Performance Analysis

Benchmarks & Performance

Rigorous, reproducible numbers. Gemma 4 31B ranks 3rd globally among open-source models on Arena AI with an ELO of ~1452, beating models 20x its size in practical agentic simulations.

~1452
Arena AI ELO
3rd globally (open-source)
2150
Codeforces ELO
Expert-level coding
4.3x
AIME Improvement
vs Gemma 3 27B
4.7x
Agent Improvement
Tau2 vs Gemma 3

Gemma 4 Family — Internal Comparison

BenchmarkGemma 4 31BGemma 4 26BGemma 3 27BImprovement
MMLU Pro
Massive Multitask Language Understanding
85.2%82.6%67.6%+26%
AIME 2026
American Invitational Mathematics Exam
89.2%88.3%20.8%+329%
LiveCodeBench
Live Coding Benchmark v6
80%77.1%29.1%+175%
GPQA Diamond
Graduate-Level Science QA
84.3%82.3%42.4%+99%
Tau2 (Agent)
Agentic Tool Use Benchmark
76.9%68.2%16.2%+375%

Visual: Gemma 4 31B vs Gemma 3 27B

MMLU Pro
67.6%85.2%
AIME 2026
20.8%89.2%
LiveCodeBench
29.1%80%
GPQA Diamond
42.4%84.3%
Tau2 (Agent)
16.2%76.9%

Gemma 4 vs Competitors (2026)

How the 31B model compares against the top open model families.

MetricGemma 4 31BQwen 3.5 27BLlama 4 Scout
Reasoning (GPQA)84.3%~72.0%74.3%
Mathematics (AIME)89.2%48.7%N/A
Coding (LCB v6)80.0%43.0%N/A
Context Window256K262K10M
Arena AI ELO~1452
Codeforces ELO2150

"—" indicates data not publicly available. Qwen leads in multilingual coverage (201 languages). Llama 4 Scout leads in context length (10M tokens).

Thinking Mode & Deterministic Logic

The 31B model's dominance in mathematics is attributed to its "Thinking Mode," which allows for step-by-step reasoning before outputting a final token. This helps the model avoid "mode collapse" on complex paradoxes or edge cases.

Research variants like Gemma-4-31B-Cognitive-Unshackled demonstrate that by surgically removing "Refusal Vectors" (targeting Layer 39 in the residual stream), developers achieve a 10–15% increase in token generation speed alongside improved performance on complex logical paradoxes.

The intelligence is described as "deterministic and rigorous" — delivering consistent, reproducible answers to complex mathematical and scientific problems that previously required models with 10x the parameter count.

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