Best Open-Source AI Models (2026): 5 That Rival Frontier, 5 More to Watch
The open-model field moves fast enough that a static "top 10" list is wrong within a quarter. So this is not a frozen ranking with a single winner. It is a maintained comparison framework. The center of the page is a sortable table that maps ten open models to the exact license class, context window, serving footprint, and the one frontier strength each one challenges. You sort it by what you care about, then read the profiles for the trade-offs. We refresh it on a schedule and on every major open-model release.
The short version: DeepSeek estimates its open weights now trail the closed frontier (the most capable closed models, like GPT-5.5, Claude Opus 4.7, and Gemini 3) by only a few months on many benchmarks. The global top 5 for raw intelligence is still proprietary, but the gap is the smallest it has been. The catch is the word "open." Some of these models are true open source (MIT, Apache 2.0). Others are open-weight only, with license terms that restrict who can use them and how. This page keeps those two things separate, because for a business that distinction is the whole decision.
How to Read This Open-Source Model Comparison
Before the table makes sense, you need the word "open" pinned down. In 2026 it covers three different legal realities, and treating them as one is the most expensive mistake a procurement team makes. A model can ship its weights publicly and still forbid the use you had in mind. If you are still weighing whether open weights belong in your stack at all, our breakdown of why teams choose open-source AI models lays out the strategic case before you get to license mechanics.
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- OSI open source (Apache 2.0, MIT): an Open Source Initiative approved license. You can use it commercially, modify it, and redistribute it. DeepSeek V4 and Phi-4 use MIT. Mistral Large 3, Gemma 4, and the open Qwen line use Apache 2.0. This is the lowest-friction class for a business.
- Open weight (source-available): the weights download freely, but the license restricts use. Examples: the Llama 4 Community License (with field-of-use limits and, for individuals in the EU, a use restriction) and the Gemma Terms of Use. Usable, but read the acceptable-use policy before you build on it.
- Source-available, non-free: weights are visible, but the license blocks commercial or production deployment without a separate agreement. Mistral's Codestral 22B (Non-Production License) and the older Mistral Research License sit here.
The Open Source Initiative has publicly disputed Meta's use of the term "open source" for Llama, citing the field-of-use restrictions and the lack of training-data transparency. That dispute is the reason this page never calls a model "open source" unless its license actually qualifies. When in doubt, the table says exactly which license governs each model, and you should confirm it on the model card before you ship.
The Comparison Table (License, Context, Serving, Parity)
Click any column header to sort. The "Frontier strength it rivals" column is the parity dimension: it names where each open model is genuinely competitive, not where it wins overall. Cells marked Verify on model card are models we have not yet verified against primary sources. We do not assign them versions or benchmarks until we have. See the note under the table.
| Model | Maker | License | Context | Serving needs | Governance posture | Frontier strength it rivals |
|---|---|---|---|---|---|---|
| Llama 4 (Maverick / Scout) | Meta | Open-weight (Llama 4 Community License + AUP). Not OSI open source. | 1M (Maverick), 10M (Scout) | Single NVIDIA H100 DGX host (Maverick), or distributed | Field-of-use limits; EU-individual restriction; no training-data transparency | Native multimodality plus ultra-long context |
| DeepSeek V4 (Pro / Flash) | DeepSeek | MIT (true open-source weights) | 1M | Heavy GPU to self-host Pro (1.6T MoE); Flash lighter; or low-cost API | MIT permissive. Self-host to avoid the China-hosted API data-residency question | Reasoning and ultra-cheap long context |
| Qwen (open line: Qwen3-Coder, Qwen3.6-35B-A3B) | Alibaba | Apache 2.0 (open line). Note: the Qwen3.7-Max flagship is proprietary, API only. | Up to 1M; open variants near 256K | Qwen3.6-35B-A3B runs on a single MacBook; larger open models need a GPU server | Apache 2.0 on the open line; closed only at the very top (Max) | Agentic coding and tool orchestration |
| Mistral Large 3 (2512) | Mistral AI | Apache 2.0 | 256K (262K some configs) | Single 8x GPU node; Ministral 3 for edge | Apache 2.0 permissive; French maker, European data residency | Multilingual plus code |
| Gemma 4 (31B) / Gemma 3 27B | Google DeepMind | Gemma 4: Apache 2.0. Gemma 3: source-available (Gemma Terms of Use) | Gemma 4 large 256K; Gemma 3 27B 128K | Single GPU or TPU | Gemma 4 moved to permissive Apache 2.0; Gemma 3 carries an AUP | Multimodal on a single accelerator |
| Phi-4 Reasoning | Microsoft | MIT | Verify on model card | On-device, laptop, mobile (14B) | MIT permissive | Reasoning that runs locally |
| Kimi K2 | Moonshot AI | Verify on model card | Verify on model card | Verify on model card | Verify on model card | Verify on model card |
| GLM | Z.ai (Zhipu) | Verify on model card | Verify on model card | Verify on model card | Verify on model card | Verify on model card |
| Command (A / R) | Cohere | Verify on model card | Verify on model card | Verify on model card | Verify on model card | Verify on model card |
| Nemotron | NVIDIA | Verify on model card | Verify on model card | Verify on model card | Verify on model card | Verify on model card |
Models verified 2026-06-30. Benchmarks and context ceilings are vendor-reported or community-leaderboard figures and are directional, not independently audited.
