Quick Answer
As of July 2026, GLM-5.2 is the strongest all-round open-weight LLM in this comparison, while Kimi K2.7 Code stands out for coding agents, Gemma 4 12B is a practical laptop model, and Nemotron 3 Super suits teams prioritizing open training resources. The right choice still depends on workload, hardware and licence.
Key Takeaways
- Best overall: GLM-5.2, particularly for long-context coding, reasoning and agentic work.
- Best for coding agents: Kimi K2.7 Code at data-centre scale; Qwen3-Coder-Next for a more efficient coding server.
- Best for local use: Gemma 4 for laptops and edge devices; Qwen3.6-27B for higher-end 24GB systems.
- Best for enterprise: Nemotron 3, because NVIDIA publishes weights, training data, recipes and evaluation resources.
What Does “Open-Source LLM” Actually Mean?
An open-weight LLM makes its trained parameters available for download. You can usually run it on your infrastructure, quantize it and fine-tune it, subject to its licence.
A fully open-source AI system, under the Open Source Initiative’s definition, must also provide the freedoms to use, study, modify and share the system. That requires sufficient training-data information, training and inference code, and model parameters in the preferred form for modification.
This distinction matters because:
- A model can have an MIT or Apache 2.0 weight licence without disclosing its complete training data.
- A licence described as “modified MIT” may include additional conditions.
- “Available for download” does not automatically mean OSI-compliant open source.
- Commercial use, redistribution and hosted-service rights must be checked against the exact model version.
How We Evaluated the Models
There is no single benchmark that identifies the best model for every workload. This ranking combines five factors:
- Current independent leaderboard position.
- Official model-card and technical-report results.
- Coding, reasoning, long-context and tool-use performance.
- Licence clarity and availability of supporting artifacts.
- Real deployment requirements, including total weight memory.
LiveBench is useful because it uses objective scoring and regularly refreshed questions intended to reduce benchmark contamination. SWE-bench Verified tests software-engineering systems on 500 human-validated GitHub issues, although results can change substantially depending on the agent scaffold and evaluation version.
Vendor benchmark tables are still included, but they should not be treated as perfectly comparable. Providers may use different prompts, reasoning budgets, tools, context lengths and agent frameworks.
Best Open-Source LLMs at a Glance
| Model | Best use | Licence | Context | Deployment reality |
|---|---|---|---|---|
| GLM-5.2 | Best overall; long-horizon coding and agents | MIT | 1 million tokens | Very large server model; use multi-GPU infrastructure or an API. |
| DeepSeek V4 Pro / Flash | Reasoning, knowledge work and efficient server inference | MIT | 1 million | Pro has 1.6T total/49B active parameters; Flash has 284B/13B active. Both remain data-centre models. |
| Kimi K2.7 Code | Long-running coding agents | Modified MIT | 256K | 1T total/32B active parameters; designed for distributed deployment rather than consumer GPUs. |
| Qwen3.6-27B | High-end local use, coding and tool calling | Apache 2.0 | 262K native; extensible to about 1M | Practical in 4-bit form on some 24GB systems when context is reduced. Full-context official examples use eight GPUs. |
| Gemma 4 | Laptops, mobile devices and private edge applications | Apache 2.0 | Up to 128K in the main edge stack | Includes E2B, E4B and 12B options, with extensive LiteRT support. |
| Nemotron 3 Super / Ultra | Auditable agents, RAG and enterprise customization | NVIDIA Open Model Licence; newer releases moving to OpenMDW-1.1 | 1 million for Super | Publishes weights, datasets, recipes and evaluation resources; optimized most heavily for NVIDIA infrastructure. |
| Mistral Small 4 | Enterprise multilingual, multimodal and document workflows | Apache 2.0 | 256K | 119B total/6B active; official minimum is four H100s, two H200s or one DGX B200. |
Which Is the Best Open-Source LLM Overall?
GLM-5.2
GLM-5.2 is the strongest general recommendation for teams that want a frontier-scale open-weight model.
It provides a one-million-token context window, multiple reasoning-effort levels and a focus on long-horizon coding and agentic work. Its developers also report that the IndexShare architecture reduces per-token computation at one-million-token context compared with their earlier sparse-attention design. The model and repository are released under the MIT licence.
Choose GLM-5.2 for:
- Repository-scale software work.
