Best Open Source Large Language Models (LLMs)

An open source LLM is a large language model that people can use, study, modify, and share with enough access to understand and change how it was built. But many models described as “open source” publish downloadable weights and inference code but not the complete training data, data-processing pipeline, or reproducible training artifacts. They are more accurately called open-weight models.

For many AI teams, closed-source options like the GPT-5.6 family (Sol, Terra, and Luna) and Claude Fable 5 are convenient. You can prototype an AI product in minutes with a simple API call. But this ease comes with trade-offs of vendor lock-in, limited customization, unpredictable costs and performance, plus data privacy concerns.

That’s why open-source LLMs like GLM-5.2, DeepSeek-V4, and MiMo-V2.5-Pro have become so important. And each comes with its own distinct strengths in reasoning, multimodal understanding, and efficiency.

This post will explore the best open-source LLMs. After that, we will answer some of the FAQs teams have when evaluating free LLMs for production use.

TL;DR

• An open source LLM gives users the freedom to use, study, modify, and redistribute the model. True openness includes model weights, training and inference code, and sufficient documentation of the training data along with downloadable weights.
• The best open source LLM depends on your workload. Qwen3-Coder-Next is one of the best open-source coding LLMs; Kimi K2.6 and Qwen3.5 lead multimodal AI agents, while GLM-5.2, DeepSeek-V4-Pro, MiMo-V2.5-Pro, and LongCat-2.0 deliver frontier reasoning and long-context performance.
• If you want a local LLM, smaller dense models like OLMo 3 7B can run on consumer hardware after quantization. Large Mixture-of-Experts (MoE) open source AI models reduce inference costs but still require significant storage and memory.
• A free open-source LLM is not free to operate. Running an open source AI model still requires compute, storage, networking, monitoring, security, engineering, and maintenance costs.
• Many open-source LLMs support commercial use through permissive licenses such as Apache 2.0 or MIT, but always review the specific model license, attribution requirements, and acceptable-use terms before deploying in production.
• To choose the right open source LLM, evaluate benchmark performance (MMLU-Pro, HLE, GPQA, SWE-bench, LiveCodeBench, LiveBench), licensing, hardware requirements, deployment ecosystem, and your specific production use case.

The best open source LLM models at a glance

Here is the table comparing important fully open and open-weight model releases available as of this writing. 

What is an open source LLM?

The term open source LLM is used as a catch-all term for any model with downloadable weights. But an actual open source LLM (Large Language Model) makes enough of its development artifacts available for developers to understand, reproduce, modify, and improve the model.

These artifacts typically include:

• Model architecture and tokenizer
• Pre-trained model weights
• Training and inference code
• Training recipe and hyperparameters
• Dataset or dataset documentation
• Data preprocessing and filtering pipeline
• Post-training data and alignment methods
• Evaluation harnesses and benchmark results
• Chat templates and inference configuration
• License terms governing use, modification, and redistribution

Models that release most of these artifacts provide greater transparency, reproducibility, and flexibility in the long run. By comparison, open-weight models give developers less visibility into how the model was trained or how its capabilities were achieved.

The table below summarizes the common release models you will encounter: open source vs. open weights vs. source available

How we evaluated free LLMs

Selecting the best open-source LLM involves evaluating models using a set of technical and practical criteria based on official documentation, technical reports, model cards, and independent benchmark evaluations.

