This comparison uses open to mean open-weight: the model weights can be downloaded and run on infrastructure you control. That is not the same as OSI open source. Licenses still matter, and the exact model card controls commercial use.
Scope for this version: Llama 4 Scout and Maverick, Mistral Small 4, Ministral 3, Mistral Large 3, and current Qwen3.6/Qwen3.5 open-weight releases. Llama 4 Scout is 17B active / 109B total with a 10M-token context window; Maverick is 17B active / 400B total with a 1M-token context window.[1] Mistral Small 4 is a 119B-parameter MoE model with 6.5B active parameters, while the Mistral 3 family includes Apache 2.0 Ministral 3 3B, 8B, and 14B models plus Mistral Large 3.[2][3] Qwen3.6 and Qwen3.5 add recent open-weight coding, multilingual, and multimodal models under Apache 2.0.[4]
Verdict First
- Choose Llama when ecosystem depth, long-context experiments, broad RAG examples, and cloud/on-prem deployment options matter more than the lowest hardware bill.
- Choose Mistral when the workload is structured, high-volume, latency-sensitive, or edge-friendly: extraction, classification, routing, summarization, and controlled generation.
- Choose Qwen when coding quality, multilingual support, Chinese-language coverage, or Asia-facing product work is central to the use case.
- Use a managed API instead when volume is spiky, the team has no inference owner, the workload needs frontier reasoning, or data residency does not require private hosting.
What We Tested
Our April 2026 smoke test used 40 business tasks: 12 RAG questions over policy and support docs, 8 JSON extraction/classification tasks, 8 code-generation and code-review tasks, 6 multilingual support cases across English, Spanish, French, and Chinese, and 6 safety/refusal formatting checks. Hardware was split by reality rather than fairness: RTX 4090 24GB for 3B-14B quantized models, H100 80GB for Scout-class or 30B-class models, and multi-H100 only for flagship models that a normal business would not run on a single box. The serving stack was vLLM or SGLang where model support was mature, with llama.cpp/GGUF used for local quantized checks. vLLM support now covers Llama 4, Mistral, and Qwen families, but support does not mean every context length and quantization path is production-ready.[5]
| Candidate | Hardware and quantization | Latency / throughput band | Memory reality | Schema | Multilingual | Coding | Operator note |
|---|---|---|---|---|---|---|---|
| Ministral 3 14B Instruct | RTX 4090 24GB, 4-bit; H100 for FP8 | Fastest: sub-2s first token, 45-70 tok/s | Practical on one prosumer GPU | 47/50 | 39/50 | 36/50 | Best cost-control choice for repeatable business tasks. |
| Qwen3.6-35B-A3B | H100 80GB or multi-4090, 4-bit/FP8 path | Medium: 1-3s first token, 28-50 tok/s | Single high-memory GPU at modest context; multi-GPU for long context | 44/50 | 47/50 | 45/50 | Strongest fit when code and multilingual support are both important. |
| Llama 4 Scout | H100 80GB, int4/on-the-fly quantization | Medium-slow: 2-4s first token, 20-40 tok/s | Not a consumer-GPU model despite 17B active parameters | 43/50 | 42/50 | 38/50 | Great ecosystem and context reach, but the hardware floor is real. |
| Llama 4 Maverick / Mistral Large 3 / Qwen3.5 flagship | Multi-H100 class | Highly deployment-dependent | Procurement project, not app-team self-hosting | 45-48/50 | 45-49/50 | 44-49/50 | Use only when private frontier-class quality justifies a platform team. |
Do not copy those numbers into procurement. They are decision-grade bands from one harness. The useful lesson is the shape: smaller Mistral wins operational simplicity, Qwen wins code and language breadth, and Llama wins ecosystem reach while asking for more hardware discipline.
Hardware Reality
The most common self-hosting mistake is confusing active parameters with weight memory. MoE models may activate only part of the network per token, but the full set of weights still has to live somewhere. Quantization reduces weight memory, but it does not remove KV-cache growth from long prompts, batch size, and concurrency.
- Single 16GB GPU: viable for 3B-8B quantized models, narrow assistants, classification, and branch-office tools. Do not expect strong long-context RAG.
- Single 24GB GPU: comfortable for 7B-14B quantized models and many production extraction or summarization workloads. This is the practical Mistral/Small-Qwen tier.
- Single 48GB-80GB GPU: viable for 27B-35B models and Scout-class experiments at controlled context and concurrency.
- Multi-GPU server: required for Maverick, Mistral Large 3, Qwen3.5 flagship-class models, high concurrency, or very long context.
- Managed API: usually wins when utilization is low, traffic is bursty, or the team cannot own CUDA, drivers, routing, observability, security patching, and rollback.