- Long-running research or coding agents.
- Large-document analysis.
- Complex tool-driven workflows.
- A permissively licensed frontier model.
Its main limitation is deployment size. The model is not a realistic choice for an ordinary desktop GPU, even when a community quantization is available.
Which Open-Source LLM Is Best for Reasoning?
DeepSeek V4 Pro for Maximum Quality
DeepSeek V4 Pro is a strong candidate for difficult knowledge, mathematics and reasoning workloads. It has 1.6 trillion total parameters, activates 49 billion per token and supports a one-million-token context window. DeepSeek describes this release as a preview, so production teams should expect possible changes in serving software and checkpoints.
DeepSeek V4 Flash for Better Efficiency
DeepSeek V4 Flash reduces the model to 284 billion total and 13 billion active parameters while retaining the one-million-token window. It is the more practical DeepSeek V4 option when throughput and serving cost matter more than the final few points of quality.
Both models are still far too large for normal desktop deployment. “Only 13B active” does not mean that Flash has the storage and memory footprint of a dense 13B model. The complete expert weights must still be stored, loaded, sharded or offloaded.
Which Is the Best Open-Source LLM for Coding?
Kimi K2.7 Code for Large Coding Agents
Kimi K2.7 Code is the most specialized option in this list for long-running coding agents.
It has one trillion total parameters, 32 billion activated parameters, a 256K context window and a native vision encoder. Moonshot reports that it uses approximately 30% fewer thinking tokens than Kimi K2.6 in its own evaluations.
It is designed for:
- Multi-file repository changes.
- Iterative execution and debugging.
- Long software-engineering tasks.
- Multi-step tool calls.
- Coding-agent frameworks.
Both its code and model weights use a Modified MIT Licence, not the standard MIT text. Commercial teams should review the modification rather than relying on the licence name alone.
Qwen3-Coder-Next for Efficient Coding Servers
Qwen3-Coder-Next has 80 billion total parameters but activates only three billion per token. It supports a native 262,144-token context and tool calling, with official deployment instructions for vLLM and SGLang. Its model card also identifies compatibility with scaffolds including Claude Code, Cline and Qwen Code.
It is efficient in computation, but its 80 billion total weights still make it a workstation or server model. The official card recommends reducing the context to 32K if a server runs out of memory.
Qwen3.6-27B for Local Coding
For a single 24GB-class GPU, a four-bit Qwen3.6-27B build with a reduced context window is a more realistic starting point. The raw four-bit weights are approximately 13.5GB before runtime overhead, the KV cache, vision components and temporary memory.
Treat 24GB compatibility as a deployment estimate, not a guarantee. The official full-context examples use tensor parallelism across eight GPUs and explicitly advise reducing context after an out-of-memory error.
Which Open-Source LLM Is Best for RAG?
A retrieval-augmented generation system has at least four important components:
- An embedding model.
- A search or vector index.
- A reranker.
- A generator LLM.
The largest generator is therefore not automatically the best RAG system.
Nemotron 3 Super for Transparent Enterprise RAG
Nemotron 3 Super is particularly attractive for regulated, sovereign or auditable deployments. It has 120 billion total and 12 billion active parameters, a native one-million-token context, and published weights, datasets, training recipes and evaluation infrastructure.
NVIDIA says the model was trained on 10 trillion unique curated tokens and 25 trillion total seen tokens. More importantly for reproducibility, NVIDIA publishes substantial parts of the pretraining, post-training and reinforcement-learning data pipeline.
This makes Nemotron more useful than many nominally permissive models when a team needs to inspect how a model was developed or adapt its training recipe.
Qwen3.6-27B for Smaller Private RAG
Qwen3.6-27B is a better fit for a smaller on-premises system. It offers enough capacity for document synthesis and tool calling without requiring the infrastructure of Nemotron, GLM or DeepSeek.
For RAG, start with a 16K–32K context even when the model supports much more. Retrieve a small number of high-quality passages, require source citations in the output and measure answer faithfulness. Increasing context length should be a response to measured retrieval failures, not the first design decision.
Which Open-Source LLM Is Best for Tool Calling and Agents?
For complex server-side agents, the strongest candidates are:
- GLM-5.2 for general long-horizon tasks.
- Kimi K2.7 Code for software agents.
