# Ow · Open Weights — element 2 of 58

> Your model, your terms. Turns model dependence into model ownership.

- **Group:** 1 · Intelligence
- **Necessity:** Optional
- **Price band:** Free
- **Maturity:** Emerging
- **Edition:** v2026.Q3 · verified 2026-09-13

## Leading tools (v2026.Q3)

- **DeepSeek (V4 Pro / Flash)** — frontier value, clean mit
- **Kimi (K3 / K2.6)** — strongest open brain
- **Qwen (3.6 / 3.8-Max)** — every size, apache 2.0
- **GLM (5.2)** — self-hostable coding frontier
- **Mistral (Mistral 3 family)** — eu-friendly, apache flagship

## Our take

You don't need this until cost, privacy, or latency says you do. When it does, it's the difference between renting and owning.

## Combines with

Fm, Rt


## The top 5 — deep dossier (verified 2026-09-13)

DeepSeek, for most startups — V4 (Apr 2026) pairs a clean MIT license and 1M-token context with the most aggressive price-performance in AI ($0.04 blended per agentic task vs Kimi K3's $0.94), and it's hosted everywhere. Kimi K3 is the pick when you want the strongest open-weight brain, period (Artificial Analysis 57, #3 overall behind only closed frontier models); Qwen when you want one Apache-2.0 family spanning 0.6B to 2.4T and the biggest fine-tuning ecosystem; GLM-5.2 when you want a frontier-class coding agent you can actually self-host on 8 GPUs; Mistral when EU jurisdiction and procurement decide. Llama — the 2023–2025 default — is no longer a top pick: Meta pivoted to the closed Muse line and Llama 4 is its terminal open offering.

1. **DeepSeek (V4 Pro / Flash)** (DeepSeek (High-Flyer)) — Weights free (MIT) · API: Flash $0.14/M in, $0.28/M out · Pro $0.435/$0.87 · cache hits from $0.0028 · 2x at Beijing peak hours. Best for: Frontier-class capability at commodity prices — the default open family for high-volume agents, with weights you can walk away with. Why: V4 (Apr 24, 2026) reset the category: 1.6T/49B-active Pro and 284B/13B Flash, both 1M-token context, 80.6% SWE-bench Verified at release, under a no-strings MIT license with weights on Hugging Face day one. Artificial Analysis puts blended cost per agentic task at $0.04 — 24x cheaper than Kimi K3, 8x cheaper than GLM-5.2 — and one dollar buys ~1.15M output tokens from Pro. Watch: Raw intelligence trails Kimi K3 and GLM-5.2 (V4 Pro scores 44 on AA's index vs K3's 57); text-only, no vision. Hosted API is China-jurisdiction with new 2x peak-hour pricing — regulated teams should self-host or use US hosts. 1.6T params makes DIY serving a multi-node project. [https://www.deepseek.com](https://www.deepseek.com)
2. **Kimi (K3 / K2.6)** (Moonshot AI) — Weights free (Modified MIT) · K3 API $3/M in, $15/M out, cache $0.30 · K2.6 $0.95/$4.00. Best for: The strongest open-weight model available — peak capability via hosts (Together, Fireworks, OpenRouter), not your own racks. Why: K3 (announced Jul 16, weights Jul 26, 2026) is a 2.8T-parameter MoE with vision and 1M context that scores 57 on the Artificial Analysis Intelligence Index — the top open model, #3 overall behind only the closed frontier — with 1.13M HF downloads in its first ten days. K2.6 (Apr 2026) remains the practical tier: 1T/32B-active, 58.6% SWE-bench Pro (tied GPT-5.5), 300-parallel-sub-agent swarms. Watch: Self-hosting is theater for most: 1.39TB of INT4 weights, Moonshot recommends 64+ accelerators — ~96.5% of a DGX B200's memory before cache. API is the priciest of the Chinese trio. Modified MIT adds an attribution clause above 100M MAU / $20M-month revenue. [https://www.kimi.com](https://www.kimi.com)
3. **Qwen (3.6 / 3.8-Max)** (Alibaba) — Weights free (Apache 2.0 for most) · hosted 3.8-Max $2/M in, $6/M out, cached $0.25 · small models pennies via any host. Best for: One family for everything — edge to 2.4T frontier — with the largest fine-tune/derivative ecosystem in open AI. Why: The most prolific and most-downloaded open family: 11+ flagship releases in 18 months, 942M cumulative downloads vs Llama's 476M, and 36.3% of all Hugging Face text-generation downloads (ATOM/Jul 2026). Qwen3.8-Max (Aug 3, 2026) is a 2.4T MoE scoring 86.6 on Terminal-Bench 2.1 — ahead of Claude Opus 4.8 — and the 3.5/3.6 lines span 0.6B dense to 235B MoE under Apache 2.0 with no usage gates. Watch: Flagship weights trail the hosted launch — 3.8-Max weights were 'next week' at announcement and the very best Plus/Max variants have historically stayed API-only. Coding trails the leaders (67.7 SWE-bench Pro vs Claude Fable 5's 80.0). Alibaba hasn't disclosed 3.8-Max active-parameter count. [https://qwen.ai](https://qwen.ai)
4. **GLM (5.2)** (Z.ai (Zhipu)) — Weights free (MIT) · API $1.40/M in, $4.40/M out, cache $0.26 · free tiers (GLM-4.7-Flash) · budget FlashX $0.07/M in. Best for: The self-hostable frontier coding agent — 744B fits on ~8x H200 at FP8, and it's the fastest of the big three at ~168 tok/s. Why: GLM-5.2 (Jun 13, 2026) is MIT-licensed with weights live on HF (zai-org), scores 51 on AA's index with 1M context, and leads open models on several coding/agentic harnesses at a sixth of closed-frontier API cost. Unlike K3 and DeepSeek V4 Pro, it's the one trillion-class model a well-funded startup can realistically run in-house. Watch: Trails K3 on raw capability (matched-harness DeepSWE: 46.2 vs K3's 67.5). Text-only. Hosted API carries China data-residency risk flagged by US coverage; GLM-5.5 is expected around Aug 2026, so buying decisions may be obsolete within a quarter. [https://z.ai](https://z.ai)
5. **Mistral (Mistral 3 family)** (Mistral AI) — Weights free (Apache 2.0: Large 3, Ministral 3, Small 4, Nemo) · API: Small 4 $0.15/$0.60 · Large 3 ~$2/$6 · Medium 3.5 (closed) $1.50/$7.50. Best for: EU-jurisdiction open weights with a real company behind them — the procurement-safe answer when Chinese weights are a non-starter. Why: Mistral 3 (Dec 2, 2025) put a genuine flagship under Apache 2.0: Large 3 is a 675B/41B-active multimodal MoE (#2 OSS non-reasoning on LMArena at launch) plus Ministral 3 edge models at 3B/8B/14B, all on HF, Bedrock and beyond. It's the only Western vendor shipping open frontier-scale weights on a committed cadence — with a new open frontier model in early access as of July 2026. Watch: The capability gap is real: its best open scores sit well below the Chinese trio (Medium 3.5, its strongest, is closed and scores 30 on AA vs K3's 57). Its true frontier (Medium 3.5, OCR, Voxtral tiers) stays API-only — the open/closed line moves release by release. [https://mistral.ai](https://mistral.ai)

