Llama 4 vs Qwen 3 vs DeepSeek V3 vs Mistral Large: Open-Weight Models 2026

By Tyler Casey · AI-assisted research & drafting · Human editorial oversight
@getboski

DeepSeek V3 was trained for $6 million. Llama 4 Maverick activates just 17 billion of its 400 billion parameters per request. Qwen 3's flagship model matches or beats GPT-4o on math reasoning while costing a fraction per token. And Mistral Large 2, built by a European team of 800 people, holds its own against models from organizations 100x its size.

Open-weight models aren't catching up to proprietary ones anymore. On several benchmarks, they've already passed them. See our coverage of the frontier model wars for the broader competitive picture. The question isn't whether to use open-weight models in 2026. It's which one fits your actual workload. This guide compares the four leading families on the numbers that matter: performance, cost, licensing, fine-tuning support, and real-world fit.

At a Glance

*Qwen3 MMLU exact score varies by evaluation method; reported range 81–86. †Mistral Large 2 ranks second to GPT-4o on MATH per Mistral's benchmarks. ‡Pricing based on Mistral Large 3; Large 2 pricing may differ by provider.

Llama 4 Maverick: Meta's MoE Flagship

Meta released the Llama 4 family on April 5, 2025, and the architecture marks a sharp break from Llama 3. Both Scout and Maverick use Mixture-of-Experts (see our transformer architecture explainer for the foundations), meaning only a fraction of the model's parameters activate per request. Maverick packs 400 billion total parameters but routes through just 17 billion per forward pass across 128 experts. Scout uses the same 17B active parameter count with 16 experts and 109B total.

The headline number is Maverick's 1 million token context window. Scout pushes even further to 10 million tokens, though practical use at that length requires careful chunking. For retrieval-heavy applications, legal document analysis, or codebase-scale context, these windows are genuinely useful rather than just marketing copy.

On benchmarks, Maverick scores 85.5 on MMLU and 61.2 on MATH. It beats GPT-4o and Gemini 2.0 Flash across multiple evaluations while using less than half the active parameters of DeepSeek V3. The model earned an ELO of 1417 on LMSYS Chatbot Arena, placing it second globally at the time, though that ranking came with controversy about Meta's evaluation methodology.

Maverick is natively multimodal. It processes images and text in a single forward pass rather than bolting vision onto a text-only backbone. For teams building applications that mix document understanding with conversation, this matters.

The Llama 4 Community License allows commercial use, which puts it ahead of many proprietary alternatives. But it's not Apache 2.0 or MIT. Meta retains certain restrictions, particularly around training competing models and use by organizations above 700 million monthly active users. For most companies, the license is fine. For those building foundation models, read the fine print.

Inference costs are competitive. OpenRouter lists Maverick at $0.15/M input tokens and $0.60/M output, with Scout even cheaper at $0.08/$0.30. Self-hosting requires serious hardware: Maverick needs roughly 400GB of VRAM for full precision, though quantized versions run on smaller clusters.

The catch: Maverick's MoE architecture alternates dense and expert layers rather than using experts in every block, which complicates fine-tuning compared to models like DeepSeek V3. If your use case requires heavy customization, the tooling is less mature than for dense models.

Qwen3-235B-A22B: Alibaba's Reasoning Powerhouse

Alibaba's Qwen team has been shipping at a pace that's hard to ignore. For context on Qwen's trajectory, see Qwen and the open-source revolution.) The Qwen3 series spans from 0.6B to 235B parameters, covering everything from edge devices to datacenter-scale deployment. The flagship, Qwen3-235B-A22B, is a MoE model with 235 billion total parameters and 22 billion active per token.

Where Qwen3 stands apart is reasoning. The model integrates thinking mode and non-thinking mode into a single framework with a thinking budget mechanism. In thinking mode, it performs multi-step chain-of-thought reasoning for complex problems. In non-thinking mode, it responds quickly for straightforward queries. You can control the compute allocation at inference time, which means the same model handles both quick Q&A and deep mathematical proofs.

