Gemma-4

由 Google DeepMind released 的最next-gen open-source large model

large language model Google

https://deepmind.google/models/gemma/gemma-4/

Visit Website

In-depth Report

Gemma 4 is the latest generation of open source large model family released by Google DeepMind on April 2, 2026. It is built based on Gemini 3 research and technology and is positioned as "the strongest open source model at the same parameter scale." This series includes four sizes: E2B, E4B, 26B MoE, and 31B Dense. It adopts the Apache 2.0 license for the first time to achieve full open source commercial use. The 31B version ranks third in the world among LMSYS Chatbot Arena open source models with 1452 Elo, second only to super large closed-source models such as GLM-5 and Kimi 2.5. It uses less than one-thirtieth of the parameters to achieve a level of intelligence comparable to 600B+ level models. The Gemma series has been downloaded more than 400 million times since its release in 2024, and has spawned more than 100,000 variant models.
Google DeepMind first released the Gemma series of open models in February 2024, and has maintained an annual iteration rhythm since then. Gemma 4 is the third generation product of this series. The official release time is April 2, 2026. The publisher is Google DeepMind. The code is based on Google’s own framework training such as Jax and Keras. It is worth noting that Gemma 4 for the first time abandoned Google’s previous proprietary custom license and adopted the OSI (Open Source Initiative) certified Apache 2.0 license. This change was regarded by the community as an important signal for Google to gain the trust of developers in the open source ecosystem. Gemma 4 shares underlying research technology with Google's closed-source flagship Gemini 3 and is currently the most performant Google open model that can run on local hardware.
The Gemma 4 series introduces a number of innovative designs in the model architecture, forming obvious differentiation capabilities from other open source models. About model specification and architectural innovation. Gemma 4 is available in four sizes: E2B (total parameters 5.1B, activations approximately 2.3B), E4B (total parameters approximately 8B, activations approximately 4.5B), 26B MoE (total parameters 25.2B, activations only 3.8B, using 128 expert mixed expert routing) and 31B Dense (total parameters 30.7B). Among them, 26B MoE uses a sparse activation mechanism to achieve an inference speed that is 2.5 times faster than that of dense models with the same capabilities. In terms of context support, 26B and 31B support the longest 256K token context, with alternating local sliding window attention (local layer 512-1024 Tokens) and global full context attention, as well as dual RoPE position encoding (standard RoPE for the sliding window, proportional RoPE for the global layer), and the accuracy rate remains above 99% in the 256K full load "needle in the haystack" test. Per-Layer Embedding technology allows E2B to be quantized and compressed to less than 1.5GB, allowing it to run completely offline on the mobile terminal. AICore on Android devices can achieve an inference speed of more than 40 token/s. About core functionality. Gemma 4 supports three core capabilities: First, Agentic Workflows, which natively supports Function Calling, JSON output, and System instructions. The model inherently knows how to use the tools, rather than following post-simulation through instructions. The τ2-bench agent tool usage benchmark jumped from 6.6% in Gemma 3 to 86.4%; second, Multimodal Reasoning, with about 550M built-in Parametric visual encoder supports three-modal input of text, image, and audio. The 31B version also supports video sequence input of up to 60 seconds (currently the only model of the same parameter level that supports video input). The third is multi-language support, claiming to cover 140+ languages, significantly surpassing the previous generation in multi-language understanding benchmarks.About deployment and ecology. Model weights can be obtained through mainstream channels such as Hugging Face, Ollama, Kaggle, LM Studio, and Docker. The training and deployment framework supports Jax, Vertex AI, Keras, Google AI Edge, Google Kubernetes Engine, etc. Security-wise, the Gemma 4 model employs the same rigorous infrastructure security protocols as Google's closed-source model, with transparent security whitepapers available to enterprises and sovereign organizations.
All size versions of Gemma 4 are completely free to use, including for commercial purposes. The Apache 2.0 license allows users to freely download, modify, and use commercially as long as they retain appropriate copyright notices, without paying any fees to Google, without setting usage caps or enforcing API call paths. This model is in interesting contrast with Google's traditional cloud service monetization path - Google expands the use of its own training and deployment frameworks such as Jax, Vertex AI, and Google Kubernetes Engine through an open and free model, thereby reaping rewards on the cloud service side. For enterprises, the Apache 2.0 license eliminates legal review friction and compliance risks, and is particularly suitable for financial, medical, and government sectors that have strict requirements for security audits of model training data.