### How to choose
- If You're burning real money on closed-model API calls for high-volume or agentic workloads → DeepSeek V4 — $0.04 blended per task and an 8x average open-vs-closed inference gap (MIT Sloan: $0.23 vs $1.86/M tokens) is the whole argument for this element.
- If You want maximum open capability and will consume it via hosts anyway → Kimi K3 on Together, Fireworks or OpenRouter — AA 57, #3 overall — and skip the fantasy of racking 64 accelerators yourself.
- If Privacy, compliance, or data residency forces weights inside your walls → GLM-5.2 (frontier coding agent on ~8x H200), or a Qwen 3.6 / Gemma 4 size that matches your hardware — not K3 or V4 Pro, which are multi-node projects.
- If EU jurisdiction, procurement, or 'no Chinese weights' policy constrains you → Mistral's Apache-2.0 stack (Large 3 down to Ministral 3B) — accepting a real capability gap vs the Chinese trio — with gpt-oss/Gemma 4 as US-origin small options.
- If You plan to fine-tune, distill, or ship models inside your product → Prefer clean Apache 2.0/MIT (Qwen, DeepSeek, GLM, Mistral, Gemma 4) over conditioned licenses — Llama's 700M-MAU-and-EU-restricted community license and Kimi's attribution clause are fine until the day they aren't.

### The field (25 more)

Llama 4 (Scout / Maverick) (fading), Gemma 4, gpt-oss-120b / 20b, Kimi K2.6, Inkling, MiniMax M3, Nemotron 3 Ultra 550B, MiMo-V2.5, Qwen3-Coder-Next, OLMo, Hunyuan, ERNIE, Command A, Granite, Falcon (fading), Grok (open releases) (fading), Llama 4 Behemoth (dead), DBRX (fading), Jamba (fading), Together AI, Fireworks AI, Groq, DeepInfra / Baseten / Modal, Hugging Face, AWS Bedrock / Azure Foundry / Vertex

Full dossier data: https://elems.ai/e/ow.json

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Source: [elems.ai](https://elems.ai/e/ow.html) — the periodic table of the AI-led startup. Data: https://elems.ai/elements.json (CC BY 4.0, cite elems.ai).