We evaluated each open-source LLM based on:

• Open-source status and licensing: Availability of model weights, license type, commercial-use permissions, redistribution rights, attribution requirements, and alignment with the Open Source Initiative (OSI) definition where applicable.
• Real-world capabilities: Performance across reasoning, coding, tool calling, multilingual tasks, retrieval, multimodal understanding, long-context reasoning, and AI agent workflows.
• Benchmark performance: Results on benchmarks such as MMLU-Pro, Humanity’s Last Exam (HLE), GPQA Diamond, AIME 2025, LiveCodeBench, SWE-bench Verified, LiveBench, Arena-Hard, BFCL, MMMU, and LongBench. We also highlight where official evaluations show a model outperforming proprietary alternatives like GPT-5, Claude 4, or Gemini 3 on specific tasks.
• Architecture efficiency: Dense vs. Mixture-of-Experts architecture, active parameters, attention mechanisms, inference throughput, and quantization support.
• Context and deployment: Native context window, long-context quality, support for Hugging Face Transformers, vLLM, SGLang, llama.cpp, Ollama, and production readiness.
• Hardware requirements: Memory footprint, GPU recommendations, FP16 and BF16 support, quantization options, tensor/expert parallelism, and local deployment feasibility.
• Safety and transparency: Model cards, documented limitations, responsible AI guidance, provenance information, update frequency, and reproducibility.

12 Open Source LLM Models and Open-Weight Models

We will compare free LLM models using a consistent evaluation framework. For each open-source LLM, we examine its benchmark performance, architecture, licensing, and deployment options.

GLM-5.2

Best for long-horizon coding with flexible reasoning effort.

GLM-5.2 is Z.ai’s flagship open-weight model for long-horizon tasks with a stable one-million-token context and multiple thinking-effort levels for balancing coding quality against latency. The IndexShare architecture reuses an indexer across groups of sparse-attention layers for efficient long-context inference. It reduces the computational cost per generated token by up to 2.9× at a one-million-token context compared with the previous approach.

• Benchmark results: It scores 99.2 on AIME 2026, ahead of Claude Opus 4.8 at 95.7, GPT-5.5 at 98.3, and Gemini 3.1 Pro at 98.2, and reaches 91.0 on IMOAnswerBench versus Claude at 83.5 and Gemini at 81.0. For software engineering, GLM-5.2 62.1 SWE-bench Pro result beats GPT-5.5 at 58.6 and Gemini at 54.2 but remains below Claude at 69.2. Its best-reported Terminal-Bench 2.1 score of 82.7 exceeds Claude’s 78.9 and Gemini’s 70.7, while FrontierSWE at 74.4 beats GPT’s 72.6 and Gemini’s 39.6. 

GLM-5.2 official model card.

LongCat-2.0

Best for large-scale coding, search, and enterprise agent workflows.

LongCat-2.0 is a 1.6T-parameter MoE with about 48B active per token. The model introduces LongCat Sparse Attention and emphasizes code understanding, repository edits, automated task execution, search, and agentic workflows.

The weights are released under MIT terms, but practical self-hosting requires a distributed accelerator environment. LongCat-2.0 is a model to evaluate when an organization already operates large inference clusters or plans to use a managed endpoint. Smaller teams should compare the business value against Qwen3-Coder-Next or a hosted frontier API before taking on the operational burden.

• Benchmark results: LongCat-2.0 reaches 59.5 on SWE-bench Pro, beating Gemini 3.1 Pro at 54.2, GPT-5.5 at 58.6, and Claude Opus 4.6 at 57.3, though it trails newer Claude 4.7 and 4.8 results. Its 78.8 RWSearch score beats Gemini at 76.3 and Claude Opus 4.8 at 77.3, while IFEval reaches 90.0, above Claude 4.7 at 88.7 and Claude 4.8 at 86.0. 

For a frontier open source LLM, the clearest proprietary wins are selected repository coding, search, and instruction-following tasks; the same table shows that it does not lead every agent or knowledge benchmark. 

LongCat-2.0 official model card.

DeepSeek-V4 Pro and Flash

Best for choosing between maximum capability and a smaller million-token model.

DeepSeek-V4 is released as two MoE sizes. The Pro checkpoint has 1.6T total parameters and 49B active; Flash has 284B total and 13B active. Both support a one-million-token context. The architecture combines compressed sparse attention methods intended to reduce long-context inference compute and KV cache usage relative to DeepSeek-V3.2.

