# Best Open Source LLMs 2026: Llama vs DeepSeek vs Qwen

> A comparison of open-weight LLMs from Meta, DeepSeek, Alibaba, Mistral, Google and Microsoft, covering parameters, context length, licenses, vendor-reported benchmarks and fine-tuning options. It also notes the latest releases as of September 2026.

## What Defines Open-Source LLMs in 2026?

**Open-weight LLMs let developers download, fine-tune and self-host models without vendor lock-in. As of September 2026, current releases include Meta's Llama 4 (April 2025), DeepSeek-V4-Pro (August 2026, MIT license), Alibaba's Qwen3.8 family (August 2026) and Google's Gemma 4 (April 2026). The detailed comparison below covers the widely used 2023-2024 generation (Llama 3, Mixtral, Gemma 2, Phi-3, DBRX, Qwen2, DeepSeek-V2 and others), with figures taken from each vendor's release materials.**

Meta released Llama 3 in 8B and 70B sizes on April 18, 2024, under the Meta Llama 3 Community License, with an 8K context window; [Llama 3.1](https://ai.meta.com/blog/meta-llama-3-1/) (July 2024) extended context to 128K and added a 405B model. Mistral AI released Mixtral 8x7B, a sparse mixture-of-experts model, in December 2023. Google DeepMind released Gemma 2 in 9B and 27B sizes in June 2024, followed by a 2B model. Microsoft released Phi-3 Mini with 3.8B parameters, trained heavily on filtered and synthetic data. Databricks released DBRX with 132B total and 36B active parameters. Alibaba Cloud released Qwen2 in sizes from 0.5B to 72B in June 2024. DeepSeek released DeepSeek-V2 with 236B total and 21B active parameters in May 2024. Technology Innovation Institute trained Falcon 180B on 3.5T tokens. Meta released Code Llama 70B for code generation in January 2024. 01.AI released Yi-1.5 with up to 34B parameters in May 2024. BigScience developed BLOOM as a 176B-parameter multilingual model under the BigScience RAIL license.

Open-weight LLMs avoid many of the restrictions of closed models like ChatGPT or Claude, although most still come with license terms. Developers download weights from Hugging Face repositories. Customization occurs through tools like LoRA adapters. Ethical development relies on safety tuning, documentation and bias audits. Smaller models, such as Phi-3 and Gemma 4's edge models, have made on-device use practical.

## What Criteria Evaluate the Best Open-Source LLMs?

**Criteria for choosing an open-source LLM include fine-tuning ease with LoRA support, performance on public benchmarks, context length, hardware and hosting costs, and license terms. Licenses vary widely: some models use Apache 2.0 or MIT, while others use custom community or responsible-AI licenses.**

### Fine-Tuning Capabilities

Fine-tuning adapts base models to specific tasks using parameter-efficient methods. LoRA and QLoRA cut memory needs enough that 7B-8B models such as Llama 3 8B can be fine-tuned on a single consumer GPU. Adapter support integrates with the Hugging Face Transformers and PEFT libraries. Gemma 2 supports fine-tuning through Keras and Hugging Face. Phi-3 Mini is available in 4K and 128K context versions. Mixtral's MoE design keeps compute per token low, although all experts must fit in memory. Qwen2 72B supports a 128K context window. DBRX was trained with Databricks' MosaicML tools. DeepSeek-V2 was aligned with supervised fine-tuning and reinforcement learning. Falcon 180B works with standard PEFT libraries. Code Llama supports code infilling. Yi-1.5 models can be adapted with LoRA like other Llama-architecture models. BLOOM 176B requires distributed multi-GPU training.

### Performance Benchmarks

Benchmarks measure capabilities on standardized tests. Qwen2-72B scores 84.2 on MMLU and 91.0 on C-Eval, per its [Hugging Face model card](https://huggingface.co/Qwen/Qwen2-72B). Mixtral 8x22B Instruct scores 90.8% on GSM8K (maj@8), according to Mistral. Phi-3 Mini reaches 69% on MMLU with 3.8B parameters, per the [Phi-3 technical report](https://arxiv.org/abs/2404.14219). DBRX scores 70.1% on HumanEval, according to Databricks. Code Llama 34B scores 53.7% on HumanEval and 56.2% on MBPP, per Meta. BLOOM covers 46 natural languages and 13 programming languages.

