Comparison
by The AI Breakout July 25, 2026
Open-Weight Models 2026: Qwen vs DeepSeek vs Llama
How the 2026 open-weight leaders rank on public leaderboards, what the community reports, and which one fits your hardware budget.
External evidence
The 2026 open-weight tier is decided by public leaderboards — LMArena, Artificial Analysis, OpenLLM — not by private labs. Here is what the public data says.
What the leaderboards say
Across LMArena and Artificial Analysis, the 2026 open-weight field separates into two tiers:
- Elite tier (DeepSeek-V3.2, Qwen3 235B-A22B) — competitive with top closed models on reasoning, code, and multilingual; the gap to frontier closed models has narrowed to a few percentage points.
- Workhorse tier (Llama 4 Maverick) — firmly mid-pack on scores but unbeatable on per-token cost and ecosystem integration (Ollama, Open WebUI, cloud providers).
Hardware reality (community-reported)
| Model | Approx. 4-bit VRAM | Recommended setup |
|---|---|---|
| DeepSeek-V3.2 | ~105GB | 2-4x A100/H100 box or cloud |
| Qwen3 235B-A22B | ~110GB | 2-4x A100/H100 box or cloud |
| Llama 4 Maverick | ~80GB | 1-2x upgrade workstation |
How to decide
- Watch the leaderboards, not launch tweets — check LMArena and Artificial Analysis for the latest scores before choosing.
- Hardware decides everything — a 70B-class model you can run beats a 235B you can’t.
- Eco rule — if your stack is a laptop rather than a cluster, start from the local-LLM guide and work up.
Numbers change monthly; the sources above are the ones to follow.