Gemma 4 vs Mistral Large 3: Open LLM Comparison (2026)

Gemma 4 is Google's open model family, released in April 2026 in sizes from E2B up to a 31B dense model. Mistral Large 3, released in December 2025, is a 675-billion-parameter Mixture-of-Experts model with 41 billion active parameters. Both accept text and image inputs and are released under the Apache 2.0 license.

Key facts

Updated 2026-09-24: added Key facts, question headings with direct answers, and an FAQ built from the questions Google shows for this topic.

What are Gemma 4 and Mistral Large 3?

Gemma 4 is Google DeepMind's open-weights model family, with sizes from E2B and E4B for phones up to 26B A4B (MoE) and 31B (dense) for single GPUs and workstations. Mistral Large 3 ↗ is Mistral AI's 675B-parameter MoE model with 41B active parameters, built for large-scale reasoning, coding and multilingual work.

Google says Gemma 4 is built from the same research and technology as Gemini 3. Mistral Large 3 is Mistral's first mixture-of-experts model since the Mixtral series. Comparing Gemma 4 and Mistral Large 3 shows two very different approaches to open models.

Gemma 4 targets accessibility on phones, laptops and single GPUs. Mistral Large 3 emphasizes scale for reasoning, coding and multilingual tasks. Their benchmarks and hardware requirements reflect that difference.

What are the core specifications of Gemma 4 and Mistral Large 3?

As of September 2026, the Gemma 4 model card ↗ lists five sizes (E2B, E4B, 12B, 26B A4B and 31B), up to 256K context, text and image input, a January 2025 knowledge cutoff and Apache 2.0 licensing. Mistral Large 3 has 675B total and 41B active parameters, a 256K context window, text and image input, and Apache 2.0 licensing.

Gemma 4: Lightweight Power for Edge Devices

Google's model card reports that Gemma 4 31B (instruction-tuned) scores 85.2% on MMLU Pro, 84.3% on GPQA Diamond and 80.0% on LiveCodeBench v6. The 26B A4B MoE model scores 82.6%, 82.3% and 77.1% on the same benchmarks.

Gemma 4 accepts text and image input across all sizes, with audio input on the E2B, E4B and 12B models. The E2B and E4B models are designed for phones and edge devices, and the larger models for laptops, single GPUs and workstations.

Gemma 4 has a knowledge cutoff of January 2025, per the model card. It is the first Gemma release under Apache 2.0; earlier Gemma versions used the Gemma Terms of Use. Gemma 4 can be fine-tuned for edge applications.

Mistral Large 3: Scalable MoE Architecture

Mistral Large 3 activates 41 billion of its 675 billion parameters per token. It supports a 256K token context window, according to its Hugging Face model card ↗. It processes text and image inputs through a 2.5B-parameter vision encoder.

Mistral says Large 3 reached #2 among open-source non-reasoning models on the LMArena leaderboard at launch. Its model card reports 67.17 on GPQA Diamond. Mistral describes it as having best-in-class multilingual conversation performance across 40+ native languages.

Mistral Large 3 uses the Apache 2.0 license, which allows commercial use. In FP8 it fits on a single node of B200 or H200 GPUs, and an NVFP4 version fits on a single node of H100s or A100s. It is available on Hugging Face and through Mistral's API.

Gemma 4 prioritizes on-device and single-GPU deployment. Mistral Large 3 focuses on server-scale capability with sparse expert activation.

How do Gemma 4 and Mistral Large 3 compare in inference speed and performance benchmarks?

Gemma 4's small and mid-size models run on hardware from phones to single GPUs, while Mistral Large 3 needs a multi-GPU server node. On vendor-reported scores, Gemma 4 31B lists 84.3% on GPQA Diamond and Mistral Large 3 lists 67.17, but the two were measured by different vendors under different setups.

Speed and Latency Tests

No independent, like-for-like speed comparison of Gemma 4 and Mistral Large 3 has been published. Speed depends on hardware, quantization, batch size and serving stack, such as vLLM or llama.cpp. Gemma 4's 26B A4B model activates only 3.8B parameters per token, which keeps per-token compute low.

Mistral Large 3 activates 41B parameters per token, far fewer than its 675B total, which reduces compute per token compared with a dense model of the same size. It still needs enough GPU memory to hold all 675B parameters. Google reports up to a 3x speedup for Gemma 4 with the multi-token prediction drafters it released in May 2026.

Both models support quantization to reduce memory use. Gemma 4 GGUF builds are available in Ollama, and Mistral publishes FP8 and NVFP4 versions of Large 3.