The two-size strategy is useful for capacity planning. Pro targets the hard reasoning and agentic coding tasks, while Flash is the more efficient candidate. 

• Benchmark results: DeepSeek-V4-Pro-Max scores 93.5 on LiveCodeBench, above Claude Opus 4.6 at 88.8 and Gemini 3.1 Pro at 91.7, and its Codeforces rating of 3206 exceeds GPT-5.4 at 3168 and Gemini at 3052. It also leads Apex Shortlist with 90.2 vs Claude at 85.9, GPT at 78.1, and Gemini at 89.1. On Toolathlon, its 51.8 beats Claude and Gemini but trails GPT-5.4 at 54.6; on SWE-bench Verified, 80.6 matches Gemini and remains slightly below Claude’s 80.8. 

DeepSeek-V4 Pro and Flash official model card

MiMo-V2.5-Pro

Best for long-horizon software engineering with one-million-token context.

MiMo-V2.5-Pro scales Xiaomi’s architecture to 1.02T total parameters and 42B active parameters. It supports one million tokens and targets complex software engineering, agentic work, and trajectories that span thousands of tool calls. Hybrid sliding-window and global attention reduces the cost of attending over long sequences, while multi-token prediction supports faster decoding.

• Benchmark results: MiMo-V2.5-Pro scores 73.7% on MiMo Coding Bench, surpassing prominent proprietary models like Gemini 3.1 Pro (67.8%) and 57.2% on SWE-bench Pro. It sustains coherent execution across nearly 1,000 tool calls in a single task, allowing it to autonomously complete full-stack app development and chip EDA design.

MiMo-V2.5-Pro official model card.

MiMo-V2-Flash

Best for a lower-active-parameter agentic MoE serving target.

Xiaomi’s MiMo-V2-Flash uses a hybrid attention design that interleaves sliding-window and global attention to reduce KV-cache storage, while multi-token prediction is intended to improve decoding speed. Post-training emphasizes coding and agentic behavior.

• Benchmark results: MiMo-V2-Flash scores 71.7 on SWE-bench Multilingual, beating GPT-5 High at 55.3 and Claude Sonnet 4.5 at 68.0, which supports its use for multilingual repository repair. It also records 80.3 on tau2-Bench, narrowly above GPT-5’s 80.2 but below Claude’s 84.7. On AIME 2025, its 94.1 beats Claude’s 87.0 while remaining just behind GPT-5 at 94.6 and Gemini 3.0 Pro at 95.0. The 73.4 SWE-bench Verified score does not beat GPT-5 or Claude, so the clearest advantage is efficient agentic and multilingual coding rather than universal coding leadership. 

Source: Official model card

Gemma 4

Best for local AI, multimodal applications, and efficient enterprise deployment.

Gemma 4 is Google’s latest family of open-weight LLMs built from the same research that powers Gemini models. Gemma 4 focuses on delivering strong reasoning, coding, and multimodal performance in smaller, more deployable checkpoints ranging from 2B to 31B, including a 26B MoE model. 

Larger variants support up to a 256K-token context window, native image understanding, multilingual generation across more than 140 languages, and broad compatibility with frameworks such as Hugging Face Transformers, vLLM, Ollama, llama.cpp, and Vertex AI.

Despite its smaller size, Gemma 4 achieves competitive performance on reasoning, coding, and multimodal benchmarks. The Gemma 4 31B model outperforms Llama 4 Maverick, DeepSeek-V3, and OpenAI o3-mini on LMArena, while the lightweight Gemma 4 27B surpasses Gemini 2.0 Flash-Lite, Mistral Small 3.1, and Llama 3.3 70B on the multimodal MMMU benchmark. 

Gemma 4 offers one of the highest intelligence-per-parameter ratios among publicly released open-source LLMs. This makes it an excellent choice for organizations seeking strong performance without the need to operate large distributed GPU clusters.

Gemma 4 official model card.

Qwen3-Coder-Next

Best open-source coding LLM for efficient agent deployments.

