Model overview
We present Kanana-2 SLM, **Kakao's second series of Small Language Models (SLMs)**, designed to deliver strong language capabilities while remaining compact and efficient for practical deployment. The series includes 3B, 1.3B, and 0.9B models. This release publicly includes the 3B model and the compressed 1.3B model, providing a balance between capability and efficiency for a wide range of applications. Kanana-2-3B was **pretrained from scratch on TPU clusters** and further improved through post-training with **supervised fine-tuning and reinforcement learning**, resulting in strong instruction-following and reasoning capabilities. Kanana-2-1.3B models are derived from Kanana-2-3B through a **cascade pruning and distillation pipeline**. To further improve deployment efficiency, they adopt **Sliding Window Attention (SWA)**, enabling memory-efficient long-context inference with support for context lengths of up to **32K tokens** while substantially reducing KV-cache memory requirements. General Tasks MMLU acc 5 62.77 56.49 62.32 MMLU-Pro acc 5 36.54 29.87 37.08 BBH acc 3 54.23 45.58 53.60 SimpleQA † acc 5 27.39 23.76 16.83 Mathematics Tasks MATH em 4 35.08 30.82 41.74 GSM8K em 8 61.03 51.93 75.74 Coding Tasks HumanEval pass@1 0 55.96 51.33 45.31 MBPP pass@1 3 50.95 44.86 53.85 Korean Tasks KMMLU acc 5 47.92 44.17 43.08 KoSimpleQA † acc 5 32.50 28.50 13.40 HAE-RAE Bench (v1.0) acc 5 80.66 75.34 55.54 MATH-Ko ‡ em 4 31.54 24.75 31.92 MBPP-Ko § pass@1 3 44.55 39.38 47.97 Long Context Tasks RULER-32K acc 0 67.50 55.72 69.01 Instruction-following, chat, tool-calling, code, math, and knowledge benchmarks for the Kanana-2 SLM series. Scores use greedy decoding (temperature 0.0, top-p 1.0, max 4096 tokens); the metric for each benchmark is listed in the Metric column.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。