Which Open-Source LLM Models Are Currently the Best?

GMI Cloud Blog | AI Infrastructure Guide | gmicloud.ai

The best open-source LLMs currently are Llama 3 405B (the most capable general-purpose open-weight model), DeepSeek-R1 (the strongest open-source reasoning model), Mistral Large (strong European alternative with multilingual capabilities), Qwen 2.5 72B (leading Chinese open-source model), and Code Llama 70B / DeepSeek Coder (top open-source code models).

Open-source models have closed much of the gap with proprietary models like GPT-4o and Claude, while offering full control over deployment, fine-tuning, and data privacy. This guide ranks the current leaders, explains what each excels at, and covers what you need to deploy them.

For GPU infrastructure to self-host open-source LLMs, providers like GMI Cloud offer H100/H200 instances alongside a 100+ model library.

Here's what each model brings to the table.

1. Llama 3 (Meta)

The most widely adopted open-source LLM family and the default starting point for most teams evaluating open-source options.

Available sizes: 8B, 70B, and 405B parameters. The 405B is the most capable, approaching closed-source model quality on most benchmarks (MMLU ~86%). The 70B is the production sweet spot for teams that need strong performance without multi-GPU setups.

Strengths: Broadest community ecosystem (fine-tuned variants, tutorials, tooling). Permissive license allows commercial use. Strong across general reasoning, writing, and instruction following.

Best for: Teams that want a well-supported, general-purpose foundation model with extensive community resources and commercial licensing.

The 8B version is excellent for learning and experimentation. The 70B handles most production tasks. The 405B is for teams that need maximum open-source capability and have the GPU infrastructure to support it.

2. DeepSeek-R1

The open-source reasoning breakthrough. DeepSeek-R1 uses inference-time compute (chain-of-thought reasoning) to achieve scores that rival or exceed GPT-4o on mathematical and logical reasoning benchmarks.

Benchmark performance: MATH ~97%, competitive with OpenAI's o3. This is remarkable for an open-weight model.

Strengths: Strongest open-source reasoning capability. Demonstrates that architectural innovation can close the gap with much larger proprietary models. Open-weight release enables the research community to study and build on reasoning techniques.

Best for: Research teams studying reasoning, applications requiring strong mathematical or logical problem-solving, and teams that need reasoning capabilities without depending on closed-source API providers.

DeepSeek-R1 trades speed for accuracy. It's slower than standard chat models but significantly more accurate on complex problems.

3. Mistral (Mistral AI)

The leading European open-source LLM provider. Mistral offers models from 7B to Large (123B), plus Mixtral, a Mixture-of-Experts architecture that delivers strong performance with efficient compute.

Strengths: Strong multilingual capabilities (European languages in particular). Mixtral 8x22B uses MoE to activate only a subset of parameters per token, reducing inference cost while maintaining quality. Permissive licensing.

Best for: Multilingual applications, European data sovereignty requirements, and teams that want an efficient architecture (MoE) for cost-effective deployment.

Mixtral's MoE approach means you get large-model quality at medium-model inference cost. This makes it particularly attractive for production deployments where cost per request matters.

4. Qwen 2.5 (Alibaba)

The leading Chinese open-source LLM family. Qwen 2.5 72B delivers strong performance across both Chinese and English language tasks.

Strengths: Best-in-class Chinese language capability among open-source models. Competitive English performance. Available in multiple sizes (7B to 72B). Apache 2.0 license for most variants.

Best for: Applications requiring strong Chinese language support, bilingual Chinese-English use cases, and teams operating in the Chinese AI ecosystem.

Qwen represents the growing competitiveness of non-US open-source models. For Chinese-language applications, it's the clear open-source leader.

5. Open-Source Code Models

Code-specialized open-source models have reached near-commercial quality for code generation and debugging.

Code Llama 70B (Meta) supports Python, JavaScript, and dozens of other languages. HumanEval pass@1 around 70-75%. Commercial license. Widely integrated into open-source coding tools.

DeepSeek Coder achieves HumanEval 80%+, making it the highest-performing open-source code model. Strong on code generation, completion, and explanation.

StarCoder (BigCode) is trained exclusively on permissively licensed code, addressing intellectual property concerns that other code models face. Lower benchmark scores but cleaner legal standing.

Best for: Teams building coding assistants, automated code review tools, or code-generation features who need full control over the model.

These five represent the current open-source frontier. But how do they compare to closed-source alternatives?