Qwen3-Coder-Next is built for coding agents and local-development workflows. It uses 80B total parameters but activates only 3B per token, supports a native 262,144-token context, and is trained for long-horizon reasoning, complex tool use, and recovery from execution failures. The model is non-thinking by design, which can simplify latency and output parsing.

• Benchmark results: Qwen’s official coding-agent chart reports 70.6 on SWE-bench Verified, 62.8 on SWE-bench Multilingual, 44.3 on SWE-bench Pro, 36.2 on Terminal-Bench 2.0, and 66.2 on Aider. On SWE-bench Pro, the model sits only 0.1 point behind Claude Sonnet 4.5 at 44.4 and 2.3 points behind Claude Opus 4.5 at 46.6 while activating just 3B parameters. 

Qwen3-Coder-Next official model card.

Qwen3.5-397B-A17B

Best for multilingual multimodal agents with broad deployment support.

Qwen3.5-397B-A17B combines a vision encoder with a 397B-parameter MoE language model that activates 17B parameters per token. It supports 262,144 tokens and can be extended toward one million tokens with the official long-context configuration. It supports vision-language training, agentic capabilities, coding, and support for 201 languages and dialects.

• Benchmark results: Qwen3.5 records 76.5 on IFBench, ahead of GPT-5.2 at 75.4, Claude 4.5 Opus at 58.0, and Gemini 3 Pro at 70.4, and its 67.6 MultiChallenge score also leads all three. With the card’s discard-all context strategy, BrowseComp rises to 78.6, above GPT-5.2 at 65.8, Claude at 67.8, and Gemini at 59.2. For multimodal use, it scores 88.6 on MathVision, 90.8 on OmniDocBench 1.5, and 93.1 on OCRBench, beating the three proprietary baselines in those tables. 

As a multimodal open source LLM, its clearest wins are instruction following, agentic search, visual math, and document understanding rather than SWE-bench leadership. 

Qwen3.5-397B-A17B official model card.

Kimi K2.6

Best for multimodal coding, interface generation, and orchestrated agents.

Kimi K2.6 keeps the 1T-total, 32B-active MoE structure and 256K context of the K2 family, then adds a 400M-parameter MoonViT vision encoder. Moonshot targets coding design, visual-to-interface generation, and agent orchestration. It features an agent-swarm mode that coordinates up to 300 sub-agents over 4,000 steps.

• Benchmark results: Kimi K2.6 scores 54.0 on HLE-Full with tools, ahead of GPT-5.4 xHigh at 52.1, Claude Opus 4.6 at 53.0, and Gemini 3.1 Pro at 51.4. Its DeepSearchQA results reach 92.5 F1 and 83.0 accuracy, beating all three proprietary baselines, while SWE-bench Pro reaches 58.6 versus GPT-5.4 at 57.7, Claude at 53.4, and Gemini at 54.2. Terminal-Bench 2.0 is 66.7, above GPT and Claude at 65.4 but below Gemini at 68.5.

Kimi K2.6 official model card.

K2-V2

Best for full-stack pretraining research and long-context experimentation.

K2-V2 is a 360-open, reasoning-enhanced LLM with a 70B-parameter dense model. The release links the pretraining, mid-training, and supervised fine-tuning data mixtures together with training and evaluation code.

K2-V2 is a better choice for teams studying data mixtures, scaling, long-context training, or downstream alignment than for teams that simply need a ready-to-use customer chatbot. A 70B dense model also carries a substantial inference cost compared with smaller quantized checkpoints.

• Benchmark results: K2-V2’s mid-4 checkpoint scores 55.1 on GPQA-Diamond, 91.4 on MATH, 46.9 on AIME 2025, and 61.8 on MBPP. Its supervised-fine-tuned High variant reaches 80.2 on AIME25, 69.3 on GPQA-D, and 91.5 on HumanEval. 

K2-V2 official model card.

