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Model A
Gemma 4 26B A4B

Google

55.09/100

Supported · Public rank #98

90% interval 39.071.1

Gemma 4 26B A4B vs Kanana-2 3B Instruct

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

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Model B
Kanana-2 3B Instruct

Kakao

Evidence status unavailable

90% interval unavailable

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    Gemma 4 26B A4B

    Gemma 4 26B A4B has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Kanana-2 3B Instruct is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Kanana-2 3B Instruct is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Kanana-2 3B Instruct does not fit this workload in one request. Gemma 4 26B A4B has no comparable published API token rate. Kanana-2 3B Instruct has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Kanana-2 3B Instruct does not fit this workload in one request. Gemma 4 26B A4B has no comparable published API token rate. Kanana-2 3B Instruct has no comparable published API token rate.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Gemma 4 26B A4B only
4
Kanana-2 3B Instruct only
3
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
Gemma 4 26B A4B
51.8
Estimated · #42/152
Kanana-2 3B Instruct
Not ranked
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 26B A4B
41.3
Supported · #109/151
Kanana-2 3B Instruct
Not ranked
Basis
BenchAlign lane · 0 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 26B A4B
66.2
Unranked · 2 rankable rows
Kanana-2 3B Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Gemma 4 26B A4B
43.2
Supported · #119/183
Kanana-2 3B Instruct
Not ranked
Basis
BenchAlign lane · 3 vs 0 public rows
Reading
Not comparable

Math

Not comparable
Gemma 4 26B A4B
Not ranked
Kanana-2 3B Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 26B A4B
Not ranked
Kanana-2 3B Instruct
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 26B A4B
44.1
#41/48
Kanana-2 3B Instruct
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 26B A4B
88.7
#30/123
Kanana-2 3B Instruct
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Kanana-2 3B Instruct
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Kanana-2 3B Instruct has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Kanana-2 3B Instruct
Self-hosted; infrastructure cost varies
Does not fit in one request

Kanana-2 3B Instruct does not fit this workload in one request. Gemma 4 26B A4B has no comparable published API token rate. Kanana-2 3B Instruct has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Kanana-2 3B Instruct
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

Kanana-2 3B Instruct does not fit this workload in one request. Gemma 4 26B A4B has no comparable published API token rate. Kanana-2 3B Instruct has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemma 4 26B A4B

No comparable hosted API rate

Kanana-2 3B Instruct

No comparable hosted API rate

Kakao Kanana-2 3B Instruct model card

Provider availability

Gemma 4 26B A4B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Kanana-2 3B Instruct

Not sourced

Reasoning profile

Gemma 4 26B A4B

Reasoning

Kanana-2 3B Instruct

Non-Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

Kanana-2 3B Instruct

Open Weight

License

Gemma 4 26B A4B

Open Weight

Kanana-2 3B Instruct

Open Weight

Release date

Gemma 4 26B A4B

2026-04-02

Kanana-2 3B Instruct

2026-07-24

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemma 4 26B A4B has the larger documented window (256K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence7 rows

Knowledge

  • MMLU-Pro

    Gemma 4 26B A4B82.6%
    Source
    Kanana-2 3B Instruct

    Not directly comparable

  • HLE

    Gemma 4 26B A4B17.2%
    Source
    Kanana-2 3B Instruct

    Not directly comparable

  • HLE w/o tools

    Gemma 4 26B A4B8.7%
    Source
    Kanana-2 3B Instruct

    Not directly comparable

Math

  • MATH-500

    Gemma 4 26B A4B
    Kanana-2 3B Instruct61.2%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 26B A4B73.8%
    Source
    Kanana-2 3B Instruct

    Not directly comparable

Instruction following

  • IFBench

    Gemma 4 26B A4B
    Kanana-2 3B Instruct33.3%
    Source

    Not directly comparable

  • IFEval

    Gemma 4 26B A4B
    Kanana-2 3B Instruct81.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemma 4 26B A4B or Kanana-2 3B Instruct?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 26B A4B or Kanana-2 3B Instruct?

Kanana-2 3B Instruct is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemma 4 26B A4B or Kanana-2 3B Instruct?

Kanana-2 3B Instruct is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 26B A4B or Kanana-2 3B Instruct?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Gemma 4 26B A4B or Kanana-2 3B Instruct?

Gemma 4 26B A4B has the larger documented context window: 256K, compared with 32K.

Related comparisons

Last updated September 10, 2026

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