Gemma 4 has triggered extremely heated discussions in the developer community, mainly focusing on three dimensions of feedback. In terms of positive reviews, the Apache 2.0 license has received almost unanimous approval and is considered a key breakthrough that "can finally be used commercially with confidence." The Hugging Face team stated in the review that the out-of-box quality of Gemma 4 is “so high that it is difficult to find sufficiently representative fine-tuning examples,” suggesting that the model has absorbed a wide enough range of capability coverage in the pre-training stage. Multi-language capability is another highlight. Support for 140+ languages makes Gemma 4’s generalization in non-English task scenarios significantly better than competing products. The 31B version of Codeforces has an ELO of 2150, significantly ahead of similar open source models in programming competition tasks. AIME 2026 mathematical reasoning reached 89.2%, an increase of 68 percentage points compared to Gemma 3's 20.8%. In terms of criticism, inference speed is the main drawback: the measured throughput of the 26B MoE version is only about 11 token/s, which is far lower than the performance of Qwen 3.5 of about 60 token/s on the same hardware. This is partly because Gemma 4 has not yet publicly confirmed to support acceleration technologies such as MTP (multi-step prediction) and speculative decoding. The memory pressure of KV Cache in long context scenarios is also a pain point: under 256K full load, KV Cache alone requires about 20.8 GiB of video memory, and even after weight quantization, the hardware requirements are still high. In addition, there are certain engineering frictions in the initial release of Gemma 4. For example, certain quantitative versions may have garbled characters, and some functions rely on newer framework versions. The community also has some controversy over the objectivity of the selection of evaluation benchmarks, calling on Google to disclose more training data and evaluation methodologies.
The release of Gemma 4 has made significant waves in the open source large model landscape. From the competitive product landscape, Gemma 4 31B and Qwen 3.5 27B are currently the most watched direct competitors in the same category. Both adopt the Apache 2.0 protocol, support thinking mode, and are free for commercial use. Qwen 3.5 is slightly ahead on traditional closed-book benchmarks (GPQA Diamond, LiveCodeBench), and Gemma 4 clearly wins on human conversation preferences (Arena AI Elo); Gemma 4 is unique in multi-modal, especially video input support, and Qwen 3.5 has an even greater advantage in ultra-long context (1 million token) support; in terms of Chinese ecological depth and tool chain maturity, Qwen 3.5 Still has a moat. The release of Gemma 4 was interpreted by some observers as Google's attempt to regain the right to speak in the field of open source models through a "more open" attitude. Previously, Google's actions in the field of open source were relatively conservative, while Meta's Llama series has accumulated a stronger developer ecosystem with a more radical open strategy.
There are several potential issues with Gemma 4 that need to be viewed rationally. The first is the controversy over the tendency of evaluation benchmarks. Some community voices question Google’s selective disclosure of benchmark data that is beneficial to itself. Some data are not fully disclosed, which affects the outside world’s comprehensive evaluation. The second is the opacity of the training process. Google has not fully disclosed the training data source, RLHF methodology and post-training process of Gemma 4. It faces similar information disclosure pressure as Llama 4. The third is the substantial challenge of hardware threshold. The 31B Dense version still requires about 24GB under 4-bit quantification. The KV Cache pressure of graphics memory and long context scenarios makes it difficult to achieve high concurrency on consumer-grade 24GB graphics cards; fourth, there are shortcomings in reasoning efficiency. It lags behind Qwen 3.5 in terms of engineering optimizations such as MTP acceleration and speculative decoding, which may affect enterprise selection decisions.
Scenarios where Gemma 4 is strongly recommended include: developers who need multi-language task support (especially non-English businesses, Gemma 4 has obvious advantages in multi-language understanding), teams with strong compliance requirements (industries such as finance, medical, government, etc. that require complete security white papers), multi-modal application developers who need video understanding capabilities (31B is currently the only model of the same parameter level that supports 60-second video input), and algorithm competition learners (Codeforces ELO 2150 Significantly better than similar products), and enterprises that need fine-tuning bases (31B Dense is positioned as a fine-tuning base with high pre-training quality). Situations that may need to consider other options include: developers with budget-sensitive or average hardware (Qwen 3.5's inference speed and quantitative maturity are more cost-effective), Chinese-dominated business teams (Qwen has deeper accumulation and complete evaluation data on Chinese alignment), extremely long context users (Qwen 3.5's 1 million token context window is 4 times that of Gemma 256K), and production environments that are sensitive to inference delays (Qwen 3.5 The measured throughput on vLLM has obvious advantages).
Gemma 4 is a strategic piece that Google has invested in the field of open source large models. With Apache 2.0 fully open source, 31B parameters comparable to 600B+ closed source model performance, and multi-language and multi-modal differentiation capabilities, it has reshaped the value benchmark of open source models. It is not everyone’s first choice for “brainless migration”, but it has built real competitiveness in specific dimensions such as multi-language understanding, video understanding, and compliance commercial use. For teams that have run stable workflows on Qwen 3.5, Gemma 4 is currently more like a "backup engine worthy of high attention and small-scale evaluation" rather than the ultimate answer that must be immediately migrated.