The math benchmarks tell the story. Qwen3-235B scores 85.7 on AIME '24 and 81.5 on AIME '25, competitive with DeepSeek-R1 and OpenAI's o1. On coding, it hits 70.7 on LiveCodeBench v5 and 2,056 on CodeForces. These aren't cherry-picked numbers. The technical report shows consistent performance across math, coding, and general knowledge benchmarks.

The model family's breadth is a practical advantage. Need something that runs on a laptop? Qwen3-4B works. Need a coding specialist? Qwen3-Coder outperformed DeepSeek V3.2 on coding tasks despite using far fewer active parameters. Need the full flagship? The 235B model is available through Alibaba Cloud and third-party providers.

Licensing is Apache 2.0, the most permissive option among these four models. No restrictions on competing model training, no MAU caps, no strings attached. For organizations that need maximum legal clarity, this matters.

API pricing through providers like Nebius runs $0.20/M input and $0.60–0.80/M output. That's more expensive than DeepSeek but cheaper than Mistral. Fine-tuning support is strong. LoRA and QLoRA work well, and distilled versions of Qwen3 achieve impressive results when trained on reasoning traces from larger models.

The catch: Qwen3's 131K context window is the smallest of the four flagship models compared here. If your application needs million-token context, Llama 4 is the better fit. And while Alibaba has been responsive about open-sourcing weights, the model's training data composition is less transparent than Meta's disclosures.

DeepSeek V3: The Cost Efficiency King

DeepSeek V3 rewrote the economics of large language models. For background on the DeepSeek family, see our DeepSeek explainer.) The team trained a 671 billion parameter model for approximately $6 million on NVIDIA H800 GPUs, a figure that made the rest of the industry reconsider their budgets. The model uses 256 MoE experts with Multi-Head Latent Attention (MLA), activating just 37 billion parameters per request.

On raw benchmarks, DeepSeek V3 leads in general knowledge. It scores 88.5 on MMLU, 75.9 on MMLU-Pro, and 59.1 on GPQA, outperforming all other open-source models at the time of release. On coding, it hits 82.6 on HumanEval. On math, 61.6 on MATH and 89.3 on GSM8K.

But the real story is inference cost. DeepSeek's API charges $0.14/M input tokens and $0.28/M output, roughly 90% cheaper than comparable OpenAI and Anthropic endpoints. The V3.2 update unified pricing further at $0.028/M for cache hits and $0.28/M for misses. For high-volume applications, the savings compound fast. Self-hosting breaks even at around 15–40 million tokens per month compared to proprietary APIs.

The companion model, DeepSeek-R1, shares V3's 671B architecture but adds explicit reasoning capabilities. It's a separate model, not a mode toggle like Qwen3's thinking mechanism, but it gives the DeepSeek family coverage across both fast inference and deep reasoning workloads.

DeepSeek's MIT license is the most permissive here. No usage restrictions whatsoever. You can train competing models, deploy at any scale, and modify the weights however you want. Combined with published training details, it's the most open option available.

The 128K context window handles most production workloads. It's not Llama 4's million tokens, but for document summarization, code generation, and conversation, 128K is rarely the bottleneck. Pre-training on 14.8 trillion tokens gives V3 broad coverage across domains.

And there's more coming. DeepSeek V4 is expected in April 2026 with multimodal support, a 1M+ token window, and what the team calls "Engram conditional memory." If V3's trajectory is any guide, V4 will push the cost-performance frontier further.

The catch: Fine-tuning DeepSeek V3 is harder than it should be. The 256-expert MoE architecture creates challenges with LoRA, particularly around precision mismatches when merging weights trained in bfloat16 and served in fp8. QAT fine-tuning helps, but the tooling isn't as plug-and-play as dense models. And while DeepSeek publishes weights and training details, the team's communication style is sparse. Don't expect blog posts walking you through deployment like you'd get from Meta or Mistral.

