Qwen-7B / Qwen-14B
Alibaba's first publicly-released open-weight foundation model family — Qwen-7B and Qwen-14B — released under a permissive commercial license. Outperformed Llama 2 on both Chinese (by a wide margin) and English benchmarks at comparable size. Established Alibaba as a credible open-LLM player and seeded the Qwen brand that would dominate Chinese open-source LLMs through 2026.
How are Intelligence, Speed & Cost bucketed?
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Qwen normalized the expectation that Chinese hyperscalers would publish frontier-comparable models openly — a pattern continued by DeepSeek, ByteDance Doubao (partial), and Tencent Hunyuan. The pattern is structurally different from US hyperscalers (where Meta is the only major open-weight publisher).
Core Capabilities
Context Window
Availability
Pricing Model
Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
What it feels like
- First-generation Qwen — historical; superseded by 2.5+.
- First open-source model to top OpenCompass leaderboard — beat Llama 3.1 405B with 1/5 the parameters
- 72B-Instruct: 74.2 on coding, 77 on math — outscored Claude 3.5 Sonnet (72.1) and GPT-4o (70.6) at release
- MMLU 85+, HumanEval 85+, MATH 80+ — frontier-tier across the board for an open-weights model
Best use cases
- Self-hosted production where Llama 3.1 405B is too big to serve
- Multilingual deployments (29 languages with strong coverage)
- Fine-tuning on private code or technical corpora
Tools to try
Not ideal for
- Frontier reasoning by mid-2025 — Qwen 3 / DeepSeek R1 / Claude 4 series have moved past it
- Vision tasks (use Qwen 2.5-VL) or audio (use Qwen 2.5-Audio)