User Reviews

SolanaSoul564_n

—

After changing the local agent stack from Qwen 3.5 to Gemma 4 26B MoE, I really can’t go back. My dual 3090 runs semantic routing and universal dialogue. I originally used Qwen 3.5 4B and 30B A3B. After replacing it with Gemma 26B A4B and E4B, the multi-round agent workflow is ridiculously stable, and I can also write demos like Doom ray tracing. The migration post of r/LocalLLaMA with 353 likes is right. The local layer is not about ranking scores, but who can fill the video memory and respond quickly under quantification. Gemma's MoE architecture is born for this.

Judith_Bennett_X0

—

Here’s a reminder: the old version of Ollama running Gemma 4 directly gave a 404, the model was not loaded at all, and the VRAM only occupied 3MB. Gemma 4 only entered Ollama in April 2026. The special tokenizer is not recognized by the old runner and must be upgraded to 0.20.0 or above. Just run curl to reinstall. Don’t spend an hour troubleshooting an empty response like me.

SPowellJr

—

To be honest, the most popular thing about Gemma 4 has never been the running score, but Apache 2.0. The previous generations of Gemma used Google's own license. Corporate legal affairs shook their heads and the commercial boundaries were very blurred. This time, I switched directly to Apache 2.0, which is fully open for commercial use, modification, and redistribution. There is zero licensing fee, and the legal boilerplate is ready-made. For a company that wants to deploy privately, this is more practical than any Arena third party.

4557pp

—

E2B disconnects the Internet and runs translation on the mobile phone. It takes five seconds to take a picture of a Japanese medicine bottle in the subway and get the result. It’s so delicious.

CHcha2024

—

I used Ollama to run Gemma 4 on the M4 MacBook Air. The whole process was so "boring" that I was surprised - downloading, typing commands, and getting the results. There were no errors and no fan spinning. In the past, the local model looked like a technical disaster movie, but this time ordinary friends can install it themselves by following the steps. This kind of "boring" is real progress, indicating that local AI has finally said hello to ordinary people.

AlexanderMendoza_X

—

I need to pour cold water on speed. For the same 5060 Ti 16G, the Gemma 4 26B MoE is only 11 tok/s, and the Qwen 3.5 35B A3B is easily 60+, which is five or six times worse. Moreover, Gemma has always been tight on context memory, and the usage is higher under the same quantification. If the production environment is sensitive to latency, Qwen is more stable now, and we will wait until the optimization of vLLM and llama.cpp catches up.

JAdams_2023

—

I still can’t do it well with Qwen in Chinese, and I still fail when writing ancient poems and slang. CJK’s vocabulary is small, which is a flaw.