Mistral Large 2: Europe's Dense Contender

Mistral Large 2 takes the opposite architectural bet. While everyone else went MoE, Mistral built a 123 billion parameter dense transformer where every parameter is active on every forward pass. No expert routing, no sparse activation. Just a single, large, fully utilized model.

That density pays off in consistency. On HumanEval, Mistral Large 2 scores 92.0, the highest of these four models by a meaningful margin. On MMLU, it hits 84.0. The multilingual MMLU performance is remarkably even: 82.8 for French, 81.6 for German, 82.7 for Spanish, 82.7 for Italian. For organizations serving global audiences, that consistency across languages is more valuable than a few points on an English-only benchmark.

Mistral Large 2 supports 80+ programming languages and dozens of natural languages. The code generation quality is strong across the board, not just in Python and JavaScript where most models are optimized. If your team writes Rust, Go, or TypeScript, Mistral's multi-language coverage matters.

The model ships under Apache 2.0, giving you the same licensing freedom as Qwen3. Mistral has positioned itself as Europe's answer to the US-China AI duopoly, and the permissive licensing is part of that strategy. For European organizations with data sovereignty requirements, a French-made model under Apache 2.0 is a simpler compliance story than models from Meta, Alibaba, or a Chinese research lab.

Mistral has already moved ahead with Mistral Large 3, a much larger model. Large 2 remains relevant because its 123B size is practical for single-node deployment. You don't need a multi-GPU cluster. You don't need expert routing infrastructure. A single high-end node with enough memory runs the full model. For teams that value deployment simplicity over maximum benchmark scores, that's a real advantage.

API pricing through Mistral's platform and third-party providers tends to run higher than DeepSeek or Llama 4, reflecting the compute cost of a dense model. Expect roughly $0.50/M input and $1.50/M output through most providers, though Mistral's own endpoint pricing varies by tier.

The catch: Dense architecture means higher inference cost per token compared to MoE models with similar benchmark scores. Mistral Large 2's 123B parameters all activate on every request, while Llama 4 Maverick achieves similar MMLU scores with just 17B active. At scale, that cost difference adds up. And while 128K context is adequate, it doesn't match Llama 4's million-token window for applications that need extreme context length.

When to Choose What

Building a cost-sensitive production system: DeepSeek V3. The API pricing is 3-5x cheaper than alternatives at comparable quality. MIT licensing means no legal review needed. If V4 drops in April as expected, you'll get an upgrade path without switching providers.

Math-heavy or reasoning-intensive applications: Qwen3-235B with thinking mode. The AIME scores speak for themselves, and the ability to toggle between fast and deep reasoning at inference time gives you flexibility that fixed-mode models can't match.

Long-context applications (RAG, code analysis, legal docs): Llama 4 Maverick or Scout. A million-token window with Scout's 10M option means you can fit entire codebases or document collections in context. No other open-weight model comes close on context length.

Multilingual production deployments: Mistral Large 2 for consistent quality across European and Asian languages. Qwen3 if your primary non-English language is Chinese, Japanese, or Korean.

Code generation across multiple languages: Mistral Large 2. The 92.0 HumanEval score and support for 80+ programming languages make it the strongest pure coding choice among these four.

Startups with limited GPU budget: Qwen3's smaller variants (4B, 8B, 14B) offer strong performance that runs on consumer hardware. DeepSeek's distilled models are another option. Don't default to the flagship when a smaller model handles your use case.

Enterprises needing maximum licensing clarity: DeepSeek (MIT) or Qwen3/Mistral (Apache 2.0). Llama 4's community license has restrictions that may matter depending on your scale and use case.

What the Benchmarks Miss

Every model in this comparison has been heavily optimized for the benchmarks listed above. That's not a secret, and it's not necessarily a problem. But it means the numbers don't tell the full story.

MMLU measures breadth of knowledge. It doesn't measure whether a model can follow complex, multi-turn instructions in production. HumanEval tests whether a model can write short Python functions from docstrings. It doesn't test whether it can debug a 5,000-line codebase or refactor a module without breaking upstream dependencies. MATH benchmarks test formal mathematical reasoning on clean, well-specified problems. They don't test whether a model handles ambiguous, real-world data analysis where the question itself needs clarification.

