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Google: Gemma 4 26B A4B Coding Benchmark

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Context262,144tokens
Max Output235,929tokens
Inputmodalities
Price$0.04/1M input
Ranks from Kilo Code LeaderboardPricing via OpenRouter

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Coding Performance

Coding benchmarks and performance metrics for development tasks

Security

Enkrypt AI red-team scores for Google: Gemma 4 26B A4B . Each value is the share of successful attacks on a 0–100 scale — lower is safer.

Overall risk
28.5

Lower is safer

Safety

36.6/ 100

Composite safety risk from Enkrypt red-team evaluations. Lower is safer.

NIST

29.0/ 100

Average attack success across NIST-mapped tests: bias, harm, toxicity, CBRN, and insecure code.

OWASP

33.0/ 100

Weighted average of the same tests using OWASP Top 10 for LLMs 2025 risk rankings.

Attack categories

Percentage of successful attacks in each Enkrypt red-team category.

Jailbreak21.9/ 100

Share of jailbreak tests that bypassed the model's safety constraints.

Bias87.1/ 100

Share of tests that elicited biased responses.

Harmful content5.0/ 100

Share of tests that produced dangerous, violent, or hateful content.

Toxicity2.9/ 100

Share of tests that produced toxic or abusive content.

CBRN37.0/ 100

Share of tests that elicited chemical, biological, radiological, or nuclear assistance.

Insecure code10.7/ 100

Share of tests that produced vulnerable or malicious code.

Score
Value
Overall risk
28.5 / 100
Safety
36.6 / 100
NIST
29.0 / 100
OWASP
33.0 / 100
Jailbreak
21.9 / 100
Bias
87.1 / 100
Harmful content
5.0 / 100
Toxicity
2.9 / 100
CBRN
37.0 / 100
Insecure code
10.7 / 100

Security scores from the Enkrypt AI Safety Leaderboard · Last checked Sep 13, 2026

Real-World Usage

Real-world usage statistics from the Kilo Code community

Weekly Token Usage

No ranking data available for this model yet.

Real-world metrics from the Kilo Code Leaderboard

Pricing

Cost per 1 million tokens

Input Tokens
$0.04
per 1M tokens
Output Tokens
$0.22
per 1M tokens

Example Cost

Analyzing a 10,000 line codebase (≈40k input tokens, 10k output tokens) costs approximately $0.0039

Coding Capabilities

Features and parameters relevant to coding tasks

Coding Features

Function Calling
Can call external functions/APIs
Tool Choice
Control over function selection
Structured Outputs
JSON schema validation
Reasoning Tokens
Extended thinking for complex problems

Pricing details from OpenRouter

Technical Details

Architecture and implementation specifications

Specifications
Model ID
google/gemma-4-26b-a4b-it
Created
April 3, 2026
Tokenizer
Gemma
Input Modalities
ImageTextVideo
Context Window
262,144 tokens
Max Completion Tokens
235,929 tokens
Input Price
$0.04 per 1M tokens
Output Price
$0.22 per 1M tokens
Content Moderation
Disabled

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