Data as of October 25, 2026 · How the score is built
Released Jul 24, 202632K contextKakao Kanana-2 1.3B Instruct model card
Kanana-2 1.3B Instruct
Decision readingKanana-2 1.3B Instruct is tracked, but not publicly ranked yet. The profile exposes 4 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Released Jul 24, 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
Unranked
field median 50Not eligible for a public rank
Price
Self-hosted; infrastructure cost varies
input median $0.75No comparable first-party hosted token rate
Speed
Not measured
field median 96 tok/sTime to first token not measured
Context
32Ktokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Published rows are visible, but no category has enough eligible evidence for a comparative rank.
Validate before choosing
4 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 4 displayable benchmark rows
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| CodingWeight 20%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank Not rankedWeight 5%2 benchmarksVerified | 9.7 | 2 benchmarks | Verified | |||
| MathWeight 5%1 benchmarkVerified | Score pending | 1 benchmark | Verified |
4 of 766 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- AgenticNot measured
- CodingNot measured
- ReasoningNot measured
- MultimodalNot measured
- KnowledgeNot measured
- MultilingualNot measured
- Inst. Following2/2 verified
- Math1/1 verified
Benchmark ledger
Inst. Following opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Inst. Following2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score34.7% | Versus best verified row Best verified: Flint · 89.2% | Gap54.5 behind | WeightDisplay only | Provider exact |
| IFEvalInstruction-Following Eval | Score77.6% | Versus best verified row Best verified: LFM2.5-24B-A2B · 96.8% | Gap19.1 behind | WeightDisplay only | Provider exact |
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MATH-500MATH-500 Problem Set | Score61.4% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 98.8% | Gap37.4 behind | WeightDisplay only | Provider exact |
Korean1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| KMMLUKorean Massive Multitask Language Understanding | Score42.8% | Versus best verified row Best verified: Kanana-2 3B Instruct · 43.3% | Gap0.5 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 4 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- kakaocorp/kanana-2-1.3b-instructKakao Kanana-2 1.3B Instruct model card
- Context window
- 32KKakao Kanana-2 1.3B Instruct model card
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Kakao publishes the 1.3B-parameter on-device instruct model on Hugging Face under its own Kanana license with a native 32,768-token context.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordKakao Kanana-2 1.3B Instruct model card
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track Kanana-2 1.3B Instruct, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
Kanana-2 1.3B Instruct is a open weight model with a 32K context window. No explicit reasoning mode is documented in this profile.
Kakao publishes the 1.3B-parameter on-device instruct model on Hugging Face under its own Kanana license with a native 32,768-token context.
Official exact-value snapshot from the Kanana-2 Hugging Face model cards. BenchLM maps IFBench, IFEval, MATH-500, and KMMLU (CoT) onto existing lanes; the remaining chat, tool-calling, code, and Korean rows have no exact compatible lane. The row stays unranked.
Kanana-2 1.3B Instruct sits in the Kanana 2 family with Kanana-2 3B Instruct. 4 of 766 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Last updated October 25, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Kanana-2 1.3B Instruct perform overall in AI benchmarks?
Kanana-2 1.3B Instruct has 4 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is Kanana-2 1.3B Instruct good for mathematics?
Kanana-2 1.3B Instruct has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Kanana-2 1.3B Instruct good for instruction following?
Kanana-2 1.3B Instruct has source-displayable benchmark coverage for instruction following, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
How does Kanana-2 1.3B Instruct perform on Korean benchmarks?
Kanana-2 1.3B Instruct has 1 source-displayable Korean benchmark score on this profile. Those rows remain separate from the global overall score because regional evaluations use a different comparison set. Use the dedicated Korean tables for side-by-side positions and inspect each source before applying the result elsewhere.
Is Kanana-2 1.3B Instruct open source?
Kanana-2 1.3B Instruct is an open-weight model from Kakao. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to Kanana-2 1.3B Instruct?
Kanana-2 1.3B Instruct belongs to the Kanana 2 family. Related tracked variants include Kanana-2 3B Instruct. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Kanana-2 1.3B Instruct have full benchmark coverage on BenchLM?
No. Kanana-2 1.3B Instruct currently has 4 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Kanana-2 1.3B Instruct?
Kanana-2 1.3B Instruct has a documented context window of 32K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.