Kimi K2 Thinking

Best for long reasoning chains with repeated tool use.

Kimi K2 Thinking is an MoE model and trained to interleave reasoning and function calls across long agent trajectories. It shows stable behavior across 200–300 sequential tool invocations and offers a native INT4 checkpoint from quantization-aware training.

The native low-bit format can reduce inference cost relative to a high-precision checkpoint, but the full 1T-scale weight set still belongs in distributed infrastructure. 

• Benchmark results: Moonshot reports 44.9 on HLE with tools, ahead of GPT-5 High at 41.7 and Claude Sonnet 4.5 Thinking at 32.0, and 60.2 on BrowseComp, ahead of GPT-5 at 54.9 and Claude at 24.1. For coding, K2 Thinking reaches 61.1 on SWE-bench Multilingual and 41.9 on Multi-SWE-bench, beating GPT-5’s reported 55.3 and 39.3, while its 47.1 Terminal-Bench score also exceeds GPT-5’s 43.8 but remains below Claude’s 51.0. 

Kimi K2 Thinking official model card.

LongCat-Flash-Thinking-2601

Best for tool-augmented reasoning in noisy, changing environments.

Meituan’s January 2026 LongCat update is a 560B-class MoE reasoning model. Its training focuses on environment scaling, multi-environment reinforcement learning, tool use, search, and robustness to imperfect results from tools or environments. A Heavy Thinking mode explores multiple trajectories before aggregating a final answer.

This model is relevant for teams building research or operations agents that must recover from ambiguous tool responses. The weights are licensed under the MIT license and include vLLM and SGLang adaptations.

• Benchmark results: With context management, LongCat-Flash-Thinking-2601 reaches 73.1 on BrowseComp versus GPT-5.2 Thinking at 65.8. It scores 99.3 on the tau2-Telecom tool-use task, above GPT-5.2 at 98.7 and Claude Opus 4.5 at 98.2, and 35.8 on Random Complex Tasks, above Claude at 32.6, Gemini 3 Pro at 32.5, and GPT-5.2 at 17.2. Heavy Thinking reaches 86.8 on IMO-AnswerBench, narrowly above Gemini’s 86.7 and Claude’s 82.8. Its 70.0 SWE-bench Verified result trails the proprietary leaders, so its best closed-model wins are search, noisy tool use, and agent generalization. 

LongCat-Flash-Thinking-2601 official model card.

Why teams use open source AI models and why a free LLM still has costs

Open models are attractive because they move important design choices from the API provider to the deploying organization. The value is not simply free AI. The value is control over the model revision, infrastructure, data path, evaluation process, and adaptation strategy.

• Data control and privacy: A self-hosted model can keep prompts, retrieved documents, tool outputs, and logs inside a private network. This helps when legal, contractual, or operational requirements make third-party processing difficult.
• Customization: Teams can use supervised fine-tuning, LoRA adapters, preference optimization, distillation, quantization, constrained decoding, custom chat templates, and domain-specific retrieval without waiting for a provider feature.
• Version stability: A pinned checkpoint does not change unless the team changes it. This makes regression testing and regulated validation easier than relying on a continuously updated hosted alias.
• Deployment flexibility: The same model can be served on premises, in a private cloud, through a managed endpoint, or behind an OpenAI-compatible internal API.
• Research transparency: Fully open releases such as K2-V2 expose more of the training process. It enables researchers to examine data mixtures, checkpoints, optimization choices, and post-training methods.
• Economics at sustained volume: Self-hosting can reduce marginal inference cost when utilization is high and infrastructure is efficiently shared. At low or unpredictable traffic, a managed API may still be cheaper and easier.

