cashcrown // wiedza

Comparisons

Models, approaches and tools side by side — honestly, with explicit criteria. “Best” is computed, not asserted; the model data comes from our routing matrix.

OpenClaw routing tiers — GPU cost proxy and task coverage (source: routing matrix)
Model tier
Flagship	3	9 (best)	cloud	masked
Mid	1.5	6	cloud	masked
Small	1	3	cloud	masked
BGE-M3 (local)	0.15 (best)	1	on-prem	stays local

How to give a model domain knowledge — a qualitative comparison.
	RAG	Fine-tuning	Prompt only
Fresh/current data	Yes	No	No
Setup cost	Low	High	Low
Update without retraining	Yes	No	Yes
Style/behaviour control	Partial	Full	Partial
Hallucination risk	Low	Medium	High
Citable sources	Yes	No	No

Where to process data and run models — privacy/cost/quality trade-offs.
	Local	Hybrid	Cloud
Data stays on-prem	Yes	Partial	No
Top model quality	Medium	High	High
Cost at scale	Low	Medium	High
PII protection	Full	Full	Partial
Vendor independence	High	Medium	Low
Ops complexity	High	Medium	Low

What to wire workflows with — compared on data control and scale.
	n8n (self-hosted)	Make	Custom code
Self-hosting (your data)	Yes	No	Yes
Cost at scale	Low	High	Low
Time to start	Fast	Fast	Slow
Flexibility	Medium	Medium	High
Vendor lock-in	Low	High	None
Data control	Full	Partial	Full

A comparison of our default production models — profiles, not “general intelligence”. Full measured data: the model atlas.
	DeepSeek-V4	Mistral Large 3	Qwen3-Coder	Gemma 3
Primary task	reasoning	chat + translation	code	summarize + fast
Throughput	High	Medium	High	Low
Context window	High	Medium	Medium	Medium
Reasoning mode	Yes	No	No	No
Vision (image)	No	No	No	No
Cost (GPU proxy)	High	High	High	Medium

When to turn reasoning (thinking) on and when not — forced on, it can be slow, costly and return empty content.
	Thinking (reasoning)	Instruct (non-thinking)
Response speed	Slow	Fast
Cost	High	Low
Accuracy on hard decisions	High	Medium
Empty-answer risk in chat	High	None
Best for	analysis, planning, agents	chat, code, translation, summaries
When to enable	only when the task needs reasoning	by default (think off)

Build a custom assistant or use an off-the-shelf one — an honest qualitative comparison.
	Custom	Off-the-shelf (SaaS)
Answers from your knowledge (RAG)	Full	Partial
Data control / residency	Full	Partial
Integration with systems (CRM, etc.)	Full	Partial
Time to launch	Slow	Fast
Startup cost	Medium	Low
Cost at scale	Low	High
Vendor independence (no lock-in)	High	Low
Guardrails / behaviour control	Full	Partial
Citable sources	Yes	Partial

Bigger isn't always better — a qualitative comparison for choosing per task.
	Small (specialised)	Large (general)
Inference cost	Low	High
Latency	Fast	Slow
Quality on complex tasks	Medium	High
Local hosting ease	Full	Partial
Privacy (data local)	Full	Partial
Fine-tuning cost	Low	High
Versatility (many tasks)	Partial	Full

An honest, qualitative comparison of four vector databases for RAG. Cashcrown self-hosts Qdrant, but each of these has valid use cases — pgvector wins when you already run Postgres, and Pinecone when you want zero self-operated infrastructure. Ratings are approximate and depend on scale and version.
	Qdrant	Pinecone	pgvector (Postgres)	Weaviate
Self-hostable	Yes	No	Yes	Yes
Managed / SaaS option	Yes	Yes	Yes	Yes
Open source	Yes	No	Yes	Yes
Hybrid (keyword + vector) search	Full	Partial	Partial	Full
Metadata filtering	Full	Full	Full	Full
Horizontal scale	High	High	Low	High
Operational simplicity	Medium	High	High	Medium
Cost at small scale	Low	Medium	Low	Low

An honest, qualitative comparison. Cashcrown runs BGE-M3 locally (via Ollama, 1024-dim) so data never leaves the box. That does not make BGE-M3 best in every row: Cohere Embed v4 leads on very long context, and self-hosting carries its own operational cost.
	BGE-M3 (self-hosted)	OpenAI text-embedding-3	Cohere Embed v4	multilingual-e5
Multilingual quality (incl. Polish)	High	Medium	High	Medium
Self-hostable	Yes	No	Partial	Yes
Data stays local (no cloud)	Full	None	Partial	Full
Cost	Low	Medium	High	Low
Long context	Medium	High	High	Low
Hybrid / sparse support	Full	None	Partial	None
Open model weights	Yes	No	No	Yes
Time to deploy (ready API vs. own infra)	Slow	Fast	Fast	Slow

An honest, qualitative comparison of four approaches to building AI agents. For vendor lock-in, lower is better. No approach wins on every row: custom code gives full control and auditability at the cost of a steep learning curve and build effort, while managed assistant APIs are fast and production-ready out of the box at the cost of control and transparency.
	Custom code (Cashcrown's orchestration)	LangChain / LangGraph	n8n / no-code	Managed assistant APIs
Control over behaviour	Full	High	Partial	Low
Auditability / logging	Full	Partial	Partial	Low
Vendor lock-in (lower = better)	Low	Medium	Medium	High
Cost transparency	High	Medium	Medium	Low
Production-readiness	High	Medium	Medium	High
Learning curve	Slow	Slow	Fast	Fast

Want every model with measured parameters and per-task selection? Model atlas →

Comparisons

Models, approaches and tools side by side — honestly, with explicit criteria. “Best” is computed, not asserted; the model data comes from our routing matrix.

