Llama 4 vs Mistral Small 4 (2026)
Quick comparison
| Llama 4 | Mistral Small 4 | |
|---|---|---|
| Vendor | Meta | Mistral AI |
| Architecture | Mixture-of-experts — Scout 109B total, Maverick 400B total / 17B active | Mixture-of-experts — 119B total, 6.5B active per token |
| Smallest useful variant | Llama 4 Scout — single high-end GPU class | Fits a single workstation GPU (small active footprint) |
| Context window | Very large on paper; usable range varies by variant | ~260,000 tokens |
| Licence | Llama 4 Community License (use terms + ~700M MAU clause) | Apache 2.0 |
| Hosted API price | Set by each host — often $0.10–$0.60 / 1M at the budget end | ~$0.15 / 1M input, ~$0.60 / 1M output |
| Fine-tune ecosystem | Large — most tooling supports Llama first | Solid, smaller |
| Self-hostable | Yes | Yes |
Details are approximate and current as of August 2026. Open-weight specifics — licence text, context limits, host pricing — move quickly, so verify against Meta's and Mistral's own pages before committing.
Licence: the part people skip and regret
If you're building a product on top of an open model, read the licence before the benchmarks. Mistral Small 4 is Apache 2.0: use it commercially, fine-tune it, ship it, no user-count clause. Llama 4's Community License is permissive in spirit but not the same thing — it carries acceptable-use rules and a provision aimed at very large platforms (around 700 million monthly active users). Almost nobody hits that threshold, but "almost nobody" is not "nobody", and some procurement teams won't sign off on it regardless.
Hardware and operational cost
For self-hosting, the question that decides your bill is how many GPUs you need to keep one instance warm. Mistral Small 4's active-parameter footprint is small enough to serve from a single high-end card, which keeps both capital and idle cost down. Llama 4 Scout is also aimed at single-GPU deployment but sits heavier; the larger Llama 4 variants move you into multi-GPU territory and a different cost bracket.
If you'd rather rent than own, both are available through the usual inference hosts at budget-tier prices. At that point the comparison blurs into "which host, which variant" — and a hosted closed model like DeepSeek V4 Flash or Gemini 2.5 Flash-Lite is often the simpler answer unless you specifically need open weights. Our local deployment guide has the full hardware breakdown.
Where Llama 4 pulls ahead
Ecosystem and tooling
Llama is the default target for open-model tooling. Quantisation formats, serving stacks, LoRA adapters, community fine-tunes — if a project supports one open model, it supports Llama. That saves real time.
Room to scale
Starting on Scout and moving to a larger Llama 4 variant later means the same prompt format, the same tokenizer, the same tooling. Growing into a bigger model without switching families is worth something.
Harder reasoning tasks
Given enough hardware, the larger Llama 4 variants have more headroom on multi-step reasoning and code than a deliberately small model does. If your workload has a hard tail, that ceiling matters.
Where Mistral Small 4 pulls ahead
Lowest barrier to running it
One GPU, a clean licence, a small footprint. For a team standing up self-hosted inference for the first time, Mistral Small 4 is the least painful place to start.
Latency and throughput per dollar
A small active-parameter MoE serves fast and batches well. For high-volume, well-scoped tasks — classification, extraction, short replies — it delivers a lot of throughput per GPU.
European data-residency story
Mistral is EU-based, which some organisations weight heavily for procurement and data-governance reasons, independent of the model itself.
Head-to-head by use case
| Use case | Better pick | Why |
|---|---|---|
| First self-hosted deployment | Mistral Small 4 | Single GPU, clean licence |
| High-volume classification / extraction | Mistral Small 4 | Throughput per dollar |
| Product that may need a bigger model later | Llama 4 | Scale within one family |
| Heavy fine-tuning / adapter work | Llama 4 | Widest tooling support |
| Hard multi-step reasoning, hardware available | Llama 4 (larger variant) | More headroom |
| Strict licence review | Mistral Small 4 | No MAU clause |
| Just want low cost, no self-host requirement | Neither — use a hosted budget model | Less operational work |
So which one?
Pick Mistral Small 4 if:
- You want to run it on one GPU with minimal fuss
- Your licence review is strict
- The workload is high-volume and well-scoped
- EU data residency is a factor
Pick Llama 4 if:
- You want the broadest tooling and fine-tune ecosystem
- You expect to scale up to a larger model in the same family
- You have hardware for a bigger variant and need the reasoning headroom
- Your team already has Llama serving infrastructure
If you're not sure you need open weights at all: start with a hosted budget model, measure, and only move to self-hosting Llama 4 or Mistral Small 4 once volume or privacy makes the operational cost worth it.
FAQ
Is Llama 4 or Mistral Small 4 better for self-hosting?
Mistral Small 4 has the smaller footprint and is the easier model to run on a single workstation GPU. Llama 4 Scout is also single-GPU-class but heavier, and the larger Llama 4 variants need multi-GPU setups. For the lowest hardware bar, start with Mistral Small 4.
What are the licence differences?
Mistral Small 4 ships under Apache 2.0 with no user-count restriction. Llama 4 uses Meta's Llama 4 Community License, which is free for almost everyone but adds acceptable-use terms and a clause for platforms above roughly 700 million monthly active users. Check the current text before building on either.
Which has the larger context window?
Both are usable in the mid-hundreds of thousands of tokens, with Mistral Small 4 around 260K. Llama 4 advertises a very large window, but real long-context quality depends on the variant and your serving setup.
Should I just use a hosted API instead?
If you have no privacy or cost reason to self-host, a hosted budget model like Gemini 2.5 Flash-Lite or DeepSeek V4 Flash is usually less work and competitive on price. Self-hosting Llama 4 or Mistral Small 4 pays off at high, steady volume or when data can't leave your network.