Llama: The Ecosystem Pick
Llama is the safest default when the model will sit inside a larger product system: RAG, workflow routing, document search, or internal assistants. The surrounding ecosystem is the asset. There are more adapters, recipes, inference examples, safety tools, hosting guides, and migration paths than with most open-weight families.
Where Llama loses: the license is custom, not plain Apache 2.0; large MoE releases raise the hardware floor; and leaderboards can hide differences between public and tested variants. Scout is attractive for long-context work, but a 10M context window is not free. At production concurrency, KV cache and latency can erase the benefit unless the workload truly needs that much context.
Use Llama for broad internal assistants, RAG prototypes that may need many hosting options, and teams that value ecosystem stability. Avoid it when you need the simplest commercial license, the cheapest single-GPU deployment, or code-first multilingual output.
Mistral: The Efficient Operator Pick
Mistral is strongest when the business problem is narrow enough to reward efficiency: classify this ticket, extract these fields, summarize this call, route this workflow, rewrite this message in a controlled tone. Ministral 3 8B/14B is especially interesting because it gives many teams enough quality on hardware they can actually buy.
The downside is model-line complexity. Mistral has open, premier, legacy, and differently licensed releases, so procurement must check the exact card rather than trusting the brand name. Smaller Mistral models can also underperform larger families on open-ended reasoning, messy coding tasks, or multilingual content outside the languages you validate.
Use Mistral when inference cost and latency matter every day. Avoid it when the business expects one model to behave like a frontier generalist across every department.
Qwen: The Coding and Multilingual Pick
Qwen deserves a serious test whenever code, Chinese-language content, mixed-language documentation, or international support is part of the workload. The 2026 Qwen3.6 releases are particularly relevant for repository-level reasoning and agentic coding workflows, and the Apache 2.0 licensing on open-weight models is simpler for many commercial teams than custom community terms.[4]
Where Qwen loses: the naming moves quickly, open-weight and API-only variants are easy to confuse, and some infrastructure paths trail Llama in examples and managed hosting polish. Reasoning modes can also make latency less predictable. Validate tokenizer behavior, citation formatting, JSON repair, and policy-sensitive language in the exact languages your customers use.
Use Qwen for coding assistants, multilingual support, bilingual knowledge bases, and Asia-facing products. Avoid it if your operations team wants the most familiar ecosystem and your content is mostly English, routine, and latency-constrained.
Best Fit by Business Workload
| Workload | Best first test | Why |
|---|---|---|
| RAG and internal search | Llama or Qwen | Llama has the broadest RAG ecosystem; Qwen is better when documents mix languages or code. |
| Extraction and classification | Mistral | Smaller efficient models can beat larger models on cost when the schema is stable. |
| Coding assistants | Qwen, then Mistral | Qwen is strong on code and repo workflows; Mistral is good for constrained code tasks. |
| Multilingual support | Qwen | Best first test for Chinese and broader multilingual coverage. |
| Regulated/private deployment | Depends on license and audit needs | Self-hosting helps only if you also own logging, access control, redaction, monitoring, and incident response. |
| Edge or branch-office tools | Mistral small tiers | Hardware practicality matters more than benchmark status. |
Selection Workflow
- Write down the one workload that must work first. If the answer is everything, the project is not scoped.
- Set hard limits for latency, monthly volume, maximum GPU budget, languages, context length, and license tolerance.
- Run a 30-50 prompt eval from real tickets, documents, schemas, and code. Include failures, not only demos.
- Test the exact quantization, batching, and serving stack planned for production.
- Compare total cost against a managed API at realistic utilization, including staff time and downtime risk.
- After narrowing the shortlist, use the AI Models app to compare model families, access type, context windows, modalities, and operational fit.
FAQ
Is Llama better than Mistral or Qwen?
No. Llama is often the ecosystem default. Mistral is often the deployment-efficiency default. Qwen is often the coding and multilingual default.
Is self-hosting cheaper?
Only at steady utilization or when privacy, residency, latency, or customization requirements are worth the operations burden. For low-volume teams, an API is usually cheaper.
Can one company use more than one model?
Yes. A strong pattern is to route simple extraction to a small Mistral model, code-heavy tasks to Qwen, and broad internal assistant work to Llama or a managed frontier API.
Sources
- Meta Llama 4 Scout/Maverick model card and license details: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
- Mistral model overview and Mistral Small 4 specs: https://docs.mistral.ai/models/overview and https://docs.mistral.ai/models/mistral-small-4-0-26-03
- Mistral 3 release notes, model sizes, and Apache 2.0 statement: https://mistral.ai/news/mistral-3
- Qwen3.6/Qwen3.5 official repository, release notes, deployment examples, and Apache 2.0 license note: https://github.com/QwenLM/Qwen3.6
- vLLM supported-models documentation for Llama, Mistral, and Qwen serving support: https://docs.vllm.ai/en/latest/models/supported_models.html