OpenClaw routing tiers — GPU cost proxy and task coverage (source: routing matrix)
Model tier
Flagship	3	9 (best)	cloud	masked
Mid	1.5	6	cloud	masked
Small	1	3	cloud	masked
BGE-M3 (local)	0.15 (best)	1	on-prem	stays local

How to give a model domain knowledge — a qualitative comparison.
	RAG	Fine-tuning	Prompt only
Fresh/current data	Yes	No	No
Setup cost	Low	High	Low
Update without retraining	Yes	No	Yes
Style/behaviour control	Partial	Full	Partial
Hallucination risk	Low	Medium	High
Citable sources	Yes	No	No

Where to process data and run models — privacy/cost/quality trade-offs.
	Local	Hybrid	Cloud
Data stays on-prem	Yes	Partial	No
Top model quality	Medium	High	High
Cost at scale	Low	Medium	High
PII protection	Full	Full	Partial
Vendor independence	High	Medium	Low
Ops complexity	High	Medium	Low

What to wire workflows with — compared on data control and scale.
	n8n (self-hosted)	Make	Custom code
Self-hosting (your data)	Yes	No	Yes
Cost at scale	Low	High	Low
Time to start	Fast	Fast	Slow
Flexibility	Medium	Medium	High
Vendor lock-in	Low	High	None
Data control	Full	Partial	Full

A comparison of our default production models — profiles, not “general intelligence”. Full measured data: the model atlas.
	DeepSeek-V4	Mistral Large 3	Qwen3-Coder	Gemma 3
Primary task	reasoning	chat + translation	code	summarize + fast
Throughput	High	Medium	High	Low
Context window	High	Medium	Medium	Medium
Reasoning mode	Yes	No	No	No
Vision (image)	No	No	No	No
Cost (GPU proxy)	High	High	High	Medium

When to turn reasoning (thinking) on and when not — forced on, it can be slow, costly and return empty content.
	Thinking (reasoning)	Instruct (non-thinking)
Response speed	Slow	Fast
Cost	High	Low
Accuracy on hard decisions	High	Medium
Empty-answer risk in chat	High	None
Best for	analysis, planning, agents	chat, code, translation, summaries
When to enable	only when the task needs reasoning	by default (think off)

Build a custom assistant or use an off-the-shelf one — an honest qualitative comparison.
	Custom	Off-the-shelf (SaaS)
Answers from your knowledge (RAG)	Full	Partial
Data control / residency	Full	Partial
Integration with systems (CRM, etc.)	Full	Partial
Time to launch	Slow	Fast
Startup cost	Medium	Low
Cost at scale	Low	High
Vendor independence (no lock-in)	High	Low
Guardrails / behaviour control	Full	Partial
Citable sources	Yes	Partial

Bigger isn't always better — a qualitative comparison for choosing per task.
	Small (specialised)	Large (general)
Inference cost	Low	High
Latency	Fast	Slow
Quality on complex tasks	Medium	High
Local hosting ease	Full	Partial
Privacy (data local)	Full	Partial
Fine-tuning cost	Low	High
Versatility (many tasks)	Partial	Full

An honest, qualitative comparison of four vector databases for RAG. Cashcrown self-hosts Qdrant, but each of these has valid use cases — pgvector wins when you already run Postgres, and Pinecone when you want zero self-operated infrastructure. Ratings are approximate and depend on scale and version.
	Qdrant	Pinecone	pgvector (Postgres)	Weaviate
Self-hostable	Yes	No	Yes	Yes
Managed / SaaS option	Yes	Yes	Yes	Yes
Open source	Yes	No	Yes	Yes
Hybrid (keyword + vector) search	Full	Partial	Partial	Full
Metadata filtering	Full	Full	Full	Full
Horizontal scale	High	High	Low	High
Operational simplicity	Medium	High	High	Medium
Cost at small scale	Low	Medium	Low	Low

An honest, qualitative comparison. Cashcrown runs BGE-M3 locally (via Ollama, 1024-dim) so data never leaves the box. That does not make BGE-M3 best in every row: Cohere Embed v4 leads on very long context, and self-hosting carries its own operational cost.
	BGE-M3 (self-hosted)	OpenAI text-embedding-3	Cohere Embed v4	multilingual-e5
Multilingual quality (incl. Polish)	High	Medium	High	Medium
Self-hostable	Yes	No	Partial	Yes
Data stays local (no cloud)	Full	None	Partial	Full
Cost	Low	Medium	High	Low
Long context	Medium	High	High	Low
Hybrid / sparse support	Full	None	Partial	None
Open model weights	Yes	No	No	Yes
Time to deploy (ready API vs. own infra)	Slow	Fast	Fast	Slow

An honest, qualitative comparison of four approaches to building AI agents. For vendor lock-in, lower is better. No approach wins on every row: custom code gives full control and auditability at the cost of a steep learning curve and build effort, while managed assistant APIs are fast and production-ready out of the box at the cost of control and transparency.
	Custom code (Cashcrown's orchestration)	LangChain / LangGraph	n8n / no-code	Managed assistant APIs
Control over behaviour	Full	High	Partial	Low
Auditability / logging	Full	Partial	Partial	Low
Vendor lock-in (lower = better)	Low	Medium	Medium	High
Cost transparency	High	Medium	Medium	Low
Production-readiness	High	Medium	Medium	High
Learning curve	Slow	Slow	Fast	Fast

Want every model with measured parameters and per-task selection? Model atlas →