Capability · applied inside an engagement

Mistral & Open-Weight Models

Open-weight AI on infrastructure you operate — with licence, control and data flows made explicit and verified.

Hyperion does not position itself as a general engineering bench. Technical fluency is used to make better product decisions, challenge assumptions, define evidence and coordinate the critical specialists required by the mandate.

Select & size
Deploy
Adapt
Govern & evaluate

From model choice to governed production.

Many industrial settings need a deployment boundary that a generic third-party API may not satisfy. Open-weight models can run on infrastructure you operate, but control and data residency still depend on the whole topology — including telemetry, support, updates, backups and subprocessors. Hyperion owns the decision about where that boundary sits and what evidence would justify it, and coordinates the specialists who implement it. Mistral is our first reference, but the recommendation is workload-selected and vendor-independent: the right answer may be Mistral, another open-weight model, a managed frontier model, retrieval without fine-tuning, fine-tuning, or no LLM at all.

Model selection and sizing for the task; a deployment topology (on-premise, edge, or private cloud); domain adaptation through fine-tuning or retrieval; an inference-serving layer; and a governance and evaluation loop, so quality is measured rather than assumed.

Mistral and other open-weight models; quantization (GGUF, AWQ, GPTQ) for the target hardware; serving with vLLM, llama.cpp or Ollama; LoRA/QLoRA fine-tuning; retrieval integration; evaluation harnesses for quality and regression; on-premise GPU and edge deployment.

Current Mistral models

A dated snapshot of the Mistral model line — Hyperion's first-choice runtime, workload-selected and vendor-independent. Each entry is vendor-documented; verify a model at its official source before relying on it.

Mistral Large 3 25.12
Flagship multimodal LLM · Released 2025-12-01 · 128K+ tokens · text + vision
Licence: Open-weight (commercial licence for production — confirm at source)
Open weights
Mistral Medium 3.5 26.04
Frontier multimodal / agentic + coding LLM · Released 2026-04-01 · 128K+ tokens · text + vision
Licence: Commercial / Mistral platform
Proprietary
Mistral Small 4 26.03
Efficient hybrid LLM (instruct + reasoning + coding) · Released 2026-03-01 · 128K+ tokens · text + vision
Licence: Apache-2.0
Open weights
Ministral 3 (14B / 8B / 3B) 25.12
Edge-class LLM family (14B / 8B / 3B) · Released 2025-12-01 · 128K tokens · text
Licence: Mixed (open-weight + platform — confirm per size at source)
Open weights
Codestral 25.08
Code-specialised LLM · Released 2025-08-01 · 256K tokens · text (code)
Licence: Mistral AI Non-Production License (commercial licence for production)
Open weights
Devstral 2 / Devstral Small 2 25.12
Agentic coding LLM · Released 2025-12-10 · 128K+ tokens · text (code)
Licence: Mixed (Devstral Small 2 open; Devstral 2 platform — confirm at source)
Open weights
Pixtral Large 24.11
Multimodal (vision) model · Released 2024-11-18 · 128K tokens · text + vision
Licence: Mistral Research License (commercial licence for production)
Open weights

Registry verified 2026-06-28

Frame a model and deployment decision

Open-weight models vs proprietary API

Self-hosting can favour sovereignty, cost predictability and longevity when the licence, complete data flow and operating model support it.

Model examples are illustrative of open classes (e.g. Mistral Small / Ministral-class open-weight models).
Dimension	Open-weight (you run)	Proprietary API
Ownership	You host the weights	Vendor-hosted
Data residency	Can remain in a verified perimeter	Depends on vendor regions, contracts and flows
Cost model	Infrastructure plus operations	Usage- and contract-based
Adaptation	Licence-dependent fine-tune / LoRA	Provider-dependent
Longevity	Pin a version you trust	Deprecation risk