Llama, DeepSeek, Qwen, Mistral, Gemma
These five are the verified core of the table. Each profile states the current flagship, the real license, and the one thing it does well enough to make a frontier model optional for that job. If you are weighing these open weights against hosted commercial options, our ranking of the best AI tools covers the proprietary side of that decision.
Meta Llama 4
Llama 4 is built as a Mixture-of-Experts family (an MoE design activates only a fraction of its parameters per token, which is what keeps serving cheap) with native multimodality. The product workhorse, Llama 4 Maverick, holds 400 billion total parameters but activates only 17 billion per token, which is how Meta runs it on a single NVIDIA H100 DGX host. Its sibling, Llama 4 Scout, carries a 10 million token context window, the largest on this page. The early-fusion design trains text, image, and video together from the start rather than bolting a vision encoder on later.
On licensing, be precise: Llama 4 ships under the Llama 4 Community License, an open-weight, source-available license, not OSI open source. The acceptable-use policy restricts certain fields, and later Llama licenses bar use by individuals in the EU. If your legal team needs a clean permissive license, Llama is the one to check carefully. For raw capability per dollar at scale, it is hard to beat. See the Meta Llama hub for the deployment details.
DeepSeek V4
DeepSeek V4 is the value leader, and it is genuine MIT-licensed open source. The family splits into V4-Pro (1.6 trillion total, 49 billion active) and the lighter V4-Flash (284 billion total, 13 billion active). Both run a 1 million token context natively. The headline is efficiency: a hybrid attention design (Compressed Sparse Attention plus Heavily Compressed Attention) lets V4-Pro run a 1M-token context using only 27 percent of the per-token compute and 10 percent of the memory cache of the previous generation.
Two honesty notes. First, the hosted DeepSeek API runs from China, so regulated workloads should self-host the MIT weights to keep data in their own environment. Second, in February 2026 Anthropic publicly accused DeepSeek of using fraudulent accounts to generate Claude conversations for training. That is a verifiable event worth weighing, not a reason to dismiss the weights. The DeepSeek hub covers the pricing tiers.
Alibaba Qwen
Qwen is where licensing honesty matters most. The very top model, Qwen3.7-Max, is proprietary and API only, so it does not belong on an open-source list. But Alibaba ships a strong open-weight line under Apache 2.0: Qwen3-Coder (480B-A35B) and Qwen3.6-35B-A3B, which scores 73.4 on SWE-Bench Verified (a coding benchmark; higher is better) while running on a single MacBook. For agentic coding, that open line is the one that rivals frontier coding assistants. The Qwen hub has the full lineup.
Mistral Large 3
Mistral Large 3 (the 2512 release) is a sparse MoE with 41 billion active and 675 billion total parameters, a 256K context, and a true Apache 2.0 license. That license is a real shift: the prior Mistral Large 2 shipped under the restrictive Mistral Research License. Large 3 leads on multilingual conversation outside English and Chinese and posts around 92 percent on the HumanEval coding test. It runs on a single 8x GPU node, and the maker being based in France makes European data residency straightforward. The Mistral hub lists which models are Apache 2.0 and which are proprietary.
Google Gemma
Gemma is the "fits on one accelerator" champion. Gemma 3 27B is natively multimodal and runs on a single GPU or TPU, and on the Chatbot Arena it scored an Elo of 1338, ahead of much larger models. The newest release, Gemma 4, moved to a permissive Apache 2.0 license and adds native audio on its edge variants, a meaningful improvement over Gemma 3's source-available Gemma Terms of Use. If you want frontier-style multimodal capability without a GPU cluster, Gemma is the default. The Gemma hub has the per-size breakdown.
