This is a submission for the Gemma 4 Challenge: Write About Gemma 4
Gemma 4 Complete Guide 2026
Gemma 4 is shaping up to be the most consequential open weight model release of the year, and the headline is not just the leaderboard scores. Google shipped four model sizes, native multimodality, a 256K context window on the larger variants, and for the first time in the Gemma line, a clean Apache 2.0 license.
For engineering teams that have been waiting on an open weight model good enough to actually replace a frontier API for a meaningful slice of their workload, this is the first credible candidate from Google.
This guide is the long version. It walks through what the family looks like,
i) what the architecture actually does,
ii) what the benchmark numbers mean in practice,
iii) how it stacks up against Llama 4, Qwen 3.5, DeepSeek V4 Flash and its own predecessors,
iv) where to host it, and where it falls short.
v) If you are evaluating Gemma 4 for production, this is the document you can hand to your team.
TL;DR, the Quick Read
Before we dive into the long version, here is the snapshot you can keep in your back pocket.
Released: April 2, 2026, by Google DeepMind.
The family ships in four sizes, namely Gemma 4 E2B (around 2.3B effective), E4B (around 4.5B effective), 26B A4B (a Mixture of Experts with 4B active), and a dense 31B. Licensing has finally moved to Apache 2.0, which is a meaningful change because earlier Gemma generations shipped under the custom Gemma Terms of Use that often made enterprise legal review painful.
Context windows reach 128K tokens on the E2B and E4B variants, and 256K on the 26B A4B and the 31B. Every variant takes text plus image input, while E2B and E4B additionally take audio. Output, on every variant, is text only.
On the strong side, Gemma 4 nails reasoning, math (AIME 2026 ~89%), code generation (LiveCodeBench v6 ~80%), long context recall, and on device deployment via MediaPipe and LiteRT. On the weak side, it trails Qwen 3.5 27B on SWE-bench Verified, has no native speech output, and a reminder worth repeating, Gemma is not Gemini, so fine tuning, weights and serving become your problem.
Watch the offical demo here:
What Gemma 4 Is, And How It Differs From Gemini
Gemma is Google's open weight model family. Gemini is Google's closed, hosted, frontier model family. They share research lineage, in fact Google describes Gemma 4 as built from Gemini 3 research, but the deployment story is genuinely different.
With Gemini, you call an API, you pay per token, you do not get the weights, and you cannot fine tune the underlying parameters, you get adapters at best. With Gemma 4, you download the weights from Hugging Face, Kaggle or Ollama, you run them on your own hardware (or a cloud GPU you rent), you fine tune fully, and your unit economics become GPU hours and electricity rather than per token API spend.
The practical implication is straightforward.
i) Reach for Gemma 4 when you need on device inference, when you need to fine tune on private data, when your token volume makes a hosted API uneconomical, or when you need an air gapped deployment.
ii) Reach for Gemini when you want zero ops frontier intelligence and you are happy to pay for it.
The Gemma 4 Family
Four sizes, two architectural patterns (dense and MoE), and a clear split between the edge and server tiers.
| Variant | Architecture | Total / Active params | Context | Modalities in | Primary target |
|---|---|---|---|---|---|
| Gemma 4 E2B | Dense | ~2.3B effective | 128K | Text, image, audio | Phones, IoT, low power laptops |
| Gemma 4 E4B | Dense | ~4.5B effective | 128K | Text, image, audio | High end phones, edge servers, Raspberry Pi class |
| Gemma 4 26B A4B | Mixture of Experts | 26B total / ~4B active per token | 256K | Text, image | Single high end GPU server, cost sensitive throughput |
| Gemma 4 31B | Dense | 30.7B | 256K | Text, image | Quality first server inference, fine tuning |
A small naming clarification first. The E in E2B and E4B stands for edge, not experts. These are dense models built for on device.
The 26B A4B is the actual MoE in the family. Roughly 4 billion parameters fire on any given forward pass, so latency and cost behave like a 4B model, while quality benefits from the full 26B parameter pool. The 31B is the no tricks dense model, slower than the MoE, but typically the highest quality answer when you need the best response per query rather than the best response per dollar.
Architecture, Context Window, And Tokenizer
Gemma 4 keeps the decoder only transformer skeleton that has defined the family,
but it tightens almost every component.