Reasoning and Multilingual Benchmarks

Gemma 4 31B scores 89.2% on AIME 2026 without tools and 76.9% on MMMU Pro, per Google's model card. Mistral Large 3's model card reports 67.17 on GPQA Diamond. Google reports 88.4% on MMMLU (multilingual MMLU) for Gemma 4 31B.

Gemma 4 offers out-of-the-box support for 35+ languages and was pre-trained on 140+ languages, per Google. Mistral Large 3 supports 40+ native languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic.

Neither vendor publishes a full, shared benchmark set for both models, so direct score comparisons should be treated with caution.

Gemma 4 is suited to edge deployment for mobile tasks. Mistral Large 3 targets data-center deployment.

For comparisons with frontier proprietary models, see our ChatGPT vs Claude vs Gemini comparison.

How cost-effective are Gemma 4 and Mistral Large 3 for open-source savings and deployment economics?

Both models are free to download under Apache 2.0. Self-hosting cost depends on your hardware: Gemma 4's smaller models run on consumer devices, while Mistral Large 3 needs a multi-GPU node. As of September 2026, Mistral's API charges $0.5 per million input tokens and $1.5 per million output tokens for Mistral Large.

Free vs Hosted Costs

Gemma 4 downloads for free via Hugging Face and Ollama. Mistral Large 3 is available under Apache 2.0 at no cost on Hugging Face. Self-hosting costs depend on your hardware and electricity, not license fees.

Mistral Large 3 can be self-hosted on cloud GPU nodes or used through Mistral's API and cloud partners. New Google Cloud customers receive free trial credits, which can be applied to hosting Gemma on Google Cloud. Mistral's free plan includes $10 per month in API credits, per its pricing page ↗ (as of September 2026).

Gemma 4 has no API fees when run on-device. Mistral Large costs $0.5 per million input tokens and $1.5 per million output tokens on Mistral's API (as of September 2026).

Total Ownership Cost

Gemma 4's E2B and E4B models run on phones and laptops, and the 26B A4B and 31B models on a single high-memory GPU or workstation. Mistral Large 3 needs a server node of data-center GPUs, even in FP8. Fine-tuning cost scales with model size, so Gemma 4 is far cheaper to adapt.

Mistral says batch processing cuts API prices by 50%, and cached input tokens cut input cost by up to 90%. Running either model locally avoids per-token API fees, though you pay for hardware instead.

Actionable steps for optimization: 1. Download models from Hugging Face. 2. Quantize to reduce memory requirements. 3. Use vLLM for batch inference. 4. Apply for available cloud credits from Google Cloud or Mistral's free plan.

Compare these economics to other open models in our open-source LLM comparison of Llama, DeepSeek and Qwen.

How do Gemma 4 and Mistral Large 3 stack up against competitors in the open-source LLM ecosystem?

Gemma 4 competes with small models such as Microsoft's Phi-4-mini (3.8B) for on-device use, while Mistral Large 3 competes with large MoE models such as Meta's Llama 4 Maverick and InclusionAI's Ring-1T. Parameter counts and licenses differ widely, so the right choice depends on hardware and licensing needs.

Top Open-Source Rivals

Meta's Llama 4 Scout has 17 billion active parameters, 16 experts and 109 billion total parameters. Llama 4 Scout supports a 10 million token context window, according to Meta ↗. Meta released Llama 4 in April 2025.

Microsoft's Phi-4 has 14 billion parameters under the MIT license. Phi-4 runs on modest hardware. Microsoft's Phi-4-mini has 3.8 billion parameters, small enough for on-device deployment.

Alibaba's Qwen family also offers open-weight models across many sizes and is a common multilingual choice.

Cohere's Command R+ has 104 billion parameters and is optimized for RAG. Command R+ is optimized for 10 languages and uses a non-commercial CC-BY-NC license. Mistral Small 4 is Mistral's smaller current model.

GLM-4.5-Air from Z.ai has 106 billion total parameters with 12 billion active, under the MIT license. Ring-1T from InclusionAI has 1 trillion total parameters with 50 billion active, also under MIT.

DeepSeek V4 Pro is another large model option, available through DeepSeek's API.

Gemma 4's E2B and E4B models compete with Phi-4-mini for on-device use. Mistral Large 3 competes with Llama 4 Maverick, which has 400 billion total parameters and 17 billion active.

Mistral Large 3 activates 41 billion parameters per token versus 17 billion for Llama 4 Scout and Maverick. Gemma 4's smallest models run on-device, which none of these larger rivals can do.

Proprietary Alternatives

OpenAI's GPT-5.5, Anthropic's Claude Opus 4.8 and xAI's Grok 4.3 are available only through APIs, without open weights. Check each vendor's pricing page for current token rates.

