---
title: "Qwen3 vs Llama4 Comparison 2026: Performance, Pricing, and Use Cases"
description: "Detailed Qwen3 vs Llama4 comparison covering benchmarks, pricing, multilingual support, and deployment options for developers in 2026."
keywords: "Qwen3 vs Llama4, Qwen3 comparison, Llama4 comparison, open source LLM, Alibaba vs Meta AI"
date: "2026-05-07"
slug: "qwen3-vs-llama4"
---

# Qwen3 vs Llama4 Comparison 2026

Qwen3 and Llama4 are the two most important open-source LLM releases of 2026. Both use Mixture-of-Experts (MoE) architectures, but they target different strengths. This comparison covers benchmarks, pricing, deployment, and real-world use cases.

## Model Overview

| Feature | Qwen3 235B (MoE) | Llama4 Maverick 400B (MoE) | Llama4 Scout 109B (MoE) |
|---------|-------------------|---------------------------|------------------------|
| Total Parameters | 235B | 400B | 109B |
| Active Parameters | ~22B | ~17B | ~17B |
| Architecture | MoE | MoE | MoE |
| Context Window | 128K | 128K (1M with Scout) | 1M |
| License | Apache 2.0 | Llama License | Llama License |
| Multilingual | Excellent (119 languages) | Good (200+ languages) | Good (200+ languages) |
| Open Weights | Yes | Yes | Yes |
| Release Date | April 2026 | April 2026 | April 2026 |

## Benchmark Comparison

| Benchmark | Qwen3 235B | Llama4 Maverick | Llama4 Scout | DeepSeek V3 | GPT-4o |
|-----------|-----------|-----------------|-------------|-------------|--------|
| MMLU | 82.6% | 80.5% | 76.4% | 88.5% | 88.7% |
| MMLU-Pro | 72.5% | 70.1% | 65.8% | 78.3% | 77.6% |
| HumanEval | 85.7% | 86.4% | 82.1% | 89.2% | 90.2% |
| MATH | 73.8% | 70.5% | 65.2% | 78.3% | 76.6% |
| GPQA | 52.3% | 51.1% | 46.8% | 59.1% | 53.6% |
| MultiPL-E | 78.4% | 76.9% | 72.3% | 82.1% | 83.5% |

## API Pricing Comparison

| Provider | Model | Input Price | Output Price |
|----------|-------|-------------|--------------|
| Alibaba Cloud | Qwen3 235B | ¥4/M tokens | ¥12/M tokens |
| Alibaba Cloud | Qwen-Plus | ¥0.8/M tokens | ¥2/M tokens |
| Together AI | Llama4 Maverick | ~$0.28/M tokens | ~$0.28/M tokens |
| Groq | Llama4 Maverick | ~$0.30/M tokens | ~$0.30/M tokens |
| SiliconFlow | Qwen 2.5 72B | ¥1.4/M tokens | ¥1.4/M tokens |

In USD terms, Qwen3 via Alibaba Cloud costs approximately $0.56/M input tokens, making Llama4 Maverick via Together AI or Groq the cheaper option for API access.

## Key Differences

### Multilingual Performance

Qwen3 was built with multilingual capabilities as a core priority:

- **Qwen3**: Trained on data spanning 119 languages with particular strength in Chinese, Japanese, Korean, and Southeast Asian languages
- **Llama4**: Supports 200+ languages but with variable quality; strongest in English and European languages

For Chinese language tasks, Qwen3 is the clear winner. For primarily English workloads, Llama4 has a slight edge in some benchmarks.

### Reasoning Capabilities

Qwen3 includes a "thinking mode" that enables chain-of-thought reasoning similar to DeepSeek R1:

- **Qwen3 thinking mode**: Explicit reasoning traces for complex problems
- **Qwen3 non-thinking mode**: Fast, direct responses for simple queries
- **Llama4**: Standard autoregressive generation without explicit thinking

This dual-mode approach in Qwen3 gives it flexibility — use thinking mode for hard problems and standard mode for speed.

### Long Context

Llama4 Scout's 1M token context window is a major differentiator:

- **Llama4 Scout**: 1M tokens — process entire codebases, long documents
- **Qwen3**: 128K tokens — sufficient for most use cases
- **Llama4 Maverick**: 128K tokens

If you regularly process documents over 128K tokens, Llama4 Scout is your best open-source option.

### Deployment Requirements

| Requirement | Qwen3 235B | Llama4 Maverick | Llama4 Scout |
|-------------|-----------|-----------------|-------------|
| Min GPUs (H100) | 2 | 2 | 4 |
| Min VRAM | ~160GB | ~160GB | ~320GB |
| Quantized (GPTQ 4-bit) | 1x A100 | 1x A100 | 2x A100 |
| Consumer GPU viable | Q3 22B variant only | Q3 17B variant only | No |

## When to Choose Qwen3

- Chinese or multilingual tasks are primary
- You need the thinking mode for reasoning tasks
- You're in the Alibaba Cloud ecosystem
- You want Apache 2.0 licensing (more permissive than Llama License)
- You prefer Alibaba's API pricing for your region

## When to Choose Llama4

- You need 1M token context (Scout variant)
- Primarily English-language workloads
- You want the cheapest API access (Together/Groq)
- You're already in the Meta/Llama ecosystem
- Coding is your primary use case (slight HumanEval edge)

## Cost Comparison: Real Workloads

### Processing 5M input + 1M output tokens daily

| Model/Route | Daily Cost | Monthly Cost |
|------------|-----------|-------------|
| Qwen3 via Alibaba Cloud | ~$5.56 | ~$167 |
| Qwen3 via SiliconFlow | ~$2.50 | ~$75 |
| Llama4 via Together AI | ~$1.68 | ~$50 |
| Llama4 via Groq | ~$1.80 | ~$54 |
| Self-hosted Qwen3 | GPU cost only | ~$500-1000 (GPU rental) |
| Self-hosted Llama4 | GPU cost only | ~$500-1000 (GPU rental) |

## Conclusion

Both Qwen3 and Llama4 are excellent open-source models that push the frontier forward. Qwen3 wins on multilingual performance and reasoning flexibility (thinking mode), while Llama4 wins on long context (1M Scout), slightly cheaper API pricing, and coding performance.

For most developers, the choice comes down to: **Chinese/multilingual = Qwen3, English/long-context = Llama4**.