Gemma 4 vs Llama 4 vs Mistral: The Open Model Benchmark War of 2026

The open-weight AI landscape of 2026 is the most competitive in history. Google's Gemma 4, Meta's Llama 4, and Mistral's latest releases are all capable enough that the winner depends entirely on what you are trying to do. This is a systematic comparison across the tasks that matter most for professional deployments.

The Models Under Comparison

Gemma 4 31B Dense: Google DeepMind's flagship open model. Apache 2.0 license. Full multimodal capability including video. 256K context window.

Llama 4 Scout 17B: Meta's efficient architecture, a 17B active parameter MoE that punches significantly above its weight class. Available under Meta's community license with commercial use provisions.

Llama 4 Maverick 17B: A different MoE configuration from Meta optimized for instruction following and agentic tasks.

Mistral Medium 3: Mistral's 24B dense model, available under Apache 2.0. Strong on European language tasks and technical domains.

Coding: Gemma 4 Wins Narrowly

On HumanEval and MBPP, Gemma 4 31B leads the field with scores around 87% and 83% respectively. Llama 4 Scout follows closely at 84% and 79%. Mistral Medium 3 trails but remains respectable at 79% and 74%.

The practical difference on real coding tasks is smaller than benchmark numbers suggest. For boilerplate generation, all three models perform identically. The gap emerges on complex algorithmic problems requiring multi-step reasoning, where Gemma 4's longer context window and superior reasoning architecture provide a genuine advantage.

For local deployment where speed matters, Llama 4 Scout's smaller active parameter count generates code faster than Gemma 4 31B at equivalent hardware levels — a meaningful tradeoff for interactive coding assistance.

Reasoning: Llama 4 Maverick Surprises

On GPQA (Graduate-level Professional Questions and Answers) — the most demanding public reasoning benchmark currently available — Llama 4 Maverick outperforms expectations, reaching 68% versus Gemma 4's 71% and Mistral's 61%.

The gap is small enough that hardware speed advantages shift the practical winner. Llama 4 Maverick on an RTX 4090 generates reasoning chain tokens faster than Gemma 4 31B, which can mean faster time-to-final-answer on problems where the reasoning chain is long.

For complex analytical tasks in professional settings, the choice between Gemma 4 and Llama 4 Maverick is genuinely a coin flip. Both represent a qualitative leap over anything that was available in open weights eighteen months ago.

Math: Gemma 4 Dominates

MATH benchmark and AMC/AIME competition problems reveal the sharpest differentiation. Gemma 4 31B scores 72% on MATH 500 versus Llama 4 Scout at 64% and Mistral Medium 3 at 58%.

This gap appears to reflect Google's training advantage from proprietary math datasets accumulated through Google's educational products. For financial modeling, scientific calculation, and quantitative analysis use cases, Gemma 4 is the clear recommendation.

Instruction Following: All Three Are Excellent

On instruction following benchmarks (IFEval), the three models are within 3 percentage points of each other. All three handle complex, multi-constraint instructions — format requirements, length limits, persona maintenance — with high reliability.

This convergence reflects maturation in RLHF and instruction tuning methodology across the major open-weight labs. The days when open models struggled with structured output and complex instructions are over.

Multimodal: Gemma 4 Is Unmatched (Among Open Models)

The multimodal comparison is not close. Gemma 4 is the only model in this comparison that handles video, audio, and images natively. Llama 4 has vision capability but limited video understanding. Mistral Medium 3 does not currently support multimodal inputs in its locally deployable form.

For document processing, image analysis, and video comprehension workflows, Gemma 4 is the only viable open-weight choice. This capability gap is significant and unlikely to close quickly — Google's multimodal training infrastructure represents years of investment that competitors are only beginning to replicate.

Enterprise Deployment Recommendation

The right model depends on your primary use case:

The open-weight model ecosystem of 2026 provides genuine enterprise-grade capability across all these dimensions. The question is no longer whether open models are good enough — it is which open model is optimally suited to your specific requirements.