Limitations you should plan for

• Infrastructure burden: Large checkpoints require fast storage, high-bandwidth networking, accelerator memory, tensor or expert parallelism, observability, autoscaling, and failure recovery.
• Security and safety ownership: The deploying team must handle authentication, prompt-injection defenses, tool permissions, abuse controls, content policies, red teaming, and incident response.
• License ambiguity: “Open” marketing language may hide modified terms, acceptable-use policies, branding requirements, or differences between the downloadable model and hosted service.
• Evaluation noise: Vendor-reported benchmark scores often use different reasoning budgets, tool access, prompts, context management, and agent harnesses.
• Model maintenance: Serving frameworks move quickly. A new architecture may require nightly builds, custom kernels, remote code, special parsers, or a specific quantization format.
• Total cost: GPU idle time, engineering, monitoring, data pipelines, security, and support can outweigh token-price savings.

How to choose the best open source LLM

A model shortlist should begin with constraints. Use the following sequence to avoid selecting a model that performs well in a vendor demo but cannot be licensed, served, or evaluated in your environment.

1. Define the workload. Separate chat, retrieval-augmented generation, code completion, repository agents, research agents, multimodal document analysis, and long-form reasoning. Each workload stresses different capabilities.
2. Set the license boundary. Decide whether the organization accepts Apache 2.0, MIT, modified licenses, or source-available terms. Record commercial use, redistribution, derivative works, attribution, patents, trademarks, and acceptable-use conditions.
3. Choose the deployment tier. Decide whether the target is a laptop, single GPU, multi-GPU server, distributed cluster, or managed endpoint. Eliminate checkpoints that cannot fit the budget.
4. Test realistic context. Do not select a one-million-token model because the maximum number looks impressive. Measure quality, first-token latency, prefill throughput, KV-cache memory, and cost at your common 8K, 32K, 128K, or larger prompts.
5. Validate modality and tools. Check whether the exact downloadable checkpoint supports vision, reasoning output, function calling, structured JSON, parallel tools, and the chat template expected by your agent framework.
6. Run task-specific evaluations. Build a private test set containing real documents, repositories, edge cases, security prompts, and failure scenarios. Measure correctness, tool success, latency, cost, and recovery—not only style preference.
7. Plan operations. Confirm support for serving engine, quantization availability, loading time, autoscaling, observability, fallback behavior, and version pinning.

Best open source coding LLM and other model picks by use case

For multi-model systems, route tasks by workload instead of forcing one model to handle everything. A smaller coding model can handle routine repository operations, while a larger reasoning model receives only complex planning or recovery tasks. See our guide to AI agent routing for a practical routing framework.

How to host an open source LLM

Hosting can be as simple as opening a quantized model in a desktop application or as complex as sharding a trillion-parameter MoE across a cluster. The workflow below separates experimentation from production.

Option 1: Run a small quantized model with Ollama or LM Studio

Ollama and LM Studio provide local model catalogs, downloads, chat interfaces, and local API endpoints. Use them when a supported GGUF or other compatible quantization exists. Confirm that the quantization came from a trusted publisher and that the model’s license applies to the converted weights. Desktop tools are ideal for prototyping, not automatically for multi-user production.

Option 2: Use Hugging Face Transformers for development

Hugging Face Transformers provides the easiest way to load an open-source LLM, experiment with prompts, benchmark model performance, fine-tune on custom datasets, and integrate the model into Python applications. The example below demonstrates how to load the LongCat-Flash-Thinking-2601 checkpoint directly from Hugging Face for local inference and development.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meituan-longcat/LongCat-Flash-Thinking-2601"

# Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please tell me what is $$1 + 1$$ and $$2 \times 2$$?"},
    {"role": "assistant", "reasoning_content": "This question is straightforward: $$1 + 1 = 2$$ and $$2 \times 2 = 4$$.", "content": "The answers are 2 and 4."},
    {"role": "user", "content": "Check again?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    enable_thinking=True,
    add_generation_prompt=True,
    save_history_reasoning_content=False # Discard reasoning history to save tokens
)

# Template Output Structure:
# <longcat_system>You are a helpful assistant.<longcat_user>Please tell me what is $$1 + 1$$ and $$2 \times 2$$?<longcat_assistant>The answers are 2 and 4</longcat_s><longcat_user>Check again? /think_on <longcat_assistant><longcat_think>\n

