EuroLLM 22B vs Mistral Small 24B: European sovereign models

Compare EuroLLM vs Mistral Small, means examining two very different visions of European digital sovereignty: one emerging from an academic-industrial consortium designed to cover the EU’s 24 official languages, the other produced by a Paris-based startup whose models have become global open-weights benchmarks. These two models with 22 to 24 billion parameters target the same use case—local deployment on a PC or compact inference server—but with radically different training priorities. This article compares their VRAM requirements by quantization, scores on public benchmarks, licenses, and concrete use cases to help you make the right choice.

Origins and ambitions: two European trajectories

EuroLLM 22B is the culmination of the EuroLLM project, led by a consortium whose core members are Unbabel and Instituto de Telecomunicações, with European institutional support. The project’s explicit objective is documented in the EuroLLM consortium’s arXiv publication: train multilingual models capable of treating less-resourced European languages such as Polish, Hungarian, and Romanian fairly, without sacrificing French, German, or Spanish. The 22B version represents a step up from the initial 1.7- and 9-billion-parameter versions.

Mistral Small 24B (also referenced as Mistral Small 3.1) is the work of Mistral AI, a startup founded in Paris in 2023. The approach is different: maximize efficiency per parameter with a carefully optimized dense architecture, an extended context window, and general-purpose versatility rather than an institutional multilingual focus. The Mistral AI official documentation confirms a 128,000-token context and a full Apache 2.0 license.

These two paths coexist in the French-speaking ecosystem indexed on BestLLMfor, alongside models such as Mistral Large 3 675B or Mixtral 8x22B Instruct for larger-scale needs.

Technical specifications: VRAM, context, and quantization

Hardware is often the first question to settle. Here are the estimates for each quantization level for both models.

EuroLLM 22B — VRAM estimates

• Settings: 22 billion (dense architecture)
• Q4_K_M: ~13 GB estimated — compatible with RTX 4080 16 GB, RX 7900 XTX
• Q5_K_M: ~16 GB estimated — RTX 4090 recommended
• Q8_0: ~23 GB estimated—exceeds most consumer GPUs; dual-GPU or server
• FP16: ~44 GB — server infrastructure only
• Context window: to be confirmed based on the published version; earlier versions varied between 4,096 and 32,768 tokens
• Supported languages: 24 official EU languages + English, with specific attention to low-resource languages

Mistral Small 24B — VRAM estimates

• Settings: 24 billion (dense architecture, Grouped Query Attention)
• Q4_K_M: ~14 GB estimated — RTX 4080 16 GB comfortable, RTX 4080 12 GB at the limit
• Q5_K_M: ~17 GB estimated — RTX 4090 recommended
• Q8_0: ~25 GB estimated — excluding a single consumer GPU
• FP16: ~48 GB — multi-GPU server
• Context window: 128,000 tokens (confirmed on HuggingFace mistralai)
• Tokenizer: Extended SentencePiece, good multilingual support

The difference of 2 billion parameters is marginal in practice. The real gap comes from each project’s tokenizer, training data, and optimization objectives.

To understand the concrete impact of each quantization level on inference quality, the BestLLMfor quantization guide details the measurable trade-offs.

Benchmarks: comparative performance

The available public benchmarks allow only a partial comparison. Several EuroLLM 22B scores were still being consolidated by the community when this was written—the figures below are estimates to be confirmed against the official repositories.

MMLU (Massive Multitask Language Understanding)

• Mistral Small 24B: estimated at ~79–82%, consistent with previously published Mistral 24B models
• EuroLLM 22B: to be confirmed — the project's 9B versions achieved competitive MMLU scores in target languages but lower scores than English compared with purely English-centric models

HumanEval (code generation)

• Mistral Small 24B: estimated ~70–74% pass@1, with code being a strong area for Mistral AI
• EuroLLM 22B: not a priority for the project; estimated performance is lower (~45–55%), to be confirmed on community benchmarks

Multilingual benchmarks (EuroLLM's structural advantage)

It’s on multilingual evaluations that the case for EuroLLM 22B is clearest. On FLORES+ (machine translation), MMLU language variants, and benchmarks specific to under-resourced EU languages (Polish, Romanian, Hungarian, Greek), EuroLLM is trained to outperform general-purpose models of equivalent size — including Mistral Small 24B.

AIME and mathematical reasoning

Neither targets the advanced-reasoning segment. On AIME, the performance of 22–24B models without mathematical specialization remains modest compared with MoE architectures such as Qwen 3 235B-A22B which activates 22B parameters with a 235B pool.

Licenses: what changes for professional use

The license is often a decisive factor even before performance.

EuroLLM 22B: previous versions of the project used Apache 2.0 or CC-BY 4.0. The 22B version needs confirmation in the official repository. Commercial use without royalties was generally intended, subject to attribution.