A few highlights worth knowing before you read the model card.
a) Hybrid attention. Gemma 4 interleaves local sliding window attention with full global attention, with the final layer always global. Smaller dense models use 512 token sliding windows, and larger ones use 1024. This is the trick that makes the 256K context feasible without VRAM blowing up linearly.
b) RULER long context recall. On RULER at 128K, Gemma 3 scored 13.5%. Gemma 4 scores 66.4% on the same test. The context window is not just nominal, it actually retrieves at depth.
c) Vocabulary. A 262,144 token vocabulary, BPE with byte fallback. Strong multilingual coverage across more than 140 languages.
d) Vision tokens. A variable visual budget per image, namely 70, 140, 280, 560 or 1120 tokens, so you trade quality against context spend.
e) Audio (E2B and E4B only). Native speech recognition and audio understanding, with no separate ASR layer required for many use cases.
f) Reasoning mode. Gemma 4 can produce more than 4,000 tokens of explicit reasoning before committing to an answer, plus native function calling and structured JSON output.
The MoE in the 26B A4B is the architectural story to internalise. It lets a single A100 80GB or two consumer GPUs serve a model that punches well above 4B in quality terms, at roughly 4B in cost terms. That is the new dominant design point for the open weight server tier in 2026.
License, Apache 2.0, Finally
Read this section carefully if you have ever had Legal kill a Gemma rollout.
Earlier Gemma releases shipped under the Gemma Terms of Use, a custom license. It was more permissive than Llama 2's, but it included a Prohibited Use Policy with clauses around harm to minors, attacks on critical infrastructure, generation of CSAM, and other broad carve outs. The clauses were defensible in spirit, but enterprise legal teams routinely flagged the language as ambiguous and asked for indemnification or scope limiting before signing off. That friction kept Gemma out of plenty of production stacks.
Gemma 4 ships under Apache 2.0. No custom restrictions, no usage carve outs, and no monthly active user thresholds the way the Llama 4 Community License has.
Apache 2.0 explicitly grants commercial use, modification, redistribution, and distribution of derivative works including derivative weights. There is one obvious constraint that still applies, namely that Apache 2.0 does not grant trademark rights, so you cannot ship a product called "Gemma" or imply Google endorsement.
This is materially less restrictive than the previous Gemma Terms of Use, and noticeably less restrictive than Llama 4's Community License (which is free for organisations under 700M monthly active users but adds compliance language). For most engineering teams, this is the change that turns Gemma from interesting into approvable.
Two caveats worth being honest about.
a) Apache 2.0 governs the weights, it does not give you the training data or the training pipeline. Gemma 4 is open weight, not open source in the strict OSI sense applied to data.
b) Google can still publish acceptable use guidelines separately, nothing about Apache 2.0 prevents that. Today, the license file in the repo is the controlling document, and that document is Apache 2.0.
Benchmarks That Actually Matter
The headline numbers for Gemma 4 31B (instruction tuned) are pulled from Google's model card, plus the independent reproductions surfaced in the LM Studio and Hugging Face threads.
| Benchmark | Gemma 4 31B | Gemma 3 27B | Llama 4 Scout (109B) | Qwen 3.5 27B | DeepSeek V4 Flash |
|---|---|---|---|---|---|
| MMLU-Pro | 85.2 | ~67 | ~78 | 86.1 | ~84 |
| GPQA Diamond | 84.3 | 42.4 | ~70 | 85.5 | ~80 |
| LiveCodeBench v6 | 80.0 | 29.1 | ~55 | ~78 | ~74 |
| SWE-bench Verified | ~63 | ~22 | ~48 | 72.4 | ~64 |
| AIME 2026 (math) | 89.2 | 20.8 | ~55 | ~85 | ~82 |
| Codeforces ELO | 2,150 | 110 | ~1,500 | ~1,950 | ~1,800 |
Approximate values for the non Gemma rows are pulled from each project's own card or the Artificial Analysis index, treat them as directional. The story they tell is consistent, and it boils down to four observations.
i) Gemma 4 31B is in the same neighbourhood as Qwen 3.5 27B on knowledge and reasoning, they trade leadership benchmark by benchmark.
ii) Gemma 4 has the upper hand on math and competitive programming.
iii) Qwen 3.5 27B still wins SWE-bench Verified, the benchmark that most closely tracks can this model close a real GitHub issue. If your primary use case is autonomous code editing on real repos, evaluate Qwen 3.5 alongside Gemma 4 before you commit.
iv) Gemma 4's gain over Gemma 3 is enormous, with multiple benchmarks improving 3 to 20 times. Most teams running Gemma 3 in production should plan a migration window.
Where To Run Gemma 4
There are three deployment surfaces to think about, namely hosted, self hosted server, and on device.
a) Hosted
If you want zero ops, the model is a one line call away on several providers.
Vertex AI (Model Garden) is the first party path. You can fine tune on Vertex AI Training Clusters and serve through Model Garden endpoints, paying for compute time on the underlying accelerator (
A2/G2family orTPUs).