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

print(tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n"))

# Example Output:
# The user wants a double-check. Since $$1 + 1 = 2$$ and $$2 \times 2 = 4$$ are basic arithmetic truths, the previous answer is correct.\n</longcat_think>\nI have verified the calculations: $$1 + 1 = 2$$ and $$2 \times 2 = 4$$. The initial answer remains correct.</longcat_s>

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meituan-longcat/LongCat-Flash-Thinking-2601"

# Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please tell me what is $$1 + 1$$ and $$2 \times 2$$?"},
    {"role": "assistant", "reasoning_content": "This question is straightforward: $$1 + 1 = 2$$ and $$2 \times 2 = 4$$.", "content": "The answers are 2 and 4."},
    {"role": "user", "content": "Check again?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    enable_thinking=True,
    add_generation_prompt=True,
    save_history_reasoning_content=False # Discard reasoning history to save tokens
)

# Template Output Structure:
# <longcat_system>You are a helpful assistant.<longcat_user>Please tell me what is $$1 + 1$$ and $$2 \times 2$$?<longcat_assistant>The answers are 2 and 4</longcat_s><longcat_user>Check again? /think_on <longcat_assistant><longcat_think>\n

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

print(tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n"))

# Example Output:
# The user wants a double-check. Since $$1 + 1 = 2$$ and $$2 \times 2 = 4$$ are basic arithmetic truths, the previous answer is correct.\n</longcat_think>\nI have verified the calculations: $$1 + 1 = 2$$ and $$2 \times 2 = 4$$. The initial answer remains correct.</longcat_s>

Option 3: Serve an OpenAI-compatible API with vLLM

vLLM is designed for high-throughput, memory-efficient serving. Many model cards now provide commands for launching an OpenAI-compatible endpoint. Qwen’s official example for Qwen3-Coder-Next uses tensor parallelism and can enable automatic tool choice.

pip install 'vllm>=0.15.0'

vllm serve Qwen/Qwen3-Coder-Next   --port 8000   --tensor-parallel-size 2   --enable-auto-tool-choice   --tool-call-parser qwen3_coder   --max-model-len 32768

pip install 'vllm>=0.15.0'

vllm serve Qwen/Qwen3-Coder-Next   --port 8000   --tensor-parallel-size 2   --enable-auto-tool-choice   --tool-call-parser qwen3_coder   --max-model-len 32768

The model supports a larger native context, but starting with 32K reduces memory risk. Increase the limit only after measuring available VRAM, prefill latency, throughput, and KV-cache use.

Option 4: Serve with SGLang

SGLang is another production serving framework commonly referenced by the latest model cards. It supports OpenAI-compatible APIs, tool parsers, reasoning parsers, tensor parallelism, and architecture-specific optimizations. New frontier releases may require a minimum version, a nightly build, or a model-specific cookbook.

pip install 'sglang[all]>=0.5.8'

python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --port 30000

pip install 'sglang[all]>=0.5.8'

python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --port 30000

Call the self-hosted endpoint from an application

import openai

client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print(response.choices[0].message.content)

import openai

client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="qwen/qwen2.5-0.5b-instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print(response.choices[0].message.content)

Do you need an LLM API key for an open source LLM?

The API key belongs to the service layer. When you run inference locally, your application can call a loopback endpoint with no authentication during development. When the endpoint is shared, create your own tokens or use an identity-aware gateway. When a cloud vendor hosts the model, it issues its own key and controls pricing, rate limits, logs, retention, regional availability, and model revisions.

Conclusion

Choosing the best open source LLM starts with matching the workload to the license, hardware, context, and evaluation evidence. Compare open source LLM models and confirm whether each open source large language model is fully open or only open weight. The broader open source AI models market rewards teams that document these distinctions.

