Key takeaways
- DeepSeek, Qwen, Kimi, GLM and gpt-oss lead open-weight models, with rankings shifting monthly.
- Function calling needs correct chat templates and tool parsers in your serving engine.
- Open rerankers and embedding models such as BGE-M3 complete a fully open RAG stack.
- Langfuse, Phoenix and similar tools trace prompts, costs and quality for open deployments.
- Most popular models are open-weight, while fully open models also release data and code.
Open-weight language models now rival proprietary ones on many tasks, and a full open-source stack exists around them: embedding models, rerankers, vector databases and observability tools. I use open models in practice, with Groq-hosted Llama in DocuLens AI and QLoRA fine-tuning in FineTune Studio, so this page covers the current best open LLMs, examples, Hugging Face picks, function calling, coding, rerankers, embeddings, vector search, observability, and open weight versus open source.
What is the current best open source LLM?
Leading open-weight models in 2026 come from the DeepSeek, Qwen, Kimi, GLM and gpt-oss families, with large mixture-of-experts models approaching frontier proprietary performance on many benchmarks. Rankings shift almost monthly as new releases arrive.
Also asked as: best open source llm huggingface · best open source llm models for coding · open weight vs open source llm
The best choice depends on hardware and task. Very large models such as DeepSeek's latest releases, Kimi K2 and the biggest Qwen3 models need multi-GPU servers or hosted providers. gpt-oss-120b runs on a single high-memory GPU, while mid-size models serve laptops and smaller servers. Licences vary from MIT and Apache 2.0 to custom terms. Check LMArena and task-specific leaderboards, then test shortlisted models on your own prompts before deciding.
What are examples of open source LLM models?
Examples include Meta's Llama family, Alibaba's Qwen, DeepSeek's V3 and R1, Mistral's models, Google's Gemma, Microsoft's Phi, OpenAI's gpt-oss, Moonshot's Kimi K2, Zhipu's GLM, and fully open research models such as AI2's OLMo.
Most are open-weight rather than fully open source: weights are downloadable, but training data and full training code are not. OLMo and similar projects release data, code and checkpoints, enabling full reproducibility. Sizes range from under one billion parameters for phones to hundreds of billions for servers. Many come in base, instruct, reasoning and coding variants. Community fine-tunes and quantizations on Hugging Face multiply the options further, so check provenance before using derivatives.
What is the best open source LLM on Hugging Face?
On Hugging Face, the most downloaded and trending open models include the Qwen3, Llama, Gemma, gpt-oss, DeepSeek and Mistral families, each with instruct and quantized versions. The best one depends on your size limits and task.
Use the Hub's filters for task, parameter size, licence and library, then sort by trending or downloads. Read model cards for benchmarks, context length, languages and intended use. Prefer official organisations or trusted quantizers for GGUF and other formats. Hugging Face's Inference Providers let you test large models without hosting them. Once you shortlist candidates, compare them on your own evaluation set, measuring quality, speed and memory on your target hardware.
What is the best open source LLM for function calling?
Strong open models for function calling include Qwen3, gpt-oss, Kimi K2, GLM-4.5 and later GLM releases, and recent Llama and Mistral models. The Berkeley Function Calling Leaderboard compares models on tool-use accuracy across many scenarios.
Also asked as: best function calling open source llm
Reliable function calling needs correct JSON arguments, choosing the right tool, knowing when not to call tools and handling multi-step sequences. Serving matters too: use inference engines such as vLLM or Ollama with each model's correct chat template and tool parser, or tool calls will fail. Smaller models handle single, well-described tools well but struggle with long agent chains. Validate arguments against schemas and test on your actual tools before production.
What are the best open source LLMs for coding?
Leading open coding models include Qwen3-Coder, DeepSeek's latest models, Kimi K2, GLM's recent releases, Mistral's Devstral and gpt-oss. Large versions approach proprietary models on benchmarks such as SWE-bench, while smaller variants run locally.