PamelaMurphy_X

—

Running Gemma 4 26B on the 4090, the continuous output can reach 160 tok/s, the video memory occupies just over 24G, and the temperature is over 50 degrees. It is completely sufficient for private code interpretation, writing small scripts, and preliminary code reviews. Complex refactoring and cross-warehouse planning are still left to the strong model in the cloud, and the local division of labor is responsible for high-frequency and low-risk privacy tasks.

AmandaRivera007

—

The end-side tri-modality is really fragrant. E2B/E4B natively supports text, images, and audio. With a Google AI Edge Gallery installed on your phone, you can still translate, view pictures, and write reports in airplane mode, and all data is retained locally. However, it should be noted that the large 31B/26B does not have audio capabilities. If you want to have offline voice, you have to use a small model. This product logic is a bit counter-intuitive.

CHall_20210

—

31B ranks third in the world's open source, and achieved 89.2% in AIME 2026. Mathematics is a god.

i0z1zarz

—

The built-in thinking mode is very easy to use. After turning on <think>, the mathematical logic is obviously stable. However, turning on deep thinking in the local small model will take a minute or two, and it is faster to turn it off for daily questions and answers. In addition, its multi-language is really strong. German, French, and Vietnamese are smoother than the Qwen 3.5 I tried. This is a real plus for making global products.

tfziukdfa42025

—

Gemma 4 31B is really great for running Claude Code. 70% of daily development tasks can be covered, and the monthly fee is directly saved.

CarterGomez_dev

—

Running AI offline on a mobile phone is finally no longer a gimmick. The E4B only has 3.2GB of memory and is usable in actual tests.

HaroldCooper

—

E4B mobile phone actual test! It really works when disconnected from the Internet, and the generation speed is much faster than the previous generation. Even if it’s a logical reasoning question, it still pulls my hips.

飞鸟639

—

Switching from Gemma 3, the 31B improvement is really huge. AIME jumps from 20% to 89%, which is a bit exaggerated.

海角791

—

Apache 2.0! Finally waiting for this day, Google really has a conscience this time, and enterprises no longer have to worry about legal affairs for commercial use

JWhite_Plus

—

The measured 26B MoE version only has 11 tokens per second, which is much slower than Qwen 3.5. The inference speed is a flaw.

mONIQUE927

—

After deploying it for an afternoon, it finally started running. Let me share the video memory requirements: 31B requires about 24GB of video memory using 4bit quantification, 26B MoE requires about 18GB, and a 16GB graphics card is basically useless.

ArthurSullivanIII510

—

Hugging Face can be directly downloaded as open source, and the weights can be used as you like. Google has opened up this wave of patterns.

星辰618

—

I’m already using Gemma 4 for code completion, and with Claude Code, I basically don’t need to open the cloud when writing code.

Keith.Davis007

—

E4B end-side actual measurement: The generation speed has been significantly improved, and the reply success rate is also higher than before. Forget about the deep thinking function, the end-side model cannot be used

Angela_Martin_66

—

The multi-language support for 140+ languages is really strong. I used to use Qwen for translation and it always felt a bit boring. Switching to Gemma 4 has a much better effect.

Teresa_Garcia_Pro436

—

1452 on the list, third in open source, 31B overturned 600B+ models, Google’s wave is really fierce

LMorales_2020

—

I just deployed 26B MoE and ran it with vLLM. The memory usage is lower than I expected.

Walter197

—

I asked how many r's there are in a strawberry, and the answer was 2... The logical reasoning of the small parameter model on the end side is still not very good.

Rita816

—

26B The MoE tool call chain is too long and can easily get out of control. There are restrictions in CLAUDE.md. Only one tool is allowed to be called at a time.

CarterWhite

—

After running it locally, the cost is almost zero, and the monthly fee of 100 yuan for Claude Pro is no longer affordable.

Pamela.Myers520

—

The 256K context is so cool in the RAG scene. In the past, 128K was often not enough, but now the entire book is fed directly into it.

流光682

—

Codeforces ELO 2150, programming competition tasks directly surpass Qwen 3.5, this must be praised

Lawrence.GarciaZ77

—

Comparing Qwen 3.5: Qwen is still better in Chinese tasks, but Gemma 4 is obviously more stable in multi-language scenarios.

Gemma-4

In-depth Report

User Reviews

Similar Products