rates of $3.00 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.