- Nemotron 3 Super or Ultra for auditable multi-agent systems.
- Qwen3-Coder-Next for coding tools and IDE integration.
Nemotron’s documentation includes deployment cookbooks for vLLM, SGLang and TensorRT-LLM. The newer Nemotron Ultra materials also describe support for agent harnesses such as Hermes Agent and OpenClaw.
For a fully local OpenClaw-style setup, Gemma 4 12B is another practical option. Google’s LiteRT-LM server exposes a compatible local endpoint and specifically lists OpenClaw, OpenCode, Continue and Aider as possible clients.
Tool calling should be evaluated separately from normal chat. Measure:
- Valid JSON or schema-conformant output.
- Correct tool selection.
- Argument accuracy.
- Recovery after failed tool calls.
- Whether the agent stops when the task is complete.
Which Is the Best Multilingual Open-Source LLM?
Mistral Small 4 for Enterprise Deployment
Mistral Small 4 combines multimodal input, configurable reasoning, 119 billion total parameters, six billion active parameters and a 256K context window. It is a strong choice for multilingual document processing and enterprise assistants, but its name is misleading from a hardware perspective: Mistral’s minimum published infrastructure starts at four H100 GPUs.
Gemma 4 for Multilingual Edge Applications
Google says Gemma 4 supports more than 140 languages and can run across mobile, desktop, web, Raspberry Pi and other edge environments. Its Apache 2.0 licence also makes it easier to evaluate for commercial products than models with bespoke community licences.
Ministral 3 for Smaller Systems
The Ministral 3 family provides dense 3B, 8B and 14B models under Apache 2.0. Mistral describes the family as multimodal and multilingual across more than 40 native languages.
Language coverage does not guarantee equal quality in every language. Build a native-language test set covering terminology, tone, cultural context and safety before selecting a model.
What Can You Run with 8GB, 16GB or 24GB of VRAM?
These ranges assume quantized weights and moderate context lengths. Actual memory depends on the runtime, quantization method, batch size, KV-cache format, multimodal encoder and prompt length.
| Available memory | Practical starting points | Important caveat |
|---|---|---|
| 8GB VRAM | Gemma 4 E4B; four-bit Ministral 3 8B | Leave memory for the runtime and KV cache. Long context may cause an out-of-memory error. |
| 16GB VRAM or unified memory | Gemma 4 12B; four-bit Ministral 3 14B | Gemma’s official LiteRT test used roughly 7.8–8.1GB at only 2K context, so real workloads need more headroom. |
| 24GB VRAM | Four-bit Qwen3.6-27B with reduced context | A large context or multimodal workload can exceed 24GB even when the weights fit. |
| 48GB–80GB | Quantized Qwen3-Coder-Next; larger dense models | Its 80B total weights still need about 40GB before runtime overhead at four bits. |
| Multi-GPU server | GLM-5.2, DeepSeek V4, Kimi K2.7 Code, Nemotron 3 and Mistral Small 4 | Follow the exact official serving recipe rather than estimating from active parameters. |
Google’s Gemma 4 12B LiteRT benchmark reported a 6,235MB model file, approximately 8,064MB of GPU memory and 66.26 decoded tokens per second on an AMD Radeon AI Pro R9700. On an M4 Mac, it reported about 7,763MB of GPU memory and 29.56 decoded tokens per second. Both tests used a 2,048-token context, so they should not be extrapolated directly to long-context workloads.
How Should You Compare GGUF, AWQ, GPTQ and Other Quantizations?
Do not choose a quantization format by its name alone. Compare the exact model, bit depth and runtime on your intended hardware.
A useful test records:
- Task accuracy against the unquantized model.
- Tokens per second after the first request.
- Time to first token.
- Peak memory at your real context length.
- Tool-call and structured-output success.
- Quality on long reasoning tasks.
Lower-bit quantization may save enough memory to make a model runnable while reducing reasoning, coding or tool-use reliability. A slightly smaller model at a higher-quality quantization can outperform a larger model compressed too aggressively.
Also remember that active parameters measure computation, not total storage. An MoE model with three billion active parameters and 80 billion total parameters does not have the same memory requirements as a dense three-billion-parameter model. Meta’s Llama 4 documentation, for example, explicitly notes that all MoE parameters remain stored even though only a subset is activated during inference.
How Can You Choose the Right Model in Practice?
