// head_to_head
Kimi K3 vs Sonar Pro Search
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 and Sonar Pro Search are priced within ~10% of each other.
Sonar Pro Search 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
- Blended / 1M
- $6.00
- Context
- 1.0M
- Released
- Jul 16, 2026
- Overall score
- 79.2
perplexity
Sonar Pro Search
- Blended / 1M
- $6.00
- Context
- 200K
- Released
- Oct 30, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Kimi K3 | Sonar Pro Search |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 79.2 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1909 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $6.00 | $6.00 |
| Input price / 1M | $3.00 | $3.00 |
| 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 | — |
| Context window | 1.0Mwin | 200K |
| Max output tokens | 944Kwin | 8K |
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, Sonar Pro Search 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 | Sonar Pro Search |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,726/mo | $1,920/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2,220/mo | $3,300/mo |
| Coding agent 40K in / 4K out × 20K requests | $2,088/mo | $3,600/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $3,990/mo | $4,125/mo |
| Bulk classification 500 in / 20 out × 5M requests | $7,650/mo | $9,000/mo |
Which should you pick?
You need to fit large documents in one call
Kimi K3
Wider context window — 1.0M against 200K.
Kimi K3 vs Sonar Pro Search FAQ
Which is better, Kimi K3 or Sonar Pro Search?
Kimi K3 and Sonar Pro Search are priced within ~10% of each other. Sonar Pro Search 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 cheaper than Sonar Pro Search?
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.
Does Kimi K3 or Sonar Pro Search have a bigger context window?
Kimi K3 has the larger context window: 1.0M for Kimi K3 against 200K for Sonar Pro Search. 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 and Sonar Pro Search support prompt caching?
Kimi K3 publishes a cached-input rate of $0.300 per million tokens against a full input rate of $3.00. The catalogue lists no separate cached rate for Sonar Pro Search, which means the provider does not price it separately here — not that caching is unavailable.
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.