### Deployment and Cost Analysis

Deployment hosts models on cloud or edge. All of these models are free to download, subject to their licenses. Hosted API prices for open models vary by provider and change often, so check current pricing pages before choosing a host. Gemma 2 27B is designed to run inference at full precision on a single A100 80GB or H100 GPU, or a single Google Cloud TPU host, according to Google. Phi-3 Mini is small enough to run on phones. Falcon 180B and BLOOM 176B need multi-GPU servers. Code Llama can be deployed through Hugging Face Inference Endpoints.

### Ethical AI Features

Ethical features mitigate risks in development. Meta applied safety fine-tuning to Llama 3 and released Llama Guard for input and output filtering. Google publishes a Responsible Generative AI Toolkit for Gemma, which includes SynthID Text watermarking tools. Microsoft's Phi-3 report describes safety post-training. DBRX is released under the Databricks Open Model License. Falcon 180B's training data draws on the curated RefinedWeb dataset. BLOOM's RAIL license restricts specific harmful uses and was developed under BigScience's ethical charter.

## How Do Top Open-Source LLMs Compare?

**Top open-source LLMs of the 2023-2024 generation differ in context length, architecture and license: Llama 3.1 offers 128K context, Mixtral uses mixture-of-experts for efficiency, Gemma 2 targets single-GPU deployment, Phi-3 targets edge devices, and Qwen2-72B scores 84.2 on MMLU. Their licenses range from Apache 2.0 and MIT to custom community licenses.**

### Meta's Llama 3: Leader in Customization and Multilingual Tasks

Meta AI developed Llama 3 with 8B and 70B parameters, pretrained on over 15T tokens. Llama 3 uses the Meta Llama 3 Community License, which permits commercial use with conditions. Llama 3 launched with an 8K context window; Llama 3.1 extended it to 128K and added support for eight languages. Llama 3 fine-tunes with LoRA on a single GPU for the 8B model. Meta released Llama 4 in April 2025, including Llama 4 Scout with a 10 million token context window.

### Mistral AI's Mixtral: Efficiency Through MoE Architecture

Mistral AI created the Mixtral 8x7B and 8x22B models. Mixtral 8x7B uses a sparse MoE design with about 47B total parameters and a 32K context window. Mixtral 8x22B has 141B total parameters, 39B active, and a 64K context window, according to [Mistral](https://mistral.ai/news/mixtral-8x22b). Both use the Apache 2.0 license. Mixtral 8x22B is fluent in English, French, Italian, German and Spanish. Mistral's current flagship open model is Mistral Large 3.

For detailed efficiency comparisons, see our [Gemma 4 vs Mistral Large 3 comparison](https://aitoolranked.com/blog/gemma-4-vs-mistral-large-ultimate-llm-comparison-open-source-efficiency-multilingual-capabilities).

### Google's Gemma: Lightweight for On-Device Deployment

Google DeepMind released Gemma 2 in 9B and 27B sizes on June 27, 2024, followed by a 2B model. Gemma 2 uses the Gemma license, which permits research and commercial use under Google's terms. Gemma 2 2B is small enough for on-device use. Gemma 2 27B runs at full precision on a single high-end GPU or TPU host. Gemma 2 fine-tunes via Keras or Hugging Face. Gemma 2 has an 8K context window. Its successor, Gemma 4, moved to the Apache 2.0 license in April 2026.

### Microsoft's Phi-3: Small Model, Big Performance

Microsoft released Phi-3 Mini with 3.8B parameters in April 2024. Phi-3 uses the MIT license. Phi-3 Mini comes in 4K and 128K context versions. Phi-3 Mini scores 69% on MMLU and 8.38 on MT-bench, per its technical report. It was trained on 3.3 trillion tokens of heavily filtered web data and synthetic data. Phi-3 Mini is small enough to run on a phone. Phi-3 fine-tunes on a single GPU without distributed setups.

### Databricks' DBRX and Alibaba's Qwen 2: Enterprise-Ready Options

Databricks released DBRX on March 27, 2024, with 132B total and 36B active parameters in a fine-grained MoE design. DBRX uses the Databricks Open Model License. DBRX scores 70.1% on HumanEval, according to [Databricks](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm). DBRX has a 32K context window and was trained on 12T tokens. Alibaba Cloud released Qwen2 with 0.5B to 72B parameters in June 2024. Most Qwen2 sizes use Apache 2.0, while Qwen2-72B uses the Tongyi Qianwen license. Qwen2-72B supports 128K context and scores 84.2 on MMLU and 91.0 on C-Eval. As of September 2026, Alibaba's latest open-weight family is Qwen3.8, with Qwen3.8-27B released under Apache 2.0.