Then select an open source coding LLM for software agents, a local LLM for constrained devices, or a self-hosted LLM for private infrastructure. A downloadable free LLM still has operating costs, while a managed endpoint usually requires an LLM API key. This is how an open source LLM becomes a maintainable production system rather than a one-time experiment.

Frequently asked questions

What is an open source large language model?

An open-source LLM is a language model released with freedoms to use, study, modify, and share it. Under the Open Source AI Definition, meaningful access also includes model parameters, training and inference code, and sufficiently detailed information about the training data and process. A downloadable checkpoint alone is usually open weight, not fully open source.

What is the best open-source LLM?

The best model depends on the workload. Choose OLMo 3 or K2-V2 for transparent research, Qwen3-Coder-Next for efficient coding agents, Kimi K2.6 or Qwen3.5 for multimodal agents, and GLM-5.2, DeepSeek-V4-Pro, MiMo-V2.5-Pro, or LongCat-2.0 for frontier-scale long-horizon tasks. Run a task-specific evaluation before production.

What Is the Best Open Source Coding LLM?

Qwen3-Coder-Next is a strong efficient option for coding agents. GLM-5.2, DeepSeek-V4-Pro, MiMo-V2.5-Pro, and LongCat-2.0 target harder long-horizon software work but require much larger infrastructure.

Which local LLM can run on consumer hardware?

Smaller dense models such as OLMo 3 7B can run locally after quantization, subject to RAM, VRAM, context length, and speed requirements. Frontier MoE checkpoints in this guide may activate only a fraction of their parameters per token, but the complete weight set still has to be stored and distributed. “Low active parameters” does not mean “laptop-sized.”

Is a free LLM actually free to deploy?

The weights may be downloadable without a purchase price, but deployment is not free. You still pay for storage, GPUs or cloud accelerators, power, networking, monitoring, engineering, security, and support. Modified or source-available licenses may also impose attribution, branding, use, or distribution conditions.

Are there any open-source LLM API keys available?

There is no universal API key for open-source LLMs. A self-hosted model can run without a vendor key, although you should protect the endpoint with your own authentication. A cloud provider or model developer issues a provider-specific API key when it hosts the model for you.

How Do I Deploy a Self-Hosted LLM?

Choose a checkpoint that fits the license and hardware, test it locally with a desktop app or Transformers, serve it through vLLM or SGLang, expose an authenticated API, and add monitoring, rate limits, evaluation, and rollback.

Is ChatGPT open source?

ChatGPT is a proprietary hosted product. It is not an open-source LLM with downloadable model weights and a reproducible training stack.

Can I use an open-source LLM commercially?

Often yes, especially under permissive licenses such as Apache 2.0 or MIT. But commercial use depends on the exact checkpoint license, associated acceptable-use terms, third-party notices, patent clauses, and any conditions in a modified license. Review the actual license text rather than relying on a model list.

How much GPU memory does an open-source LLM need?

It depends on total parameters, precision, quantization, context length, batch size, KV cache, and runtime overhead. A 7B 4-bit checkpoint may fit on consumer hardware, while the frontier MoE models in this guide need multi-GPU or distributed systems.

Are open-source LLMs safer than proprietary models?

Neither category is automatically safer. Open deployment provides more control and auditability, but the operator must build access controls, content safeguards, tool permissions, monitoring, red-team tests, and incident response.

How should I track open-source LLM news?

Follow official model organizations, repositories, technical reports, serving-framework release notes, and independent evaluation projects. Update a model matrix with the release date, license, exact checkpoint, context, framework support, and evaluation status.

Asad Iqbal

Asad Iqbal is a technical writer and researcher at the intersection of AI, machine learning, and quantum computing. With an MSc in Physics and over five years writing for AI and data companies, he brings something rare to science communication: the ability to read a research paper, understand what it actually means, and explain it clearly to practitioners. He founded QbitNeural to cover the AI and quantum research landscape with practitioner-level depth.