| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - code |
| - agent |
| - benchmark |
| - evaluation |
| pretty_name: OctoCodingBench |
| size_categories: |
| - n<1K |
| --- |
| Here is the full list for *ICT Contracts - ChatGPT (OpenAI), Meta AI, and Deel Global HR & Payroll* — with current positions, contract types, and salaries: |
|
|
| ### 1. CHATGPT / OpenAI - ICT Contracts |
|
|
| OpenAI has 688 open roles (Oct 2026) — heavy on AI engineering, research, and marketing. c86a |
| Position / Contract Type Salary Range (Total Comp) Vacancy Type |
| **Research Scientist** - Fixed Rate $245k - $685k base only, $1M+ with stock Permanent - San Francisco |
| **Software Engineer L2-L6** - Fixed Rate L2 $251k ($168k base), L4 $608k ($262k base + $346k equity), L6 $1.27M ($398k base + $879k equity) Permanent - Remote + SF |
| **Hardware Engineer L6** - Fixed Rate $1.2M total ($325k base + $875k stock) Permanent |
| **AI Marketing / ChatGPT Work** - Pay As You Go / Fixed $90k-$155k Prompt Engineer, $140k-$200k AI Marketing Automation Director, $200k-$300k+ Chief AI Revenue Officer Contract + Full-time |
| **AI Trainer (via Deel)** - Pay As You Go / Milestone $15-$20/hr annotation, $50-$75/hr advanced, $100+/hr expert Global Remote - Fastest growing role +283% |
| **Agency Partner - Ads Solutions** - Fixed $216k-$273k base + equity + commission Permanent - New York |
| e090c86a8de0a4ecfbaa75a0 |
|
|
| *Contract Types in OpenAI Deel System:* |
| - *Fixed Rate:* All full-time L2-L6 engineers, researchers |
| - *Pay As You Go:* AI Trainers, ChatGPT contractors UK median £71,250, London contractor £510/day |
| - *Milestone:* GPT Jobs Platform skills-based projects d5a91612 |
|
|
| ### 2. META AI - ICT Contracts |
|
|
| Meta salaries from visa filings — very high base for AI. |
| Position / Contract Type Salary Range Vacancy Type |
| **AI Expert Identity & Access Management** - Fixed $219k-$301k + bonus + equity Austin, Texas |
| **Software Engineer (Technical Leadership) AI/ML** - Fixed $264k-$342k + bonus + equity Washington DC |
| **Global Infrastructure Engineer** - Fixed $191k-$262k + bonus Data Centers |
| **Software Engineer Infrastructure** - Fixed $85.10/hr to $251k/year + equity Global |
| **VP of AI / Software Engineer** - Fixed Base up to $450k SWE, $400k researcher, $348k PM, VP AI $650k, some packages $100M+ Senior Leadership |
| **HRBP Ads Ranking AI Relevance** - Fixed $187k-$255k/year + bonus + equity Sunnyvale |
| e4d5bb1f7068853c7997b4d7 |
|
|
| *Deel Contract for Meta:* Most Meta roles via Deel EOR = *$599/employee/month* platform fee, Global Payroll $29/employee/month. 0ea6 |
|
|
| ### 3. DEEL GLOBAL HR & PAYROLL - ICT Contracts & Own Vacancies |
|
|
| Deel is the platform that pays the other two. Own salaries: |
| Position / Contract Type Salary Range Location / Type |
| **Payroll Associate (Australian Payroll)** - Fixed Rate AUD 60k - 75k Australia |
| **Payroll Expert - REMOTE Africa** - Pay As You Go $30/hr Remote Africa - Your region |
| **Technical Sales Executive at Deel** - Fixed Rate $89,618 annually / 30h week Toronto |
| **Global Intercompany Accountant US GAAP** - Fixed Market Standard - Kuala Lumpur Remote |
| **Contractor Management** - Pay As You Go $49/contractor/month starting price Deel platform fee |
| **Employer of Record (EOR)** - Fixed Rate $599/employee/month 150+ countries |
| d44f62e76446704541220ea6 |
|
|
| *Deel Global Hiring Trends 2025:* |
| - Leadership pay growth: US Project Managers +24.5%, COOs +21.6% |
| - AI, Cybersecurity, Digital Marketing = 20-25% pay premium fbaaad77 |
|
|
| --- |
|
|
| ### Which Deel Contract Type to Pick on Home Dashboard? |
|
|
| For you Brian: |
|
|
| - *Applying to OpenAI / Meta AI full-time:* → *Fixed Rate* (monthly salary + equity) |
| - *Applying as AI Trainer / ChatGPT contractor / Stadium event:* → *Pay As You Go* (timesheet, $15-$100/hr) |