Open-Source vs. Closed-Source: Where Each Wins

Factor (Open-Source / Closed-Source)

• Peak capability - Open-Source: Strong but ~5-10% below frontier - Closed-Source: Highest (GPT-4o, Claude 3.5 Opus)
• Data privacy - Open-Source: Full control (runs on your infrastructure) - Closed-Source: Data sent to provider
• Fine-tuning - Open-Source: Full flexibility - Closed-Source: Limited or unavailable
• Cost at scale - Open-Source: GPU cost only (no per-request fees) - Closed-Source: Per-request pricing adds up
• Cost at low volume - Open-Source: GPU minimum cost applies - Closed-Source: Pay only for what you use
• Setup complexity - Open-Source: You manage deployment - Closed-Source: API call, zero setup
• Vendor lock-in - Open-Source: None - Closed-Source: Dependent on provider
• Community support - Open-Source: Extensive (Llama, Mistral) - Closed-Source: Provider support only

When to choose open-source: You need data privacy, want to fine-tune on proprietary data, expect high request volumes (where per-request API pricing exceeds GPU rental), or need vendor independence.

When to choose closed-source: You need frontier capability, want zero deployment overhead, have low or variable request volumes, or lack GPU infrastructure expertise.

Many teams use both: closed-source APIs for prototyping and evaluation, open-source models for production deployment once the workload stabilizes.

What You Need to Deploy Open-Source LLMs

Self-hosting requires GPU infrastructure. Model size determines hardware requirements.

Small models (7-8B): Fit on an L4 (24 GB) at INT4 quantization. Good for experimentation and lightweight applications. Inference cost is minimal.

Medium models (70B): Require an H100 (80 GB) at FP8. This is the standard production configuration. Per NVIDIA's H200 Product Brief (2024), the H200 delivers up to 1.9x inference speedup on Llama 2 70B vs. H100 (TensorRT-LLM, FP8, batch 64, 128/2048 tokens).

Large models (405B): Require multiple GPUs with NVLink for tensor-parallel inference. H200's 141 GB VRAM reduces the number of GPUs needed compared to H100.

Software stack: TensorRT-LLM for maximum throughput on NVIDIA hardware. vLLM for flexible memory management and rapid prototyping. Both support FP8 quantization and continuous batching.

Getting Started

Pick a model based on your primary need: Llama 3 for general purpose, DeepSeek-R1 for reasoning, Mistral for multilingual, Qwen for Chinese language, Code Llama or DeepSeek Coder for code. Download the weights, configure your inference engine, and benchmark on your task.

Cloud platforms like GMI Cloud offer GPU instances (H100 ~$2.10/GPU-hour, H200 ~$2.50/GPU-hour; check gmicloud.ai/pricing for current rates) with pre-configured NVIDIA stacks for open-source LLM deployment.

The model library covers image, video, and audio models that complement LLM pipelines.

FAQ

Which open-source LLM should I start with?

Llama 3 70B. It has the broadest community support, extensive documentation, and strong performance across most tasks. Start here, then evaluate specialized alternatives (DeepSeek-R1 for reasoning, Mistral for multilingual) if Llama doesn't meet your specific needs.

Can open-source LLMs match GPT-4o?

On specific benchmarks, yes. DeepSeek-R1 matches or exceeds GPT-4o on MATH. Llama 3 405B approaches GPT-4o on MMLU. On overall capability across all tasks, closed-source models still lead by roughly 5-10%.

Is "open-source" the same as "open-weight"?

No. Most models called "open-source" (Llama 3, Mistral) are actually open-weight: you can download and use the weights but training data and full training code aren't always public. True open-source means weights, code, and data are all available.

How much does it cost to run an open-source LLM?

At H100 ~$2.10/GPU-hour, running a 70B model at FP8 on a single GPU costs roughly $50/day for 24/7 operation. Compare this against API pricing at your expected request volume to determine which path is cheaper.

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What are the top open-source AI models today?

The top open-source AI models today are DeepSeek-V3, Llama 3.3, and Qwen 2.5, each dominating different categories from reasoning to coding. Finding the right model is often a trade-off between massive parameter counts and the high cost of GPU VRAM.

GMI Cloud (gmicloud.ai) solves this by providing on-demand H100 and H200 instances optimized for these specific open-weights architectures.

To see how the top contenders stack up, let's look at the raw specs.

