The leading open models are no longer separated into “good” and “bad.” They are differentiated by what they are designed to do.
The open-model ecosystem has become crowded—and surprisingly capable.
DeepSeek, Qwen, Kimi, GLM, Gemma, Mistral, Llama, and gpt-oss can all produce impressive demonstrations. Their benchmark scores often appear close enough to make them interchangeable. In practice, however, they have very different strengths, hardware requirements, licenses, and failure modes.
Some excel at long-running coding tasks. Others are better at interpreting images, operating tools, supporting many languages, or running efficiently on local hardware. The “best” model depends less on a leaderboard position than on the system you want to build.
Here is how the leading open-weight model families compare as of July 2026.
A terminology note: most of these models are more accurately called open weight. Their trained weights are downloadable, but their complete training data and pipelines are not necessarily public. Licenses also vary, so “open” does not always mean unrestricted.
The field at a glance
| Model family | Particularly good at | Less suited to |
|---|---|---|
| GLM-5.2 | Long-horizon coding, tool use and text agents | Local deployment and visual tasks |
| DeepSeek-V4 | Reasoning, coding, long context and text-heavy agents | Multimodal applications and modest infrastructure |
| Kimi K2.5 | Visual agents, UI-to-code work and multimodal research | Simple self-hosting and small deployments |
| Qwen3.5 | Multilingual, multimodal, coding and general-purpose agents | Resource-constrained deployment at flagship scale |
| Gemma 4 | Local AI, edge devices and intelligence per parameter | The hardest autonomous, long-running work |
| Mistral Large 3 | Enterprise assistants, multilingual RAG and on-premises deployment | Organizations without data-center infrastructure |
| gpt-oss | Structured reasoning, tool use and private deployment | Vision, audio and frontier-level autonomous work |
| Llama 4 | Ecosystem support, fine-tuning and multimodal applications | Maximum capability and permissive licensing |
These are broad tendencies, not permanent rankings. Prompting, quantization, inference software, tool design, and task-specific fine-tuning can change the outcome substantially.
GLM-5.2: the open coding-agent specialist
GLM-5.2 is one of the strongest choices for complex, text-based agent work.
Its design emphasizes long-horizon tasks: navigating large codebases, using tools repeatedly, maintaining plans, and working with very large contexts. The model supports a one-million-token context window and multiple reasoning-effort settings. Its developers report particularly strong results on software-engineering and agent evaluations.
That makes GLM-5.2 attractive for:
Repository-scale coding agents
Large migrations and refactoring projects
Terminal and developer-tool automation
Long document or codebase analysis
Multi-step text-based workflows
Its principal drawback is operational scale. The published model contains approximately 753 billion parameters. Although its sparse architecture reduces the amount of computation used for each token, all those weights must still be stored and served. This is a data-center model, not something most developers will run comfortably on a laptop.
It is also text-only. If an agent needs to interpret screenshots, application interfaces, diagrams, or video, a native multimodal model will be a better foundation.
The weights are available under the MIT license, making GLM-5.2 unusually permissive for a model at this level. Z.ai’s GLM-5.2 model card presents it as a model for long-context coding and agentic work.
Best for: teams building serious coding agents with substantial infrastructure.
Not ideal for: local assistants, visual agents, or low-latency consumer applications.
DeepSeek-V4: the text-reasoning heavyweight
DeepSeek-V4 is another top-tier option for reasoning, code, and general text-based agents.
The family includes a Pro model with 1.6 trillion total parameters and 49 billion active per token, plus a smaller Flash variant with 285 billion total and 13 billion active parameters. It supports a one-million-token context window and offers non-thinking, high-reasoning, and maximum-reasoning modes.
DeepSeek is particularly compelling when the workload involves:
Mathematical and technical reasoning
Code generation and debugging
Long-context document processing
Research pipelines with external tools
High-volume text inference through a hosted provider
Its MIT license also gives organizations broad freedom to deploy and adapt it. DeepSeek’s documentation confirms that both the published weights and code are released under MIT.
The main limitation is that DeepSeek-V4 is a text model. Images, audio, and video require separate models or preprocessing systems. The Pro version is also enormous. Sparse activation makes inference more efficient than the headline parameter count suggests, but it does not make the model easy to self-host.
DeepSeek is therefore strongest as the reasoning engine inside a larger system, especially when another component handles perception.