1. Define the Actual Task
Replace “we need an AI chatbot” with a measurable workload:
- Resolve issues in a Python repository.
- Answer questions from internal policies with citations.
- Extract JSON from invoices.
- Operate five approved tools.
- Translate support replies into four languages.
2. Establish Hard Constraints
Document:
- Available GPU and system memory.
- Required response time.
- Maximum cost per completed task.
- Privacy and data-residency requirements.
- Acceptable licences.
- Whether fine-tuning is required.
3. Build a Private Evaluation Set
Use 50–200 representative tasks. Include normal cases, difficult cases, ambiguous instructions and failure scenarios. Public benchmarks help discover candidates; private tests determine whether they fit your product.
4. Test Identical Settings
Use the same:
- System prompt.
- Tool definitions.
- Temperature and sampling settings.
- Maximum output length.
- Context documents.
- Agent framework and retry policy.
This is especially important for coding benchmarks. SWE-bench notes that different agent versions and tool-calling setups are not necessarily directly comparable.
5. Measure Completed-Task Cost
Tokens per second alone can be misleading. A faster model may generate more tokens, make more failed calls or require extra retries.
Track:
- Success rate.
- P50 and P95 latency.
- Input and output tokens.
- GPU time.
- Human correction time.
- Cost per successfully completed task.
6. Verify the Exact Licence
Record the model repository, checkpoint hash, licence version and third-party notices. Recheck them before a major release because different variants from the same model family can use different terms.
What Are the Main Limitations of This Ranking?
Rankings Change Quickly
This article reflects the available models and leaderboard results on July 2026. A new checkpoint or independent evaluation can change the order.
Benchmark Scores Are Not Always Comparable
A provider may report SWE-bench with a custom coding agent, while another uses a minimal shell loop. Reasoning models may also receive different token budgets.
A Long Context Is Not Perfect Memory
A model supporting one million tokens may still overlook evidence, confuse similar passages or waste computation. RAG, summarization and document routing remain useful.
Quantized Performance Can Differ from Published Results
Most model-card scores are obtained using official precision and serving configurations. A four-bit community checkpoint on a desktop may behave differently.
Open Weights Do Not Remove Safety Risks
Self-hosted models can hallucinate, reveal sensitive data, follow malicious retrieved instructions or misuse tools. Production systems still require access controls, validation, monitoring and sandboxing.
Conclusion
The best open-source LLM is the model that completes your workload reliably within your hardware, licence and cost constraints.
For 2026, start with GLM-5.2 for overall capability, DeepSeek V4 for reasoning, Kimi K2.7 Code for coding agents, Qwen3.6-27B for a high-end local system, Gemma 4 for laptops and edge devices, Nemotron 3 for reproducibility, and Mistral for multilingual enterprise applications.
Use public leaderboards to create a shortlist. Make the final decision with a private evaluation set, the exact quantized checkpoint and the same agent framework you will operate in production.
Reviewed by Uday Dikshit
Sr. Cloud DevOps Engineer, Real Time Data Services
Uday Dikshit is a Senior Cloud DevOps Engineer at Real Time Data Services with experience in Kubernetes, cloud platform engineering, OpenStack and HPC infrastructure. He has worked on Managed Kubernetes as a Service for public cloud customers and has administered OpenStack infrastructure across 50+ servers for AceCloud.
Frequently Asked Questions
No. GLM-5.2 is the best overall choice in this comparison, but a smaller model can be better once latency, privacy, hardware and operating cost are considered.
Kimi K2.7 Code is the strongest specialist for large coding agents. Qwen3-Coder-Next is more efficient for self-hosted coding servers, while Qwen3.6-27B is more realistic for a powerful local workstation.
Gemma 4 is the most practical family for local use because it covers edge, laptop and workstation sizes. Gemma 4 12B is a particularly useful starting point for systems with approximately 16GB of memory.
Start with Gemma 4 E2B or E4B using an optimized low-bit build. Keep the context window modest and measure peak memory in the intended application.
Many MIT and Apache 2.0 models allow commercial use, but the exact repository may contain additional notices, prohibited-use terms or third-party conditions. Modified or model-specific licences require separate review.
Not necessarily. A model may publish its weights without providing enough training-data information, code or other materials to meet the Open Source Initiative’s definition.
