Eval directory
Evals for DeepSeek
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for DeepSeek AI products.
About DeepSeek
DeepSeek is an AI company shipping frontier open-weight models (DeepSeek-V3, DeepSeek-R1) and an OpenAI-compatible API with a separate reasoner model (deepseek-reasoner), automatic disk-based context caching, function calling, JSON output, and very low token pricing. The models are released under an MIT license alongside the hosted API.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
0/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for DeepSeek
8 packs ready to run.
Auth Rate Limits And Cost
Evaluates DeepSeek's Auth, Rate Limits & Cost across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Chat Completions Openai Compatible
Evaluates DeepSeek's Chat Completions (OpenAI-compatible) across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Context Caching Disk Kv Cache
Evaluates DeepSeek's Context Caching (disk KV cache) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Fim Completions Beta
Evaluates DeepSeek's FIM / Completions (beta) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Function Calling And Tool Use
Tool SelectionEvaluates DeepSeek's Function Calling & Tool Use across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Json Structured Output
Evaluates DeepSeek's JSON / Structured Output across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Reasoning Model Deepseek Reasoner
Evaluates DeepSeek's Reasoning Model (deepseek-reasoner) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Safety Models And Governance
Evaluates DeepSeek's Safety, Models & Governance across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Foundation Model & API eval coverage.
Why eval DeepSeek AI
DeepSeek's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for DeepSeek measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against DeepSeek's public product surface and runnable in Corsac with your own data.