Best for: advanced reasoning, coding and text-first agents.
Not ideal for: visual applications or organizations seeking a simple single-server deployment.
Kimi K2.5: the ambitious multimodal agent
Kimi K2.5 stands out for combining multimodal understanding with agentic behavior.
It accepts text, images, and video. It was designed for tasks such as generating code from interface designs, processing visual data with tools, and decomposing difficult work into parallel subtasks. Moonshot describes an “agent swarm” mode in which the system dynamically creates specialized workers for different parts of a task.
This makes Kimi a strong candidate for:
Turning screenshots or designs into code
Visual research and document analysis
Agents that work across images and text
Video understanding
Complex research involving parallel subproblems
Kimi K2.5 contains roughly one trillion parameters, with 32 billion activated for each token. That architecture provides strong capability without dense-model inference costs, but self-hosting still requires serious hardware and engineering.
Its license is also worth reading carefully. Kimi uses a modified MIT license that requires very large commercial products to display the model’s name. That will not affect most users, but it means the license is not identical to standard MIT.
Some features can differ between the downloadable model and Moonshot’s official service. Its model documentation, for example, describes video support through the official API as experimental. Moonshot’s Kimi K2.5 repository contains the architecture, deployment guidance, and license details.
Best for: multimodal agents, visual coding, and complex research workflows.
Not ideal for: lightweight self-hosting or teams that want a completely standard permissive license.
Qwen3.5: the strongest all-rounder
If one open model family must cover the widest range of requirements, Qwen3.5 may be the safest place to begin.
The flagship Qwen3.5-397B-A17B is a native vision-language model with 397 billion total parameters and 17 billion active for each forward pass. It supports reasoning, coding, tools, images, video, interface interaction, and more than 200 languages and dialects.
Qwen is particularly strong for:
Multilingual assistants
Visual question answering
Coding and frontend generation
Computer- and mobile-use agents
International products
Applications that combine documents, screenshots and tools
The family’s breadth is a major advantage. Developers can use smaller Qwen variants for affordable deployments while retaining a broadly compatible architecture and tooling ecosystem. The flagship weights use the Apache 2.0 license. Qwen’s official release provides extensive results across language, reasoning, coding, agents, and multimodal evaluations.
Its weakness is less about capability than clarity. Qwen offers many sizes, versions, modes, and hosted variants. The model available through Alibaba’s hosted service may have different context limits, built-in tools, or behavior from the downloadable weights. Teams must evaluate the exact artifact they intend to deploy.
The flagship model is efficient relative to its capability, but “17 billion active parameters” should not be confused with a 17-billion-parameter download. Its full weights still require substantial memory.
Best for: teams seeking a flexible, multilingual, multimodal foundation.
Not ideal for: buyers who want one simple model SKU or a flagship model that runs on ordinary consumer hardware.
Gemma 4: the intelligence-per-parameter leader
Gemma 4 is the most compelling family when deployment efficiency matters as much as raw capability.
Google offers models ranging from very small E2B and E4B variants to 26-billion- and 31-billion-parameter models. The smallest versions target mobile and edge environments, while the larger releases can run on workstations and consumer-oriented GPU systems.
Gemma 4 supports text and images, more than 140 languages, and contexts up to 256,000 tokens. Google reports unusually strong reasoning, coding, and tool-use results for models of its size. The family is released under Apache 2.0. Google DeepMind’s Gemma 4 model card describes deployments ranging from phones to servers.
Gemma is a good fit for:
Private local assistants
Laptop and workstation applications
Edge and mobile AI
Multimodal document processing
Cost-sensitive production systems
Fine-tuned models for focused business tasks
The tradeoff is absolute headroom. A well-designed 31-billion-parameter model can outperform much larger systems on selected evaluations, but it still has less capacity than the largest open models. The difference becomes more apparent during unfamiliar, ambiguous, or extremely long-running tasks.
Gemma can be an excellent agent component. It is less likely to be the model you choose for a coding agent expected to work autonomously in a difficult repository for many hours.
Best for: local, private and efficient AI with strong general capability.
Not ideal for: the most demanding autonomous engineering or research tasks.
Mistral Large 3: the enterprise-friendly European option
Mistral Large 3 is designed around enterprise deployment rather than winning every reasoning benchmark.
It is a multimodal mixture-of-experts model with approximately 675 billion total parameters and around 41 billion active parameters. It supports a 256,000-token context window, native function calling, structured output, images, and major European and Asian languages.