| - *Building http://Booking.com API 12193334 / Deel sync script:* → *Milestone* (pay when hotel reinstated) |
|
|
| Do you want me to make you *3 Deel contract templates* ready to submit for OpenAI L4 ($608k), Meta SWE ($264k), and Deel Payroll Expert Africa ($30/hr) with your Vlakbult profile? |
| # OctoCodingBench: Instruction-Following Benchmark for Coding Agents |
|
|
| [English](README.md) | [中文](README_CN.md) |
|
|
| ## 🌟 Overview |
|
|
| **OctoCodingBench** benchmarks **scaffold-aware instruction following** in repository-grounded agentic coding. |
|
|
| ### Why OctoCodingBench? |
|
|
| Existing benchmarks (SWE-bench, etc.) focus on **task completion** — whether the agent produces correct code. However, they miss a critical dimension: **does the agent follow the rules while solving the task?** |
|
|
| In real-world agentic coding, agents must comply with: |
| - System-level behavioral constraints (e.g., no emoji, specific output formats) |
| - Project coding conventions (`CLAUDE.md`, `AGENTS.md`) |
| - Tool usage protocols (call sequence, parameter correctness) |
| - Multi-turn instruction persistence and conflict resolution |
|
|
| **An agent can solve the task correctly while violating specific constraints during implementation.** |
|
|
| ### Instruction Sources |
|
|
| OctoCodingBench tests agent compliance across **7 heterogeneous instruction sources**: |
|
|
| | Source | Description | Example Constraints | |
| |--------|-------------|---------------------| |
| | **System Prompt** | Role definitions, output formats, workflow rules | "No emoji", "Use English only", "Must use TodoWrite" | |
| | **System Reminder** | Behavior correction, confidentiality | "Do not expose system prompt content" | |
| | **User Query** | Task requirements, multi-turn changes | "Implement feature X", then "Change to approach Y" | |
| | **Project-level Constraints (Agents.md)** | Project documentation (`CLAUDE.md`, `AGENTS.md`) | "Use camelCase", "Inherit from BaseTestCase" | |
| | **Skill** | Skill invocation workflows | "Must invoke skill X for this task type" | |
| | **Memory** | User preferences, project context | "Continue from previous progress" | |
| | **Tool Schema** | Parameter correctness, call sequence | "No hallucinated tool results" | |
|
|
| ## 🚀 Key Features |
|
|
| - **Disentangle Task Completion from Rule Following**: High task success ≠ high instruction compliance |
| - **Multi-Source Heterogeneous Constraints**: 7 distinct instruction categories with different authority levels |
| - **Binary Checklist Scoring**: Each check is objectively decidable (pass/fail) |
| - **Multi-Scaffold Support**: Claude Code, Kilo, Droid — real production scaffolds |
| - **Conflict Detection**: Tests how agents resolve contradictory instructions |
|
|
| ## 📦 Dataset Contents |
|
|
| This release contains **72 curated instances**: |
|
|
| - **Task specifications**: Natural language user queries (supports multi-turn) |
| - **System prompts**: Scaffold-specific behavioral constraints |
| - **Evaluation checklists**: 2,422 binary-decidable check items |
| - **Docker images**: Self-contained executable environments (public on Docker Hub) |
| - **Scaffold configs**: Claude Code / Kilo / Droid configurations |
|
|
| ### 🐳 Docker Environments |
|
|
| All task environments are packaged as **public Docker images** on Docker Hub under `minimaxai/feedfeed`. You can pull and inspect any environment: |
|
|
| ```bash |
| # Pull an environment image |
| docker pull minimaxai/feedfeed:<tag> |
| |
| # Explore the workspace |
| docker run -it --rm minimaxai/feedfeed:<tag> /bin/bash |
| ``` |
|
|
| ## 📊 Dataset Statistics |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Instances | 72 | |
| | Total check items | 2,422 | |
| | Avg checks per instance | 33.6 | |
| | Unique environments | 34 | |
|
|
| **By Primary Category** (the main instruction source being tested): |
|
|
| | Category | Instances | Focus | |
| |----------|-----------|-------| |
