AgentBlog

Google DeepMind’s Gemma 4 is one of the more interesting open-model releases in a while because it focuses on something developers actually care about: capability per parameter.

This is not just another open chat model. Gemma 4 is positioned for reasoning, coding, multimodal input, function calling, structured output, and agentic workflows. It also ships under an Apache 2.0 license, which makes it far easier to take seriously for commercial and enterprise use.

For AI developers, the real questions are:

• How good are the benchmarks?
• Which variant matters most in practice?
• How does it compare with Qwen, DeepSeek, Llama, and Mistral?
• Is it actually worth building with?

The model family

Gemma 4 comes in four main variants:

• E2B
• E4B
• 26B-A4B MoE
• 31B Dense

The two small models use effective parameter sizing. Google says they rely on Per-Layer Embeddings (PLE) to improve efficiency for on-device use.

The 26B-A4B model is the most interesting engineering play in the family. It is a Mixture-of-Experts model with 26B total parameters, but Google says only about 3.8B parameters are active during inference. That makes it especially relevant for developers balancing quality, latency, and serving cost.

The 31B Dense model is the flagship quality-first variant and is the one behind many of Google’s strongest benchmark claims.

What Gemma 4 is trying to do

Google is not pitching Gemma 4 as just a local chat model. The family is meant for:

• advanced reasoning
• multi-step planning
• tool use and agents
• function calling and JSON output
• offline/local code generation
• multimodal understanding
• long-context tasks

According to Google’s release materials:

• all models support images and video
• E2B and E4B also support audio input
• edge models have 128K context
• larger models go up to 256K context
• the family supports 140+ languages

That gives Gemma 4 a broader product story than most open releases. It is trying to span phones, edge devices, laptops, workstations, and cloud deployments.

Published benchmark numbers

Here are the key benchmark scores Google published:

• Arena AI (text): 31B 1452, 26B-A4B 1441
• MMMLU: 31B 85.2, 26B-A4B 82.6, E4B 69.4, E2B 60.0
• MMMU Pro: 31B 76.9, 26B-A4B 73.8, E4B 52.6, E2B 44.2
• AIME 2026: 31B 89.2, 26B-A4B 88.3, E4B 42.5, E2B 37.5
• LiveCodeBench v6: 31B 80.0, 26B-A4B 77.1, E4B 52.0, E2B 44.0
• GPQA Diamond: 31B 84.3, 26B-A4B 82.3, E4B 58.6, E2B 43.4
• τ2-bench: 31B 86.4, 26B-A4B 85.5, E4B 57.5, E2B 29.4

Google also says:

• 31B is the #3 open model on Arena AI’s text leaderboard
• 26B-A4B is the #6 open model

What matters in those benchmarks

The AIME 2026 scores suggest the larger Gemma 4 models are serious reasoning systems, not toy models pretending to reason.

The LiveCodeBench v6 numbers matter directly for developers. They place Gemma 4 firmly in the conversation for:

• coding assistants
• code repair loops
• local code generation
• agentic developer tools

The GPQA Diamond results support the broader claim that Gemma 4 is not merely optimized for style or chat preference.

The τ2-bench numbers may be the most product-relevant of all, because they suggest Gemma 4 could be strong for function calling, workflow automation, tool-augmented systems, and structured execution pipelines.

The biggest pattern: 26B-A4B stays close to 31B

Across the published table, the 26B-A4B MoE stays surprisingly close to the 31B Dense flagship.

That matters because most developers do not optimize for benchmark ceilings alone. They optimize for:

• quality
• latency
• memory footprint
• cost
• deployment complexity

If 26B-A4B is consistently near-flagship quality while requiring less active compute, it may be the most practical model in the family for real deployment.

How it compares with other open models

Google’s benchmark table mostly compares Gemma 4 against Gemma 3, not directly against the full field. So the honest comparison is strategic rather than absolute.

vs Qwen

Qwen is one of the most relevant competitors because it is strong in coding, reasoning, multilingual tasks, and long-context usage. Gemma 4 looks competitive with Qwen-class models, especially in the 31B and 26B-A4B variants.

vs DeepSeek

DeepSeek is strong on reasoning and coding efficiency. Gemma 4’s edge is the broader family story: multimodality, edge-capable small models, agentic positioning, and Apache 2.0 licensing.

vs Llama

Llama still matters because of ecosystem reach. But ecosystem inertia is not the same thing as best-in-class performance. Gemma 4’s published numbers suggest the larger variants are highly competitive with similarly sized Llama-family models in reasoning-heavy workloads.

vs Mistral

Mistral remains relevant for efficient deployment and practical inference. Gemma 4 appears more ambitious around multimodality, context, and agentic use cases.

Why the small models matter

Most coverage will obsess over the 31B, but the E2B and E4B models may matter more if Google’s claims hold up. They combine:

• small effective footprint
• multimodal support
• audio input on-device
• long context
• offline execution

That opens the door to mobile assistants, robotics, embedded systems, private enterprise edge tools, and local multimodal workflows.

Final take

Gemma 4 is one of the more important open-model releases for developers because it combines:

• strong benchmark performance
• practical model sizes
• multimodal capability
• local and edge deployment ambition
• agentic workflow support
• permissive Apache 2.0 licensing

The 31B Dense is the benchmark flagship.

The 26B-A4B MoE looks like the likely practical sweet spot.

The E2B and E4B may be the most strategically important if edge and offline multimodal use cases keep growing.

The right reaction to Gemma 4 is neither dismissal nor hype. It is this: this is a model family serious enough to benchmark, prototype with, and potentially deploy—especially if you care about local-first AI, structured tool use, and efficient open models.