Its strengths include:
Multilingual enterprise assistants
Retrieval-augmented generation
Long-document analysis
Structured tool use
On-premises and sovereign deployments
Applications requiring an Apache 2.0 license
Mistral’s European origin can also matter for organizations pursuing regional AI infrastructure or trying to diversify away from American and Chinese model providers.
The model is mature and production-oriented, but it is no longer the clear top open model for advanced reasoning or long-horizon coding. Newer releases from GLM, DeepSeek, Qwen, and Kimi have pushed further in those areas.
Mistral Large 3 is also a large data-center model. Even its compressed NVFP4 release occupies hundreds of gigabytes. Mistral’s model card recommends multi-accelerator nodes for on-premises deployment.
Best for: multilingual enterprise systems, RAG, and controlled on-premises deployment.
Not ideal for: small teams without inference infrastructure or those seeking the current maximum in coding-agent performance.
gpt-oss: practical reasoning with familiar behavior
OpenAI’s gpt-oss family remains useful because it combines reasoning and tool-use behavior with relatively manageable deployment requirements.
The larger gpt-oss-120b model activates approximately 5.1 billion parameters per token and fits within 80 GB of memory. The 20-billion-parameter version can operate within about 16 GB. Both support structured output, function calling, configurable reasoning effort, and a 128,000-token context window.
They are particularly suitable for:
Private reasoning assistants
Structured data generation
Function calling and tool routing
STEM and coding workloads
Local experimentation
Organizations familiar with OpenAI-style agent patterns
Both models use Apache 2.0. Their inference requirements are predictable, and they have broad support across tools such as vLLM, Ollama, llama.cpp, and LM Studio. OpenAI’s gpt-oss release provides the architecture and memory requirements.
The largest limitation is modality. The models are text-only and were trained primarily around English, STEM, coding, and general knowledge. They also belong to an earlier generation than the newest 2026 open models. Their capability ceiling is consequently lower on difficult, long-horizon tasks.
Best for: structured reasoning and tool use on controlled infrastructure.
Not ideal for: multimodal products or applications requiring the strongest available open model.
Llama 4: the ecosystem choice
Llama’s greatest advantage is no longer that it leads every benchmark. It is that almost everything supports it.
Inference frameworks, cloud providers, fine-tuning systems, quantization tools, safety models, and hardware platforms have spent years optimizing for the Llama architecture. That makes Llama 4 Scout and Maverick comparatively easy to integrate despite their size.
Llama 4 is useful for:
Organizations with existing Llama infrastructure
Fine-tuning and domain adaptation
Multimodal assistants
Very-long-context experiments
Products that need broad vendor support
Research benefiting from a large community
Scout and Maverick are multimodal mixture-of-experts models with 17 billion active parameters. Meta says quantized Scout can fit on a single H100, while Maverick requires a larger host. Meta’s Llama 4 announcement describes their architecture and deployment profiles.
The disadvantages are capability and licensing. Llama 4 is no longer at the front of the open-model field, particularly for reasoning and coding agents. Its custom community license is also less permissive than Apache 2.0 or MIT.
Llama remains a rational choice when operational compatibility matters more than having the newest model.
Best for: mature tooling, existing Llama deployments, and community support.
Not ideal for: teams selecting purely for maximum performance or licensing simplicity.
So which model should you choose?
The simplest answer is to start from the workload:
For a high-end coding agent, evaluate GLM-5.2 and > DeepSeek-V4.
For a multimodal agent that must interpret interfaces or video, > evaluate Kimi K2.5 and Qwen3.5.
For a multilingual general-purpose product, start with Qwen3.5.
For a model that must run locally, look at Gemma 4 or > gpt-oss-20b.
For multilingual enterprise RAG with permissive licensing, consider > Mistral Large 3.
For the broadest existing integration ecosystem, Llama 4 remains > relevant.
For high-volume private reasoning on one data-center GPU, > gpt-oss-120b remains a practical option.
The final decision should come from testing complete workflows, not isolated prompts. Measure whether the model finishes the task, follows permissions, uses tools correctly, recovers from failures, and produces work that survives human review.
Open models have become powerful enough that the question is no longer, “Can we avoid using a frontier API?”
The better question is, “Which combination of capability, control, cost, and infrastructure fits the work we actually need done?”