### Emerging Contenders: DeepSeek-V2, Falcon, CodeLlama, Yi-1.5, and BLOOM

DeepSeek released DeepSeek-V2 with 236B total and 21B active parameters in May 2024. DeepSeek-V2's code is MIT-licensed and its weights use the DeepSeek Model License, which permits commercial use. DeepSeek reports that DeepSeek-V2 cuts training costs by 42.5% and the KV cache by 93.3% compared with DeepSeek 67B, per its [model card](https://huggingface.co/deepseek-ai/DeepSeek-V2). Technology Innovation Institute released Falcon 180B in September 2023. Falcon 180B was trained on 3.5T tokens under the Falcon-180B TII License and Acceptable Use Policy. Falcon 180B has a 2K context window. Meta released Code Llama 70B on January 29, 2024, supporting languages including Python, C++, Java, PHP, TypeScript, C# and Bash. 01.AI released Yi-1.5 in 6B, 9B and 34B sizes in May 2024. Yi-1.5 uses the Apache 2.0 license. BigScience created BLOOM 176B for 46 natural languages and 13 programming languages. BLOOM uses the BigScience RAIL License, which restricts specific harmful uses. As of September 2026, DeepSeek's latest open-weight release is DeepSeek-V4-Pro (August 2026) under the MIT license.

| Model | Parameters | Context Length | License | Key Benchmark Score (vendor-reported) |
| --- | --- | --- | --- | --- |
| Llama 3 | 8B/70B | 8K (128K in Llama 3.1) | Meta Llama 3 Community License | Not listed here |
| Mixtral 8x22B | 141B total (39B active) | 64K | Apache 2.0 | 90.8% GSM8K maj@8 (Instruct) |
| Gemma 2 | 2B/9B/27B | 8K | Gemma license | Not listed here |
| Phi-3 Mini | 3.8B | 4K/128K | MIT | 69% MMLU |
| DBRX | 132B total (36B active) | 32K | Databricks Open Model License | 70.1% HumanEval |
| Qwen2 | 0.5B-72B | 128K (72B) | Apache 2.0; Tongyi Qianwen for 72B | 84.2 MMLU (72B) |
| DeepSeek-V2 | 236B total (21B active) | 128K | MIT (code); DeepSeek Model License | Not listed here |
| Falcon 180B | 180B | 2K | Falcon-180B TII License | Not listed here |
| Code Llama 70B | 70B | 16K | Llama 2 license | 53.7% HumanEval (34B model) |
| Yi-1.5 | 6B/9B/34B | 4K (34B base) | Apache 2.0 | Not listed here |
| BLOOM | 176B | 2K | BigScience RAIL | Not listed here |

Scores come from each vendor's own evaluations and are not directly comparable across models.

## What Benchmarks Apply to Fine-Tuning and Deployment Costs for Custom Solutions?

**Fine-tuning time and deployment cost depend on model size, method (LoRA, QLoRA or full fine-tuning), hardware and hosting provider. Small models such as Phi-3 Mini and Llama 3 8B are the cheapest to adapt and serve, while 70B+ and large MoE models need multi-GPU hardware.**

### Fine-Tuning Comparison

1.  Download weights from Hugging Face for the target model and accept its license.
2.  Install a recent version of the Transformers, PEFT and TRL libraries.
3.  Apply LoRA or QLoRA to smaller models such as Llama 3 8B, Phi-3 Mini or Gemma 2 9B on a single GPU.
4.  Use multi-GPU setups for large models such as Mixtral 8x22B, Qwen2-72B or DBRX.
5.  Evaluate on a held-out set and compare with the base model.
6.  Check that your use complies with the model license before deployment.

LoRA trains a small set of adapter weights instead of the full model; the LoRA paper reports up to 10,000 times fewer trainable parameters than full fine-tuning of GPT-3 175B. Full fine-tuning of Qwen2-72B updates all 72B parameters and needs far more hardware. Phi-3 Mini needs a fraction of the resources of BLOOM 176B.