The LMSYS Chatbot Arena controversy with Llama 4 Maverick illustrates the problem. Meta submitted an "experimental" variant optimized for human preference rather than the standard model. The ELO score looked great. The community pushed back. LMSYS updated its policies. The standard Maverick is a good model, but the leaderboard number was inflated by a version that most users wouldn't deploy.

Similarly, DeepSeek V3's $6 million training cost is real but misleading in isolation. That figure covers the final training run, not the research, failed experiments, and infrastructure development that preceded it. The model is genuinely efficient, but the "trained for $6 million" framing understates the total investment.

What matters more than any benchmark is how these models perform on your specific data, in your production environment, with your latency and cost constraints. Run evaluations on your own tasks. If you're choosing between Qwen3 and DeepSeek V3 for a math tutoring app, test them on your actual question bank, not on AIME problems. If you're picking between Llama 4 and Mistral for a multilingual chatbot, test them with your users' actual languages and topics.

The open-weight advantage is that you can run these evaluations for free, on your own hardware, without API rate limits. Use it. For more on how to build a meaningful evaluation pipeline, see our guide on how to evaluate AI models.

FAQ

Is DeepSeek V3 safe to use given it's developed in China?
DeepSeek V3's weights are MIT-licensed and publicly available. You can inspect, modify, and self-host them. The model doesn't phone home when you run it locally. That said, some organizations have policies restricting use of Chinese-developed AI, and certain government contracts may prohibit it. The technical risk is low when self-hosting. The compliance risk depends on your regulatory environment.

Can I fine-tune these models on my own data?
Yes, all four support fine-tuning through LoRA or similar parameter-efficient methods. Qwen3 and Mistral Large 2 have the most mature fine-tuning tooling, with well-documented workflows for LoRA and QLoRA. Llama 4 and DeepSeek V3 are fine-tunable but their MoE architectures add complexity around expert routing and precision. Budget 2-8x the base model's VRAM for full fine-tuning, or 10-20% for LoRA.

Which model should I use for a RAG pipeline?
For retrieval-augmented generation, context window and instruction-following matter more than raw benchmark scores. Llama 4 Maverick's 1M token window lets you stuff more retrieved context in a single prompt, reducing the need for aggressive chunking. DeepSeek V3 is the cost-efficient choice at 128K context. For a deeper look at building retrieval systems, see our guide on building RAG systems that work.

What about DeepSeek V4?
As of March 2026, DeepSeek V4 hasn't launched yet. Reports point to an April 2026 release with multimodal capabilities, a 1M+ token context window, and improved coding performance. If you're starting a project today, build on V3. The architecture is stable, the tooling is mature, and V4 will likely offer a straightforward upgrade path.

Sources

• Llama 4 Announcement, Meta AI
• Llama 4 Benchmarks, Llama.com
• Llama 4 Maverick, Analytics Vidhya
• Llama 4 Maverick Pricing, OpenRouter
• Llama 4 vLLM Deployment Guide, Prem AI
• Qwen3 Blog Post, Qwen Team
• Qwen3 Technical Report, arXiv
• Qwen3 GitHub Repository
• DeepSeek-V3 Technical Report, arXiv
• DeepSeek-V3, Hugging Face
• DeepSeek API Pricing
• DeepSeek-R1, Hugging Face
• DeepSeek V3 Benchmarks, llm-stats.com
• DeepSeek V4 April 2026 Report, Dataconomy
• Mistral Large 2 Announcement, Mistral AI
• Mistral Large 2 Overview, DataCamp
• Mistral Pricing
• Fine-Tuning DeepSeek Models, Fireworks AI
• LLM Architecture Comparison, Sebastian Raschka
• LMSYS Chatbot Arena, OpenLM
• Self-Hosting Cost Analysis, Revolution in AI
• Nebius Token Factory Pricing
• DeepSeek V3 vs Qwen3 Benchmarks, Spectrum AI Lab