// head_to_head
Kimi K2.6 vs Sakana Namazu
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 and Sakana Namazu are priced within ~10% of each other.
Sakana Namazu 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 K2.6
- Blended / 1M
- $1.71
- Context
- 262K
- Released
- Apr 20, 2026
- Overall score
- 70.5
sakana
Sakana Namazu
- Blended / 1M
- $1.71
- Context
- 262K
- Released
- Aug 11, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Kimi K2.6 | Sakana Namazu |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 70.5 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0918 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.71 | $1.71 |
| Input price / 1M | $0.950 | $0.950 |
| Output price / 1M | $4.00 | $4.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.160 | $0.150 |
| Context window | 262K | 262K |
| Max output tokens | 236Kwin | 66K |
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, Sakana Namazu 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 | Sakana Namazu |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $491.12/mo | $490.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $684.00/mo | $680.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $637.60/mo | $632.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,211/mo | $1,210/mo |
| Bulk classification 500 in / 20 out × 5M requests | $2,380/mo | $2,375/mo |
Kimi K2.6 vs Sakana Namazu FAQ
Which is better, Kimi K2.6 or Sakana Namazu?
Kimi K2.6 and Sakana Namazu are priced within ~10% of each other. Sakana Namazu 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 K2.6 cheaper than Sakana Namazu?
They cost about the same. Both land near $1.71 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Kimi K2.6 or Sakana Namazu have a bigger context window?
They are effectively the same — 262K for Kimi K2.6 and 262K for Sakana Namazu.
Do Kimi K2.6 and Sakana Namazu support prompt caching?
Both publish a cached-input rate: $0.160 per million for Kimi K2.6 and $0.150 for Sakana Namazu, against full input rates of $0.950 and $0.950. 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.