For prototyping and price sensitive batch work, OpenRouter aggregates more than eleven providers for the 26B A4B model at roughly $0.06 per million input tokens and $0.33 per million output. Beyond that, Together AI, Fireworks, Groq, DeepInfra and Hugging Face Inference all run Gemma 4 endpoints, and pricing varies though the open weight competitive market keeps it low. For spiky workloads, Cloud Run with GPU, Google's serverless GPU runtime, can host Gemma 4 with scale to zero, which is genuinely attractive when traffic is bursty.
b) Self hosted server
vLLM is the production default. It supports Gemma 4 on NVIDIA, AMD, and Google Cloud TPUs from day one. The approximate hardware floors look like this.
| Variant | Quant / format | VRAM floor | Notes |
|---|---|---|---|
| 26B A4B | AWQ INT4 | ~15 GB | RTX 4090 24 GB with KV cache headroom |
| 26B A4B | GGUF Q4_K_M | ~16 GB | llama.cpp / Ollama dev box |
| 26B A4B | FP16 | ~52 GB | A100 80GB or H100, serves at full quality |
| 31B dense | FP16 | ~62 GB | A100 80GB or H100 single GPU |
| 31B dense | INT4 | ~18 GB | RTX 4090 / 5090, viable for single user inference |
Ollama covers the local laptop use case for E2B, E4B, and the quantised 26B and 31B. MLX with Metal acceleration runs all variants on Apple Silicon, an M3 Max or M4 Pro with 32 to 64 GB unified memory will run the 26B A4B comfortably. AMD has day zero Gemma 4 support across ROCm and the Ryzen AI stack. NVIDIA NIM, NeMo, LM Studio, Unsloth, SGLang and LiteRT-LM all have first class support too.
c) On device with MediaPipe and LiteRT
The E2B and E4B variants are explicitly designed for phones and edge devices.
The deployment stack is MediaPipe's LLM Inference API on top of LiteRT,
which handles model loading, memory and hardware acceleration (GPU or NPU) automatically.
The approximate footprints are nicely small.
E2B Q4_K_M, around
~1.3 GBon disk, with2 to 3 GBRAM at runtime.
E4B Q4_K_M, around~2.5 GBon disk, with4 to 5 GBRAM at runtime.
This is the path for AI features that work without a network round trip,
voice agents on Android, in browser RAG over a user's local documents, and offline coding helpers.
With audio input native to E2B and E4B, you can ship a meaningful voice to text to action loop without bundling a separate ASR model.
When To Choose Gemma 4 Over Alternatives
Reach for Gemma 4 when the following conditions hold.
a) You need an Apache 2.0 model. If Legal balked at Gemma 3's terms or Llama's Community License MAU clause, Gemma 4 is the cleanest option in this size class.
b) You need on device multimodality. The audio capable E2B and E4B variants are the strongest open weight option for phones today.
c) Long context matters. 256K with credible RULER recall is competitive with hosted frontier models.
d) Math, agentic reasoning or competitive programming dominate your workload. Gemma 4 31B's AIME and Codeforces numbers are exceptional for an open weight model in this size band.
Choose something else when the workload looks more like one of these.
a) Your workload is autonomous repo editing. Qwen 3.5 27B's SWE-bench Verified lead is real. Pilot both before committing.
b) You need streaming voice output. Gemma 4 has audio in but not out. Qwen 3.5 Omni handles real time speech generation.
c) You need a frontier model. If quality is the only metric, hosted Gemini 3 Pro or DeepSeek V4 Pro will outperform Gemma 4 31B on most benchmarks.
d) Cost per token at huge scale. DeepSeek V4 Flash hosted is cheap enough that for many workloads the spend math beats running your own GPUs.
Known Issues And License Caveats
No model is a free lunch, and Gemma 4 has its own quirks. Worth reading before you commit.
i) SWE-bench Verified is not the strong suit. Real GitHub issue resolution still trails Qwen 3.5 27B by a meaningful margin.
ii) No native audio output. If you want a voice agent that talks back, you bolt on a separate TTS layer.
iii) 26B A4B throughput surprise. Despite only 4B active parameters, community benchmarks on consumer GPUs show roughly 11 tok/s on an RTX 4090, slower than a comparable dense 4B model. The MoE routing overhead is real on consumer hardware. On A100 and H100 the gap closes.
iv) Apache 2.0 is not open source training data. The weights are open and commercially usable, the training corpus is not. If your compliance posture requires reproducibility from data, Gemma 4 does not satisfy that.
v) Trademark. You cannot brand your product as "Gemma" or use Google trademarks. Apache 2.0 explicitly excludes trademark grants.
vi) Vision token budget tradeoff. The 70, 140, 280, 560 and 1120 visual budgets are real. Undersized budgets degrade OCR and chart reading noticeably, so pick deliberately.
vii) Native dependency surprises. If you self host with vLLM behind a Node service, watch out for prebuilt binary fetch issues on locked down installs, where the failure mode is silent at install time and loud at runtime.
viii) Tokenizer drift from Gemma 3. The 262K vocabulary is not directly weight compatible with Gemma 3 fine tunes. Plan a re finetune, do not try to port adapters.
