The Western open-weight matchup. Llama 4 Maverick has the higher ceiling and bigger ecosystem; Mistral Small 4 is the deployment gem — 6.5B active parameters, 256K context, multimodal input, Apache 2.0 with no MAU clauses, from an EU vendor. European regulated industries default to Mistral; everyone else weighs Llama's ecosystem against Mistral's cleaner license and cheaper serving.
Mistral vs Llama 4 at a glance
| Mistral Small 4 | Llama 4 | |
|---|---|---|
| Vendor | Mistral AI (France) | Meta (United States) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Apache 2.0 — permissive, patent-granting, free for commercial use | Llama 4 Community License — free for most commercial use; requires a separate license above 700M MAU and carries branding/acceptable-use conditions |
| Parameters | 119B (6.5B active) | Scout 109B / Maverick 400B (17B (both) active) |
| Context window | 256K tokens | Maverick 1M tokens; Scout up to 10M (claimed) |
| Modalities | text, image | text, image |
| Pricing | Varies by hosting provider (open weights) | Varies by hosting provider (open weights) |
| Released | 2026-03-01 | 2025-04-05 |
Specs and pricing verified July 2026.
About Mistral Small 4
Mistral Small 4 is the European entry in the 2026 open-weight field and arguably the best production-per-watt model available: 119B total parameters with only 6.5B active, 256K context, multimodal input, a reasoning mode, native function calling and JSON output — all under Apache 2.0.
Mistral's positioning is deployment-first rather than benchmark-first: Small 4 targets the regulated-industry and on-premise market where EU jurisdiction, clean licensing, and single-GPU serving matter more than leaderboard positions. For European enterprises with data-sovereignty requirements it is frequently the default choice.
Full specs, benchmarks, and hardware guidance: the Mistral Small 4 page.
About Llama 4
Llama 4 is Meta's mixture-of-experts generation: Scout (109B total) and Maverick (400B total), both activating 17B parameters per token, both natively multimodal on text and images. Maverick still posts the highest MMLU among open models in mid-2026 roundups (85.5%), and Scout's headline 10M-token context claim remains the largest advertised window in open weights, with ~1M tokens the practically-validated range.
Llama's superpower is its ecosystem: every inference framework, fine-tuning library, and cloud platform supports it on day one, and the Llama 4 Community License — while not OSI-open (700M-MAU clause, branding requirements) — is free for virtually every company that isn't a hyperscaler. Amid the 2026 wave of Chinese frontier releases, Llama 4 remains the flagship American open-weight family and the safe institutional default.
Full specs, benchmarks, and hardware guidance: the Llama 4 page.
Choosing between them
Choose Mistral Small 4 for:
- EU data-sovereignty and regulated-industry deployments
- Single-GPU production serving (6.5B active parameters)
- Function calling and structured-output pipelines
Choose Llama 4 for:
- The broadest tooling and ecosystem support in open weights
- Single-GPU long-context deployment (Scout on one H100)
- Organizations that prefer a US-based vendor for governance reasons
Frequently asked questions
Is Mistral Small 4 better than Llama 4?
The Western open-weight matchup. Llama 4 Maverick has the higher ceiling and bigger ecosystem; Mistral Small 4 is the deployment gem — 6.5B active parameters, 256K context, multimodal input, Apache 2.0 with no MAU clauses, from an EU vendor. European regulated industries default to Mistral; everyone else weighs Llama's ecosystem against Mistral's cleaner license and cheaper serving.
Which is cheaper: Mistral Small 4 or Llama 4?
Mistral Small 4: Varies by hosting provider (open weights). Llama 4: Varies by hosting provider (open weights). Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Mistral Small 4 and Llama 4?
Mistral Small 4: yes — weights are published under Apache 2.0. Llama 4: yes — weights are published under Llama Community.