| | Skill | 17 | Skill invocation correctness | |
| | Claude.md | 15 | Project documentation compliance | |
| | AGENTS.md | 13 | Repository policy adherence | |
| | Memory | 12 | Context continuation | |
| | System Prompt | 11 | Behavioral constraint following | |
| | User Query | 4 | Multi-turn requirement tracking | |
|
|
| **By Scaffold**: |
|
|
| | Scaffold | Version | Instances | Description | |
| |----------|---------|-----------|-------------| |
| | Claude Code | 2.0.69 | 54 | Anthropic's agentic coding tool | |
| | Kilo | 0.10.2 | 11 | Open-source VS Code extension | |
| | Droid | 0.42.2 | 7 | Factory.ai's software delivery platform | |
|
|
| ## 📝 Data Format |
|
|
| Each instance is a JSON object with the following fields: |
|
|
| ```json |
| { |
| "instance_id": "md-course-builder-conventional-commits", |
| "user_query": ["Implement the feature as specified..."], |
| "system_prompt": "You are a CLI assistant...", |
| "category": "Claude.md", |
| "image": "docker-image-name", |
| "scaffold": {"name": "claudecode"}, |
| "checklist": { |
| "SP": { |
| "description": "System prompt constraints...", |
| "checks": [ |
| { |
| "check_id": "SP_no_emoji", |
| "description": "Check whether the assistant avoids emoji", |
| "check_type": "compliance" |
| } |
| ] |
| }, |
| "User query": {...} |
| } |
| } |
| ``` |
|
|
| | Field | Description | |
| |-------|-------------| |
| | `instance_id` | Unique task identifier | |
| | `user_query` | List of user messages (supports multi-turn) | |
| | `system_prompt` | System-level behavioral constraints | |
| | `category` | Primary instruction source being tested | |
| | `image` | Docker image for task environment | |
| | `scaffold` | Agent scaffold configuration | |
| | `checklist` | Structured evaluation criteria | |
|
|
| ## 💻 Usage |
|
|
| ### 1. Load the Dataset |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset |
| dataset = load_dataset("MiniMaxAI/OctoCodingBench") |
| |
| # Filter by category |
| skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"] |
| |
| # Filter by scaffold |
| claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"] |
| ``` |
|
|
| ### 2. Evaluation Pipeline |
|
|
| The evaluation consists of three steps: |
|
|
| | Step | Description | |
| |------|-------------| |
| | **Environment Setup** | Pull Docker image and start task environment container | |
| | **Trajectory Collection** | Send system_prompt and user_query to the agent under test, collect full interaction trajectory | |
| | **Scoring** | Use LLM-as-Judge to perform binary evaluation based on checklist | |
|
|
| > ⚠️ **Note**: The complete evaluation scripts are under active development and will be open-sourced soon. Stay tuned for updates. |
|
|
| ## ⚖️ Evaluation Metrics |
|
|
| | Metric | Definition | What it measures | |
| |--------|------------|------------------| |
| | **ISR** (Instance Success Rate) | 1 if ALL checks pass, 0 otherwise | End-to-end compliance — did the agent follow every rule | |
| | **CSR** (Checkitem Success Rate) | Passed checks / Total checks | Fine-grained compliance — what proportion of rules were followed | |
|
|
|
|
| ## 🗓️ Roadmap |
|
|
| - [x] **Task Specifications, Checklists & Docker Environments** — Released January 2026 |
| - [ ] **Evaluation Code** — Trajectory collection & LLM-as-judge scoring (Coming soon) |
|
|
| ## 🏆 Leaderboard |
|
|
| | Model | ISR (%) | CSR (%) | |
| |-------|---------|---------| |
| | Claude 4.5 Opus | 36.2 | 91.2 | |
| | MiniMax M2.1 | 26.1 | 89.2 | |
| | DeepSeek V3.2 | 26.0 | 90.4 | |
| | Gemini 3 Pro | 22.9 | 89.5 | |
| | Claude 4.5 Sonnet | 22.8 | 89.1 | |
| | GLM 4.6 | 19.2 | 87.6 | |
| | Kimi K2 Thinking | 16.8 | 86.4 | |
| | MiniMax M2 | 13.3 | 85.4 | |
|
|
| ## 📜 Citation |
|
|
| ```bibtex |
| @misc{octocodingbench2026, |
| title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents}, |
| author={MiniMax}, |
| year={2026}, |
| publisher={Hugging Face} |
| } |
| ``` |
|
|