Kimi K2, GLM, Command-R, Nemotron, Phi
These five are the fast-rising challengers. Only one, Phi, is verified against a primary source today, so it is the only one with full table data. The other four are held to the same rule as everything else on this page: we do not publish a version, license, or benchmark we have not verified.
Microsoft Phi-4 Reasoning (verified)
Phi-4 Reasoning is the on-device reasoning option. It is MIT licensed, around 14 billion parameters, and built for edge and mobile deployment, including local inference on a personal laptop. It is the model to reach for when the constraint is "no datacenter, no cloud round-trip," and you still want real step-by-step reasoning. Its exact context window is not confirmed in the primary sources we cite, so the table marks that single cell "Verify on model card" rather than guessing.
Kimi K2 (Moonshot AI), GLM (Z.ai), Command (Cohere), and Nemotron (NVIDIA) are listed here pending model-card verification. We have not assigned them versions, licenses, context windows, or benchmarks because we have not yet verified these against primary sources. They join the table with full data on the next scheduled refresh. This is the maintenance contract working as intended: a placeholder is honest, a fabricated spec is not.
Which Open Model Challenges Which Frontier Strength
"Rivals frontier" does not mean "beats GPT-5.5 at everything." It means that for one specific capability, an open model is now close enough that paying frontier prices is a choice, not a requirement. Here is the honest mapping.
Million-token windows used to be a frontier-only luxury. DeepSeek V4 delivers 1M tokens at a fraction of the cost, and Llama 4 Scout reaches 10M. Open wins on price per long-context token.
DeepSeek V4 / Llama 4 ScoutThe open Qwen line (Qwen3-Coder, Qwen3.6-35B-A3B at 73.4 on SWE-Bench Verified) competes directly with frontier coding assistants on real software-engineering tasks.
Qwen3-Coder (Apache 2.0)Mistral Large 3 leads on conversation outside English and Chinese, with European data residency built in by virtue of where it is made.
Mistral Large 3 (Apache 2.0)Gemma 3 27B brings native image (vision) understanding to a single GPU or TPU, and Gemma 4 adds native audio, the capability frontier models reserve for their flagship tiers.
Gemma 3 27B / Gemma 4And the part most "open beats closed" articles skip: frontier still leads where it counts most. The same Artificial Analysis index that places Qwen3.7-Max in the top 5 also shows it losing on raw chat quality, and that top-5 model is proprietary, not open. For the absolute ceiling of reasoning and multimodal capability, the named frontier models still hold it.
If you need the highest possible reasoning or multimodal capability at any cost, a zero-ops managed API with an enterprise SLA, or you have no GPU and no MLOps capacity to self-host, a managed frontier model (GPT-5.5, Claude Opus 4.7, Gemini 3) is the correct choice. Open weights save money and give control, but they hand you the operational burden. Do not pick open for a workload where you cannot staff the operations.
How to Pick One for Your Use Case
Pick by constraint, not by hype. Answer two questions, your primary need and your license requirement, and the selector points you at a verified starting model. Then confirm the current spec on the model card before you build. Once you have a shortlist, our open-source migration checklist walks through moving a workload off a proprietary API without surprises.
A verified starting point, not a verdict. Every recommendation traces to a model on the table above.
The Pro wizard scores models across four weighted dimensions (use case, data sensitivity, infrastructure maturity, and budget) and returns a ranked shortlist with a self-hosting cost estimate and a migration checklist. Free members get the lead-capture preview today; the full scoring tool ships with the membership tier.
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Frequently Asked Questions
Changelog and refresh policy
This comparison is a living document. It is re-verified on a quarterly cadence and on any major open-model release (a new flagship from Llama, DeepSeek, Qwen, Mistral, Gemma, Kimi, GLM, and similar makers), whichever comes first.
| Date | Change |
|---|---|
| 2026-06-30 | Initial table. Llama, DeepSeek, Qwen, Mistral, Gemma verified against primary sources. Kimi K2, GLM, Command, and Nemotron listed pending verification, with no fabricated specs. |
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Llama is a trademark of Meta Platforms. DeepSeek, Qwen, Tongyi Qianwen, and Alibaba Cloud are trademarks of their respective owners. Mistral is a trademark of Mistral AI. Gemma and Gemini are trademarks of Google. Phi is a trademark of Microsoft. Kimi, GLM, Command, and Nemotron are trademarks of Moonshot AI, Z.ai, Cohere, and NVIDIA respectively. Claude is a trademark of Anthropic. GPT is a trademark of OpenAI. All product names and logos are the property of their respective owners. Tech Jacks Solutions has no commercial relationship with the makers listed. This article is editorially independent.