Google's Gemini models share research and technology with Gemma 4 but are available only through Google's API and apps. Perplexity's Sonar models focus on search tasks.

For coding-focused alternatives, explore our AI coding tools guide. Other ethical open-source options are covered in our guide to ChatGPT alternatives.

What recommendations exist for buyers selecting Gemma 4 vs Mistral Large 3?

Choose Gemma 4 for edge chatbots, on-device apps and single-GPU deployments, where its five sizes from E2B to 31B fit the hardware you already own. Choose Mistral Large 3 for server-scale reasoning, coding and multilingual RAG with a 256K context window, either self-hosted on a GPU node or through Mistral's API.

Gemma 4 suits quick prototyping on laptops. Mistral Large 3 scales for complex tasks in production pipelines. Use Gemma 4 E2B or E4B for mobile deployments.

Mistral Large 3 fits coding and multilingual applications that need a large model with open weights. Serve Gemma 4 with vLLM, llama.cpp or Ollama. Deploy Mistral Large 3 with vLLM on a GPU node, or use Mistral's API.

Mistral Large 3 has far more total parameters, while Gemma 4 is far easier to run. Try both via free Hugging Face downloads or hosted APIs.

Steps for integration: 1. Install a recent version of the Transformers library. 2. Load Gemma 4 with pipeline("text-generation"). 3. Fine-tune on custom datasets if needed. 4. Evaluate on your own held-out tasks and on public benchmarks such as GPQA.

View more comparisons in ChatGPT vs Claude vs Gemini.

FAQ

Which model performs better in math and reasoning benchmarks?

On vendor-reported numbers, Gemma 4 31B lists 84.3% on GPQA Diamond and Mistral Large 3 lists 67.17. The scores come from different vendors and setups, so test both on your own tasks.

Can these models run on edge devices like laptops or phones?

Gemma 4 is built for this: E2B and E4B target phones and edge devices, and the larger models run on laptops and single GPUs. Mistral Large 3 needs a multi-GPU server node and is not suited to edge hardware.

What are the multilingual capabilities of Gemma 4 vs Mistral Large 3?

Both offer broad multilingual support. Gemma 4 has out-of-the-box support for 35+ languages and pre-training on 140+, while Mistral Large 3 supports 40+ native languages.

Are Gemma 4 and Mistral Large 3 free for commercial use?

Yes, both are released under Apache 2.0, which allows free download, fine-tuning, and commercial deployment without API fees.

How do inference speeds compare for edge deployment?

Gemma 4's small and MoE models are far faster on low-resource devices because of their size and low active parameter counts. Mistral Large 3 is a data-center model; its MoE design lowers compute per token but not its memory footprint.

What is the context window size for long-document processing?

Mistral Large 3 supports a 256K context window. Gemma 4 supports 128K on E2B and E4B and 256K on its larger models.

What is the largest Gemma 4 model?

The largest Gemma 4 model is the 31B dense model. Google's model card lists five sizes: E2B, E4B, 12B, 26B A4B and 31B. The 26B A4B model has more total parameters than the 12B but activates only 3.8B per token, so the 31B dense model remains the largest by both total and active parameters.

What Gemma 4 model should I use?

Use E2B or E4B on phones and edge devices, where they also accept audio input. Use 12B, 26B A4B or 31B on a consumer GPU or workstation, per Google's model card. Pick 26B A4B when you want near-31B benchmark scores with only 3.8B active parameters per token, and 31B for the highest vendor-reported scores.

What GPU is best for running Gemma 4?

Google's model card does not name a specific GPU. It places the 12B, 26B A4B and 31B models on consumer GPUs and workstations and the E2B and E4B models on phones and edge devices. Memory is the constraint, so quantized GGUF builds through Ollama or llama.cpp let the larger models fit on smaller cards.

Which Mistral model is the best in 2026?

Mistral describes Mistral Large 3, released in December 2025, as its most capable model to date, with 675B total and 41B active parameters under Apache 2.0. Mistral Small 4 is its smaller current model. For a single workstation, Small 4 is the practical choice; Large 3 needs a multi-GPU server node even in FP8.

Sources

• Gemma 4 model card, Google AI for Developers ↗

• Gemma 4: Byte for byte, the most capable open models, Google ↗

• Introducing Mistral 3, Mistral AI ↗

• Mistral-Large-3-675B-Instruct-2512 model card, Hugging Face ↗

• Mistral pricing, Mistral AI ↗

• The Llama 4 herd, Meta AI ↗

• Ring-1T model card, Hugging Face ↗

• Command R+ model card, Hugging Face ↗