FAQ
Is Gemma 4 actually open source?
It is open weight under Apache 2.0. The weights, model card and inference code are open and commercially usable. The training data and full pipeline are not released. By the OSI's strict definition, that is open weight, not open source, but for most commercial deployment purposes Apache 2.0 is the cleanest license you will see in this size class.
Is the Gemma 4 license really Apache 2.0?
Yes. This is the change from earlier Gemma versions, which used the custom Gemma Terms of Use with usage carve outs. Gemma 4's repository ships the standard Apache 2.0 license file. Anyone telling you Gemma 4 has restrictive terms is describing the previous generation.
What is the difference between Gemma 4 and Gemini?
Gemma 4 is open weight and self hostable. Gemini is a closed, hosted, frontier model. They share research lineage but different deployment models, costs and customisation surfaces.
Which Gemma 4 model should I pick?
a) E2B for phones and tight memory budgets.
b) E4B for high end edge and small servers.
c) 26B A4B for cost efficient single GPU server inference.
d) 31B dense for the highest quality answers when you do not care about throughput.
What hardware do I need to run Gemma 4 31B?
FP16 needs roughly 62 GB VRAM, an A100 80GB or H100. INT4 quantised drops that to about 18 GB, fitting an RTX 4090 or 5090 for single user inference.
Does Gemma 4 support function calling?
Yes. Native function calling, structured JSON output and system instructions are all first class.
How does Gemma 4 compare to Llama 4?
Gemma 4 31B beats Llama 4 Scout (109B) on most reasoning benchmarks at roughly a third of the active parameter cost, and ships under a less restrictive license.
Is Gemma 4 better than Qwen 3.5?
It depends on the workload. Gemma 4 wins on math and competitive programming, Qwen 3.5 27B wins on MMLU-Pro, GPQA Diamond and SWE-bench Verified. Both are Apache 2.0. Pilot both.
Is Gemma 4 multimodal?
All variants accept text and image. E2B and E4B also accept audio. Output is text only on every variant.
What is the context window?
128K tokens on E2B and E4B, and 256K on the 26B A4B and the 31B. RULER long context recall at 128K is roughly 66.4%, a 5x improvement over Gemma 3.
Can Gemma 4 run on a phone?
Yes. E2B and E4B are designed for it. MediaPipe's LLM Inference API and LiteRT handle on device inference with NPU and GPU acceleration on Android, and equivalent paths exist on iOS via Core ML and MLX.
What is Gemma 4n?
Gemma 4n is the community shorthand for the E2B and E4B edge variants, the on device tier of the Gemma 4 family. Architecturally they are dense models tuned and quantised for phones and embedded devices. See Gemma 4n vs Gemma 4 for the side by side.
Is Gemma 4 safe for commercial production use?
Yes, under Apache 2.0, with the standard caveats. Respect trademarks, do not redistribute the model under the Gemma name, and follow your own jurisdiction's AI usage law. There are no usage carve outs, no MAU thresholds, and no industry restrictions in the license itself.
Should I migrate from Gemma 3 to Gemma 4?
If you are running Gemma 3 in production, yes. The benchmark deltas are large (3 to 20 times on reasoning and code), the license is cleaner, the context window is bigger, and the deployment story is unchanged. Plan a re finetune, since adapter weights will not transfer cleanly.
Closing Thoughts
Picking the right open weight model is the easy half of the job. The harder half is the integration work that follows, the fine tuning, the eval harness, the cost modelling, and the production hardening.
Gemma 4 makes that work meaningfully easier than its predecessor.
✔️The license is clean,
✔️the model card is honest,
✔️the deployment surface is broad,
✔️and the benchmarks are competitive with the best of the open weight field.
If you have been holding out on Gemma because of legal friction or quality gaps, this is the release that closes both. Pilot it against your real workload, compare it head to head with Qwen 3.5 27B on the tasks that matter to you, and let your evals decide.
pls share your thoughts below with ur use cases. thanks for reading so far 💖