### Real-World Deployment Cost Breakdown

Deployment scales models for production. Costs depend on GPU type, cloud provider, region, utilization and whether you self-host or use a hosted API. 70B dense models typically need multiple high-memory GPUs or quantization. Falcon 180B and BLOOM 176B need multi-GPU servers. Small models such as Phi-3 Mini and Gemma 2 2B can run on a single consumer GPU or on-device. Check current cloud and API pricing pages, as rates change frequently.

Lightweight models such as Phi-3 cost far less to serve than 180B-class models. DeepSeek reports that DeepSeek-V2's MoE design boosts maximum generation throughput to 5.76 times that of DeepSeek 67B.

### Performance vs. Resource Efficiency

Performance balances speed and resources. Throughput depends on hardware, quantization, batch size and serving software, so vendor or community speed figures rarely transfer between setups. MoE models such as Mixtral, DBRX and DeepSeek-V2 activate only part of their parameters per token, which reduces compute per token. Small dense models such as Phi-3 Mini and Gemma 2 2B run on laptops and phones.

Microsoft reports that Phi-3 Mini's 69% MMLU score rivals much larger models such as Mixtral 8x7B. Measure throughput on your own hardware before choosing a model.

For coding-specific options, review our [AI coding tools guide](https://aitoolranked.com/blog/best-ai-for-coding-2026-developer-guide), which covers open-source and commercial tools.

## What Ethical Considerations Apply to Open-Source LLM Development?

**Ethical considerations in open-source LLM development include safety tuning and filtering (such as Meta's Llama Guard), watermarking and provenance tools (such as Google's SynthID Text), license-based use restrictions (such as BLOOM's RAIL license), and the ability to audit open weights.**

Meta applied safety fine-tuning to Llama 3 and released Llama Guard for content filtering. Mistral publishes models under Apache 2.0 without additional use restrictions. Google's Responsible Generative AI Toolkit for Gemma includes SynthID Text. Microsoft's Phi-3 report describes safety alignment. DBRX's license includes an acceptable use policy. Qwen2 was trained on data covering many languages. DeepSeek documents its alignment process in the DeepSeek-V2 paper. Falcon 180B was trained largely on the filtered RefinedWeb dataset. Code Llama was released with a responsible use guide. BLOOM was developed under BigScience's ethical charter with a RAIL license.

Permissive licenses like MIT for Phi-3 allow custom safety layers. Developers can add watermarking with SynthID Text for traceability. Open weights enable audits that are not possible with closed models like Gemini.

Other ethical open-source options are covered in our guide to ChatGPT alternatives.

## How Do I Choose the Best Open-Source LLM for My Project?

**Choose an open-source LLM by matching it to your needs: small models such as Phi-3 Mini or Gemma for budget fine-tuning and edge use, MoE models such as Mixtral or DeepSeek for efficient large-scale serving, and permissively licensed models (Apache 2.0 or MIT) when license freedom matters. Evaluate candidates on your own data.**

### On a Budget

Phi-3 Mini suits tight budgets with 3.8B parameters and single-GPU fine-tuning. DeepSeek-V2's MoE design reduces serving compute per token. Gemma 2 2B deploys on-device without cloud costs. Llama 3 8B is a free download that fine-tunes on a single GPU with LoRA. Smaller Code Llama sizes adapt to code tasks at lower cost than the 70B model.

### For High-Performance Custom Solutions

Mixtral 8x22B offers 64K context and MoE efficiency. Qwen2-72B scores 84.2 on MMLU and 91.0 on C-Eval. DBRX scores 70.1% on HumanEval. Llama 3.1 70B handles 128K context for complex tasks. Yi-1.5 34B is an Apache 2.0 option in the 34B class.

### For Ethical and Compliant Development

Gemma is supported by Google's Responsible Generative AI Toolkit, including SynthID Text. Falcon 180B's license includes an acceptable use policy. Llama 3 comes with Llama Guard and a responsible use guide. BLOOM's RAIL license restricts harmful uses. Mistral's Apache 2.0 license gives the clearest commercial terms.

| Model | Pros | Cons |
| --- | --- | --- |
| Llama 3 / 3.1 | 128K context (3.1), LoRA ease | High VRAM for 70B; custom license |
| Mixtral | MoE efficiency, Apache 2.0 | All experts must fit in memory |
| Gemma 2 | Lightweight, single-GPU 27B | Shorter 8K context |
| Phi-3 | Edge deploy, low cost | Smaller 3.8B scale |
| Qwen 2 | 84.2 MMLU (72B), 128K context | 72B uses Tongyi Qianwen license |

1.  Assess project needs like context or language.
2.  Download from Hugging Face repository.
3.  Test fine-tuning on sample dataset.
4.  Deploy via a hosted API provider or your own cloud GPUs.
5.  Iterate with ethical checks.

