Gemma 4 Review
Gemma 4 review: how a 31B model competes with 600B rivals
Google DeepMind's Gemma 4 family launched April 2, 2026 with four models under Apache 2.0. The 31B ranks #3 on Arena AI, the 26B MoE runs on a single RTX 4090, and the E2B fits on a phone. Here's what actually works and where it falls short.
Verdict
The bottom line on each Gemma 4 model
After extensive testing across reasoning, coding, multimodal, and local deployment, here's the verdict on each variant.
Overall Verdict
The most capable open model family you can run locally
Gemma 4 is the best open model family for users who want frontier-class AI on their own hardware. The 31B competes with models 20x its size on reasoning and coding. The 26B MoE is the sweet spot for most production use. The edge models bring real AI to phones and browsers.
The main weakness: on pure agentic coding (SWE-Bench), Gemma 4 still lags behind Qwen 3.6 and GLM-5.1. If your primary use case is autonomous code editing, consider those alternatives.
Verdict: Excellent
31B Dense
The flagship delivers on its promise. #3 on Arena AI, exceptional reasoning and coding, strong multimodal. The best open dense model at this size.
Strengths: reasoning, math, coding, multimodal. Weakness: SWE-Bench lags behind Qwen 3.6.
Verdict: Best value
26B MoE
Near-31B quality at a fraction of the compute. The sweet spot for production deployment. Fits on a single RTX 4090.
Strengths: efficiency, near-31B quality, single-GPU deployment. Weakness: slower than dense at low batch sizes.
Verdict: Impressive
E4B Edge
The recommended edge model. Strong reasoning and coding for its size. Native audio is a unique advantage over competitors.
Strengths: audio support, good reasoning, runs on laptops. Weakness: limited for complex tasks.
Verdict: Niche but useful
E2B Compact
Blazing fast at 95 tok/s. Useful for simple tasks and real-time applications. Not for complex reasoning.
Strengths: speed, tiny footprint, audio support. Weakness: quality drops on harder tasks.
What works
Where Gemma 4 excels
After testing across dozens of real-world tasks, these are the areas where Gemma 4 genuinely impresses.
Math reasoning
89.2% on AIME 2026 is not a fluke. The thinking mode produces clear, step-by-step solutions. Genuinely useful for math tutoring and problem solving.
Code generation
80% on LiveCodeBench v6 translates to practical coding assistance. Function implementations, debugging, and code review are all strong.
Multimodal understanding
Image analysis, document parsing, and chart comprehension work well. Variable resolution support means it handles different image types gracefully.
Local deployment
The range from 3.2GB to 17GB (at 4-bit) means there's a model for every hardware tier. Ollama setup takes under 2 minutes.
Function calling
Native function calling is reliable. JSON output is well-formed, tool selection is accurate, and multi-step agent workflows work consistently.
Multilingual
140+ language support is genuine. Quality holds up well across major languages, not just English.
Honest assessment
Where Gemma 4 falls short
No model is perfect. Here's where Gemma 4 has room to improve.
Weaknesses
- SWE-Bench: 52% vs Qwen 3.6's 73.4% - significant gap on autonomous coding
- No native audio on 26B and 31B - only edge models have audio encoders
- 26B MoE is slower than expected at low batch sizes
- E2B quality drops noticeably on complex reasoning tasks
- Long-context performance degrades beyond ~100K tokens in practice
Competition
- Qwen 3.6 35B A3B: Better at agentic coding (SWE-Bench, Terminal-Bench)
- GLM-5.1: Stronger on some Chinese language tasks
- Llama 4: Larger context window options
- DeepSeek V4: Competitive on reasoning benchmarks
- Mistral Small 4: Faster inference at similar quality tiers
Benchmarks
Official benchmarks vs real-world experience
How do the official numbers translate to actual use? Here's our assessment after extensive testing.
Official benchmarks tell part of the story. Real-world testing reveals where the numbers match experience and where they don't.
Math reasoning: benchmarks match reality - the thinking mode genuinely helps
Coding: strong on generation, weaker on autonomous editing (SWE-Bench gap)
Multimodal: image understanding is solid, document OCR works well
Speed: E2B is genuinely fast (~95 tok/s), 26B is slower than expected locally
Performance reality check
Gemma 4 vs the competition
How Gemma 4 31B compares to other leading open models on key benchmarks.
| Benchmark | Gemma 4 31B Featured | Gemma 4 26B | Qwen 3.6 35B | Llama 4 Scout |
|---|---|---|---|---|
MMLU Pro Knowledge | 85.2% | 82.6% | 83.1% | 74.3% |
AIME 2026 Math | 89.2% | 88.3% | 81.5% | 73.0% |
LiveCodeBench v6 Coding | 80.0% | 77.1% | 75.2% | 53.0% |
SWE-Bench Verified Agentic coding | 52.0% | - | 73.4% | - |
MMMU Pro Multimodal | 76.9% | 73.8% | 70.2% | 57.5% |
Arena AI ELO Overall | 1452 | 1441 | ~1440 | ~1380 |
Benchmark data from official model cards and independent testing. Scores may vary by evaluation methodology.
Reasoning
Math and science reasoning: genuinely impressive
The thinking mode on the 31B model produces clear, step-by-step solutions that are easy to follow and verify. 89.2% on AIME 2026 translates to real-world math tutoring capability.
- Thinking mode shows clear reasoning chains
- Handles multi-step problems with good accuracy
- Science reasoning (GPQA Diamond 84.3%) is strong
Coding
Strong code generation, weaker autonomous editing
Gemma 4 excels at code generation, debugging, and explanation. But on autonomous code editing tasks (SWE-Bench), it falls behind Qwen 3.6 significantly. If you need an AI coding agent, Qwen 3.6 is currently better.
- Code generation and debugging: excellent (80% LiveCodeBench)
- Function calling for agents: reliable and well-formed
- Autonomous code editing: weaker (52% vs Qwen's 73.4% SWE-Bench)
Local Use
The best open model family for local deployment
No other model family covers the range from phone to workstation as well as Gemma 4. The E2B runs at 95 tok/s on consumer hardware, and the 26B fits on a single RTX 4090 with near-31B quality.
- E2B: blazing fast, fits on phones, but limited on complex tasks
- E4B: the sweet spot for laptop users, good all-around quality
- 26B: near-31B quality on a single GPU, but slower than expected
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Comparisons
How Gemma 4 compares
Detailed comparisons with competing models.
Resources
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Deep dives into Gemma 4 architecture and capabilities.
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