# Slash AI Lab: our AI evaluation lab · Slash Digital Lab Source: https://slash-digital.io/en/ai-lab/ Slash AI Lab We evaluate models and architectures with client data (in Spanish, French and English) before committing cost or trust. What fails evaluation doesn't ship. - Models OpenAI · Anthropic · Gemini · DeepSeek · GLM · Qwen · Mistral · open source - Axes coding · reasoning · language · agents · cost - Rule Evaluation, not hype Start a project See real work In short Slash AI Lab is the AI evaluation lab of Slash Digital Lab, in Bogotá: we test models (OpenAI, Anthropic, Gemini, DeepSeek, GLM, Qwen, Mistral and local open source) on the client's real prompts before committing cost or private data. Lab rule: no model is chosen by hype. It's chosen by evaluation on the client's prompts, cost, data residency and observed failures. What we deliver ## What the lab evaluates Evaluation axes: business Spanish, French, code, agents, cost, latency and privacy. 01 ### Model selection Reproducible comparisons across providers and open models, on your use cases, not on a paid benchmark. 02 ### Language quality Your operation's Spanish and French are evaluated as hard as English; nuances matter. 03 ### Agents & tooling MCP, function calling, memory and human oversight: we measure what breaks when the agent errs. 04 ### Cost, latency, privacy Inference budget, p95 response time and data residency: cloud, VPC or local inference. How we work ## How an evaluation runs - ### Collection We gather real or representative prompts and success criteria with your team. - ### Runs Candidates run in cloud or local; we measure quality, cost and latency. - ### Report Results per axis with examples: a clear, reproducible recommendation. - ### Watch The market moves every quarter; when it does, we re-run. Scope and deliverables ## The method, in writing - **Evaluation:** 30 fixed prompts, 5 engines, 3 languages (methodology published in /insights/). - **Data residency:** Standard cloud, VPC or local inference depending on compliance. - **Report:** Comparison per axis: language, code, agents, cost, latency, failures. - **Reproducibility:** Prompts, versions and criteria documented to repeat the run. - **Cadence:** Quarterly re-evaluation or on relevant market moves. - **Publication:** Findings open in /insights/; client prompts never. Proof in production ## Proof in production 30, fixed prompts per evaluation 3, languages evaluated: ES · EN · FR 0, hype: only reproducible decisions 5, evaluation axes Read more Slash AI Lab · 13 Vendémiaire ### A history documentary made with AI on one GPU 13 Vendémiaire, on Bonaparte and 5 October 1795, generated on one RTX 4090: images, video with sound, narration and score, in French, Spanish and English. Learn more → Read more SlashBots · Desktop agents ### Desktop AI agents for our own team SlashBots, our Windows and Linux app: seven bots with a built-in browser, computer use, 421 skills, memory, MCP connectors, routines and a phone companion. Learn more → Read more slash.events · Panel Studio ### From a website to a PDF/X-4 trade-show panel Panel Studio, our slash.events app: website analysis, four concepts, chat edits, 23 preflight checks and a printer-ready file for €49. Learn more → Method ## The lab's rules The same in every publication and every project. 01 ### Methodology before numbers We publish the method before the results; no finished run, no number. 02 ### Real prompts We evaluate on the client's tasks, not toys: 30 fixed, versioned prompts per case. 03 ### Three languages Business Spanish, English and French: that's where models break. 04 ### Declared limitations Every analysis says what it doesn't measure, with its date, versions and linked sources. Publications ## Published criteria, not opinions Models ### Claude Opus 5.5: what changes for businesses Launched September 22, 2026: cheaper than Opus 5, Fable 5.1-level results per Anthropic. Pricing, benchmarks, breaking changes and when to use it. Updated Oct 28, 202611 minJean Martial Models ### The AI model map, October 2026 Claude Opus 5.5 and Sonnet 5.5, GPT-6 Astra, Gemini 4 Argon, Grok 4.7, Muse Spark: who leads in October 2026, what each price tier costs and what to test. Updated Oct 28, 202610 minJean Martial Local AI and open weights ### Open-weight models in 2026: which to use, under which license Kimi K3, Qwen3.8, DeepSeek V4.1, GLM-5.3, Gemma 4, gpt-oss, Mistral: sizes, licenses, gap to the frontier and how to host them without legal surprises. Updated Oct 28, 20269 minRémy Sanchez Agents and engineering ### Fine-tuning vs RAG: the criterion When RAG, when fine-tuning and when a hybrid: the decision order before paying for any training. Updated Sep 26, 20263 minJean Martial Frequently asked questions ## The questions we hear often **Which models do you evaluate?** The ones that can earn a slot: OpenAI, Anthropic, Gemini, DeepSeek, GLM, Qwen, Mistral and local open source. The list moves with the market. **Do you publish results?** Yes: method and findings go to /insights/. Client prompts are never published. **Is going through the lab mandatory?** Recommended, not mandatory. For sensitive-data or minority-language projects, evaluation avoids months of drift. **Do you offer local inference?** Yes: open models in your VPC or own hardware when compliance requires it. **Can we evaluate a single use case?** Yes: one focused run with your prompts and a reproducible report. **Do you accept sponsorships?** No. Slash research isn't for sale.