## What Future Awaits Open-Source LLMs?

**The trends visible in 2024 have largely played out: mixture-of-experts designs now dominate the largest open models, context windows of 128K and more are common, and licenses have become more permissive, with Gemma 4 moving to Apache 2.0 and DeepSeek-V4-Pro using MIT.**

Llama 4 Scout supports a 10 million token context window. Mistral's current flagship open model, Mistral Large 3, is a 675B-parameter MoE with 41B active parameters. Gemma 4 includes edge models for phones and a 26B MoE model. Qwen's open-weight lineup now spans the Qwen3.8 family. DeepSeek has moved from V2 to V4.

The Hugging Face Hub hosts the weights for all of these model families. Developers build on permissive licenses to create derivative models and tools.

## Frequently Asked Questions

### What are the best open source LLMs for fine-tuning in 2026?

For current releases, look at Llama 4, Qwen3.8, DeepSeek-V4 and Gemma 4. Among the 2024 generation covered here, Llama 3 8B, Phi-3 Mini and Mistral's Mixtral are popular for LoRA fine-tuning. Always check Hugging Face for the latest weights and license terms.

### How do deployment costs compare for open-source LLMs?

Open-source LLMs like Gemma and Phi-3 are free to download, but hosting costs vary by provider, hardware and model size. Small models are far cheaper to serve than 70B+ or large MoE models. Check current prices on your chosen API provider or cloud platform.

### Which open-source LLM is best for ethical AI development?

Google's Gemma is supported by the Responsible Generative AI Toolkit, including SynthID Text watermarking, and Llama 3 ships with Llama Guard. BLOOM's RAIL license restricts harmful uses. For ethical projects, choose models whose licenses and documentation support audits and alignment work.

### Can I use these open-source LLMs for commercial applications?

Usually, but licenses differ. Mixtral, Yi-1.5 and most Qwen2 sizes use Apache 2.0, and Phi-3 uses MIT. Llama 3 uses the Meta Llama 3 Community License, Qwen2-72B uses the Tongyi Qianwen license, Falcon 180B has its own TII license, and BLOOM's RAIL license restricts specific uses. Read each license before deploying.

### What performance benchmarks should I consider for open-source LLMs?

Consider MMLU for general knowledge, HumanEval and MBPP for coding, GSM8K for math, and context length (up to 128K for Qwen2-72B and Llama 3.1). MoE architectures such as DBRX and DeepSeek-V2 reduce compute per token. Test on your own use case.

### How has the open-source LLM landscape evolved since 2024?

Smaller efficient models like Phi-3 and Gemma's edge models have made on-device deployment practical, and MoE designs (DeepSeek-V2 and its successors, Mixtral, Llama 4) have become standard for large models. As of September 2026, the latest open-weight releases include DeepSeek-V4-Pro, Qwen3.8 and Gemma 4.

## Sources

-   [Introducing Meta Llama 3.1 — Meta AI](https://ai.meta.com/blog/meta-llama-3-1/)
-   [Introducing Meta Llama 3 — Meta AI](https://ai.meta.com/blog/meta-llama-3/)
-   [Cheaper, Better, Faster, Stronger: Mixtral 8x22B — Mistral AI](https://mistral.ai/news/mixtral-8x22b)
-   [Qwen2-72B model card — Hugging Face](https://huggingface.co/Qwen/Qwen2-72B)
-   [DeepSeek-V2 model card — Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2)
-   [Phi-3 Technical Report — arXiv](https://arxiv.org/abs/2404.14219)
-   [Introducing DBRX — Databricks](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm)
-   [Gemma 2 is now available to researchers and developers — Google](https://blog.google/technology/developers/google-gemma-2/)

---

Source: https://aitoolranked.com/blog/best-open-source-llm-2026-llama-deepseek-qwen-comparison