2025 Open-Source Model Comparison

Architecture

• DeepSeek-V3/R1: Mixture-of-Experts (MoE)
• Llama 3.3 70B: Dense
• Qwen 2.5-Max: MoE
• Llama 3.1 405B: Dense

VRAM Needs

• DeepSeek-V3/R1: ~700GB+ (FP16)
• Llama 3.3 70B: ~140GB (FP16)
• Qwen 2.5-Max: ~800GB+ (FP16)
• Llama 3.1 405B: ~800GB+ (FP16)

Best GPU

• DeepSeek-V3/R1: 8x H200 (141GB)
• Llama 3.3 70B: 1-2x H100 (80GB)
• Qwen 2.5-Max: 8x H200 (141GB)
• Llama 3.1 405B: 8x H200 (141GB)

Best For

• DeepSeek-V3/R1: Reasoning & ROI
• Llama 3.3 70B: General Purpose
• Qwen 2.5-Max: Coding & Math
• Llama 3.1 405B: Distillation/Teacher

While the specs tell part of the story, each model excels in different production environments.

Llama 3.3 70B: The Reliability Champion

Llama 3.3 70B remains the industry standard for enterprise reliability and general-purpose utility. It provides the reasoning capabilities of much larger models while maintaining a footprint that fits comfortably on high-end hardware.

You'll find it handles everything from creative writing to basic structured data extraction with minimal fine-tuning.

If your workload shifts toward extreme efficiency or MoE architectures, DeepSeek is the better bet.

DeepSeek-V3 & R1: The Efficiency Kings

DeepSeek-V3 and its reasoning-specialized sibling, R1, have disrupted the market by offering state-of-the-art performance at a fraction of the compute cost. By using a Mixture-of-Experts (MoE) approach, these models only activate a small portion of their 671B parameters for each token.

This translates to high throughput and lower operational costs for massive scale.

For developers focused on specialized tasks like coding or multilingual support, Qwen-2.5 takes the lead.

Qwen 2.5: The Technical Specialist

Alibaba’s Qwen 2.5 series consistently dominates benchmarks in mathematics, programming, and technical reasoning. Its training data is heavily weighted toward logic-heavy tasks, making it the preferred choice for building automated coding assistants.

Plus, it offers excellent performance across various languages, not just English.

Choosing a model is only half the battle; you also need to ensure it fits your VRAM budget.

Hardware Logic: Why H200 is the Game-Changer

The transition from 80GB H100s to 141GB H200s is critical for running these new open-source giants at full precision. Higher VRAM allows you to host larger models on a single node, significantly reducing the latency caused by multi-node communication.

You'll also see up to 1.9x faster inference on workloads like Llama 2 70B due to the H200's superior memory bandwidth.

If managing your own GPU cluster sounds like too much overhead, there's a faster way to deploy.

Inference Engine: API-Driven Multimodal Capabilities

For teams that need to go to market instantly, GMI Cloud’s Inference Engine offers 100+ pre-deployed models callable via API. This includes high-fidelity models like Kling-Image2Video-V2-Master for video generation and sora-2-pro for premium visual content.

You get the performance of optimized infrastructure without the complexity of manual GPU provisioning.

Whether you choose raw GPUs or APIs, the underlying infrastructure determines your uptime.

GMI Cloud: Built for Professional AI

GMI Cloud (gmicloud.ai) is an inaugural NVIDIA Reference Platform Cloud Partner, providing on-demand H100 and H200 SXM instances. Our nodes feature 8 GPUs with 900 GB/s NVLink bandwidth and 3.2 Tbps InfiniBand for seamless scaling.

You can deploy your chosen open-source model on a pre-configured stack including CUDA 12.x and TensorRT-LLM in minutes.

Let's wrap up with common questions about scaling these models in production.

FAQ

How do I calculate if a model fits my GPU? A rough formula is: VRAM needed ≈ (Parameters in billions × bytes per element) + KV-Cache. For example, a 70B model in FP16 (2 bytes) needs 140GB just for weights, making the H200's 141GB capacity essential.

Is quantization worth the quality loss? Quantizing to 4-bit (INT4) can reduce VRAM needs by 75%, allowing larger models to fit on cheaper hardware. However, for high-reasoning tasks like coding, we recommend staying at 8-bit or higher to maintain accuracy.

What's the best GPU for DeepSeek-R1? Because DeepSeek-R1 is a 671B parameter model, you ideally need an 8x H200 node to handle the memory requirements efficiently. Check gmicloud.ai/pricing for the most current rates on high-memory instances.

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