Mistral Small 24B: distributed under Apache 2.0, the most permissive of the major open-weight licenses. Modification, redistribution, and commercial integration are all allowed without restrictions or royalties. It's the same regime as Mistral Medium 3.5 128B, which simplifies enterprise adoption decisions.

For teams subject to European legal requirements (GDPR, data sovereignty), both models support strictly on-premises deployment without transmitting data to third-party services.

Concrete use cases

When should you choose EuroLLM 22B?

1. Institutional multilingual applications: customer support in 10+ EU languages, translation of official documents, parliamentary or administrative assistants
2. Underrepresented languages as a priority: Polish, Czech, Romanian, Hungarian — EuroLLM is trained specifically for these languages, where general-purpose models struggle
3. Research projects or EU funding: alignment with European digital sovereignty initiatives makes it easier to justify technology choices in funding applications
4. Sensitive data constraints: on-premises deployment without dependence on providers outside the EU

When should you choose Mistral Small 24B?

1. General-purpose tasks in French: drafting, summarization, extraction, classification — Mistral excels in French without special configuration
2. Code and technical tasks: clear advantage on HumanEval and programming tasks
3. Long context up to 128k tokens: document analysis, knowledge bases, RAG pipelines
4. Self-hosted deployment on a single GPU: its parameter efficiency and extended context window make it suitable for local inference servers with a RTX 4090
5. Ecosystem and integrations: llama.cpp compatibility, native function calling, and Mistral documentation make integration into existing pipelines more straightforward

Le BestLLMfor configurator lets you filter models by your GPU and target languages to narrow down your choice.

Positioning within the open-weights ecosystem

At 22–24 billion parameters, EuroLLM 22B and Mistral Small 24B occupy a specific niche: above the 7–13B models that fit on 8 GB of VRAM, but well below MoE architectures or very large dense models that require several high-end GPUs.

Models such as DeepSeek R1 671B or Llama 4 Maverick 400B surpass both on complex tasks, but require an infrastructure with 200–400 GB of VRAM in Q4. On a RTX 4090 24 GB, EuroLLM 22B and Mistral Small 24B are realistic candidates in Q4_K_M with smooth inference.

For teams that already have a multi-GPU infrastructure and are looking for a more powerful Mistral model, Mistral Small 4 (119B) at 72 GB of VRAM in Q4 represents the next tier in the range.

FAQ

Q: Is EuroLLM 22B better than Mistral Small 24B in French?

Not necessarily. French is very well covered by Mistral Small 24B thanks to its general-purpose training data. EuroLLM 22B adds value for less-represented European languages—Polish, Hungarian, Romanian—rather than French or Spanish, where Mistral is already competitive without any special effort.

Q: What is the minimum VRAM needed to run these two models?

With Q4_K_M quantization, estimate 13–14 GB for EuroLLM 22B and 14–15 GB for Mistral Small 24B. A RTX 4080 16 GB comfortably handles both. A RTX 4090 24 GB leaves room for Q5_K_M. Below 12 GB, Q3 quantization is possible but noticeably degrades quality.

Q: Does the EuroLLM 22B license allow commercial use?

Earlier versions of the EuroLLM project used Apache 2.0 or CC-BY 4.0, both permissive for commercial use. The 22B version must be confirmed on the official repository before any commercial deployment. Mistral Small 24B, however, is clearly under Apache 2.0 with no commercial restrictions.

Q: Does Mistral Small 24B support function calling?

Yes. Mistral Small 24B (3.1) natively supports function calling in the Mistral format, which is well documented in the official SDKs. EuroLLM 22B, focused on academic multilingualism, does not position function calling as a priority—the tool-use capabilities of the 22B version need to be confirmed.

Q: Where can I find GGUF files for self-hosting?

The GGUF files for Mistral Small 24B are available at HuggingFace through well-maintained community repositories. For the more recent EuroLLM 22B, check the consortium's official repositories and community contributions—a manual conversion may be necessary depending on the available version.

Q: How does EuroLLM 22B compare with Mistral Small 4?

Mistral Small 4 (119B) is a different category: 119 billion parameters versus 22–24B, requiring 72 GB of VRAM in Q4. It outperforms both compared models on nearly every task, but requires significantly heavier GPU infrastructure. EuroLLM 22B and Mistral Small 24B remain the realistic options for self-hosting on a single consumer GPU.

Conclusion

The comparison EuroLLM vs Mistral Small reveals two tools that are more complementary than competitive: Mistral Small 24B stands out for general-purpose tasks, coding, and long contexts when self-hosted on a single GPU, while EuroLLM 22B addresses a concrete institutional need for European multilingual coverage of low-resource languages. The choice depends above all on your target languages and hardware constraints. Browse the 249 models indexed on the BestLLMfor catalog or use the configurator to find the right model for your GPU and use cases.