Working Memo
Qwen comparison summary: Qwen leads on multilingual breadth and Chinese-language tasks; Llama has the largest fine-tuning community; Mistral punches above its weight on smaller sizes; DeepSeek competes on reasoning and code; Phi excels at efficiency given parameter count; Gemma benefits from Google infrastructure. No family dominates all dimensions — the right pick depends on your workload profile.
Why comparing open-weight families is harder than it looks
Open-weight LLM families release new generations on different cadences, making any comparison a snapshot that ages quickly — the relative standings on any benchmark can shift within a quarter.
A benchmark comparison between Qwen and Llama from six months ago may be meaningfully out of date today. Each family ships on its own cadence, and a new release from any of them can shift the leaderboard standings by several percentage points on standard benchmarks. The right disposition when reading any comparison — including this one — is to treat it as orientation, not verdict. The benchmark numbers inform which family to test first; your own evaluation on representative examples from your actual workload is what determines which model goes into production.
With that caveat in place, some consistent patterns have held across multiple benchmark snapshots and are worth understanding as structural features of each family.
Multilingual coverage
Qwen's multilingual training corpus is broader than most Western-origin open-weight families, giving it a structural advantage on non-English tasks that persists across generations.
Qwen was built from the start with strong multilingual coverage as a design goal — a reflection of Alibaba's primarily Asian user base and the Tongyi team's research priorities. The instruction-tuned Qwen models cover 29+ languages including English, Chinese, Spanish, French, German, Russian, Arabic, Japanese, Korean, and several Southeast Asian languages. On multilingual benchmarks, Qwen consistently outperforms Llama and Mistral on non-English evaluation sets, particularly for Asian languages.
Llama, Mistral, and Phi were primarily designed with English-first corpora and have improving but not equivalent multilingual coverage. DeepSeek shares some of Qwen's Chinese-language strength given its own origin. Gemma and Phi are generally English-strong models with lighter multilingual coverage. For teams building genuinely multilingual products, Qwen's multilingual depth is a real structural advantage that benchmark numbers tend to reflect.
English reasoning
On English reasoning benchmarks — MMLU, HellaSwag, ARC, and similar — Qwen is competitive with Llama and Mistral at equivalent parameter counts but does not consistently dominate. The 72B Qwen instruction models typically sit within a few points of the 70B Llama models on standard reasoning benchmarks, and the ordering can flip between generations. Phi stands out among smaller models for delivering surprisingly strong reasoning relative to its parameter count, which reflects Microsoft's aggressive synthetic data approach. DeepSeek has made notable progress on reasoning and math benchmarks. None of these families has a permanent lead on English reasoning that would make the choice obvious.
Code capability
Qwen-Coder variants are competitive with the best open-weight code models at equivalent sizes. On HumanEval, recent Qwen-Coder releases at 7B have scored comparably to the best 7B offerings from other families. At the 32B and 72B range, Qwen-Coder and DeepSeek-Coder compete most directly, with results varying by benchmark and language. Llama's code capability is solid through the Code Llama fine-tune line but has not consistently matched Qwen-Coder on multilingual code tasks. Mistral's code models are well-regarded for their size efficiency. For teams specifically building code-focused applications, running HumanEval and a domain-specific test suite against the top two or three candidate models is more reliable than relying solely on published numbers.
Vision-language capability
Qwen, Llama, and Gemma all have vision-language model variants. Qwen-VL has historically performed well on Chinese-document understanding and chart analysis tasks. Llama's vision variants perform well on English visual question answering. Gemma's vision integration benefits from Google's multimodal research pipeline. Phi and Mistral have had more limited vision-language releases. For multilingual document understanding — particularly if your documents include Chinese, Japanese, or Korean text — Qwen-VL is typically the strongest starting point among open-weight options.
| Family | Strength | Notable trade-off |
|---|---|---|
| Qwen | Multilingual breadth, Chinese-language depth, wide parameter sweep, Apache 2.0 on flagships | Smaller English fine-tuning community than Llama; vision tooling less mature than text tooling |
| Llama (Meta) | Largest open-weight fine-tuning community, strong English reasoning, well-documented architecture | Custom Meta license with scale restrictions; multilingual coverage trails Qwen |
| Mistral | Strong performance per parameter especially at smaller sizes; some Apache 2.0 releases | Narrower parameter sweep; fewer multilingual specialised releases |
| DeepSeek | Strong on reasoning and math; competitive on code; multilingual competence | Newer ecosystem; fewer third-party integration guides than Llama or Qwen |
| Phi (Microsoft) | Exceptional reasoning per parameter count; small model sizes suited to edge deployment | Limited large-scale releases; English-primary training reduces multilingual breadth |
| Gemma (Google) | Clean architecture, well-documented, strong English reasoning, regularly updated | Apache 2.0 with usage policy addendum; lighter multilingual coverage |
License comparison
License clarity is a real selection criterion for commercial deployments. Recent flagship Qwen text models ship under Apache 2.0 — the cleanest license in the set. Some Mistral releases also use Apache 2.0. Llama uses Meta's custom community license, which is permissive but includes a commercial-scale cap. Gemma uses Apache 2.0 with an additional usage policy. DeepSeek releases have varied. Phi uses MIT on some releases. For enterprise teams whose legal review process is sensitive to license nuance, Apache 2.0 availability from Qwen and Mistral is a practical simplifier. Background on AI procurement license evaluation is available from NIST's AI RMF guidance, and Stanford HAI publishes accessible primers on open-weight model licensing considerations.