For editor autocomplete, small models such as Qwen2.5-Coder 1.5B or 7B respond quickly. For agentic coding, models trained on software engineering tasks, like Devstral and Qwen3-Coder, handle tool use and multi-file edits better. Running the largest models needs serious hardware or hosted APIs from inference providers. Test on your own repositories with tools such as Aider, Cline or OpenHands, measuring correctness and how much human review each change needs.
What are the best open source reranking models?
Strong open rerankers include BAAI's bge-reranker-v2-m3, mixedbread's mxbai-rerank models, Jina's rerankers, Qwen3-Reranker and ColBERT-style late-interaction models. They reorder retrieved passages by relevance, improving RAG answer quality.
Rerankers use cross-encoders that read the query and each passage together, which is more accurate but slower than embedding similarity, so apply them to the top twenty to one hundred candidates from first-stage retrieval. RAG.NextUpgrad reranks after merging dense and BM25 results with reciprocal rank fusion. Multilingual rerankers matter for Indian languages and mixed-language text. Check licences, since some models restrict commercial use, and measure latency on your hardware. Start without a reranker, then add one and measure the gain.
What are the best open source text embedding models?
Strong open embedding models include BGE-M3, Qwen3 Embedding, multilingual E5, Nomic Embed, GTE models and Google's EmbeddingGemma. The MTEB leaderboard compares them across retrieval, clustering and classification tasks.
Also asked as: best text embedding models open source
Choose by language coverage, maximum input length, dimension size, speed and licence. BGE-M3 supports dense, sparse and multi-vector retrieval in one model, useful for hybrid search. Smaller models such as EmbeddingGemma suit on-device use. Some popular embedding models carry non-commercial licences, so verify terms before shipping. Benchmark rankings rarely match your domain exactly, so build a small set of real queries with known relevant documents and measure recall for each candidate. Re-embed your corpus whenever you switch models.
What are the best open source vector search databases?
Popular open-source options include Qdrant, Milvus, Weaviate, Chroma, LanceDB, OpenSearch and Vespa, plus pgvector for PostgreSQL and libraries such as FAISS for in-memory search.
Qdrant and Weaviate offer strong filtering and hybrid search, Milvus targets very large deployments, Chroma and LanceDB suit local development and embedded use, and OpenSearch and Vespa combine full-text and vector search at scale. pgvector keeps vectors beside relational data with no extra service. FAISS is a library rather than a database, ideal for experiments. Choose by scale, filtering needs, operations and your team's existing stack, then benchmark on your data. Measure recall and latency with realistic filters, not just raw speed.
What is the best open source LLM observability tool?
Popular open-source LLM observability tools include Langfuse, Arize Phoenix, OpenLLMetry, Helicone, Opik and MLflow's tracing features. They record prompts, responses, latency, token usage, costs and evaluation scores for every model call.
Observability helps debug wrong answers, find slow or expensive steps and track quality over time. Most tools support OpenTelemetry and integrate with LangChain, LlamaIndex and provider SDKs. Self-hosting keeps sensitive prompts and data inside your infrastructure. Combine tracing with evaluation datasets, so every prompt or model change is measured before release. For RAG, log retrieved passages alongside answers, since many failures come from retrieval rather than generation. Start simple: even basic logs of inputs and outputs reveal most problems early.
What is the difference between open weight and open source LLMs?
Open-weight models release downloadable weights you can run and fine-tune, often under custom licences, but not their training data or full code. Fully open-source models also release training data, code and details under open licences, enabling reproduction.
The Open Source Initiative's Open Source AI Definition requires enough information about training data, plus code and weights under open terms. Most popular models, including Llama, Qwen and DeepSeek, are open-weight; projects such as OLMo aim for full openness. For most builders, open weights provide what matters day to day: local deployment, privacy and fine-tuning. For research, auditing and reproducibility, fully open models are more valuable. Always read the licence before commercial use.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
