# Jatevo > Free inference-as-a-service via Jatevo.id — access Qwen 3.5 Plus, Kimi K2.5, and GLM 4.7 at zero cost. Use when you want free, high-quality LLM models for OpenClaw. Use when this capability is needed. - Skill: `tomevault-io/jatevo` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/jatevo` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jatevo/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/jatevo --- # Jatevo — Free LLM Inference for OpenClaw [Jatevo.id](https://jatevo.id) provides **free inference-as-a-service** for open-source LLMs. Zero cost, production-grade, hosted in Asia with international endpoints. ## Why Jatevo? - **$0 cost** — All models are completely free - **3 flagship models** — Qwen 3.5 Plus, Kimi K2.5, GLM 4.7 - **Blazing fast** — GLM 4.7 at 448 tokens/sec, Qwen 3.5+ at 50 tok/s - **OpenAI-compatible** — Drop-in replacement, works with any OpenAI SDK - **1M token context** — Qwen 3.5 Plus supports up to 1 million tokens - **Production-grade** — 32 concurrent requests tested, fully parallel ## Quick Setup (30 seconds) ### Option 1: Run the setup script (recommended) ```bash bash setup.sh ``` This will: 1. Ask for your Jatevo API key (get one free at [jatevo.id](https://jatevo.id)) 2. Patch your OpenClaw config with all available models 3. Optionally set Jatevo as your default model 4. Test the connection ### Option 2: Manual setup 1. **Get your API key** at [jatevo.id](https://jatevo.id) — sign up and generate a key (starts with `jk_`) 2. **Add to your OpenClaw config** (`~/.openclaw/openclaw.json`): Add this under `models.providers`: ```json { "models": { "mode": "merge", "providers": { "jatevo": { "baseUrl": "https://jatevo.id/api/open/v1/inference", "apiKey": "jk_your_key_here", "api": "openai-completions", "models": [ { "id": "qwen3.5-plus", "name": "Qwen 3.5 Plus", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 1000000, "maxTokens": 65536 }, { "id": "kimi-k2.5", "name": "Kimi K2.5", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 262144, "maxTokens": 32768 }, { "id": "glm-4.7", "name": "GLM 4.7", "reasoning": true, "input": ["text"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 128000, "maxTokens": 40000 } ] } } } } ``` 3. **Set your API key** as an environment variable: ```bash ## Replace "jk_your_key_here" above with your actual Jatevo API key ## The key is embedded directly in openclaw.json — no .env needed ``` 4. **Set as default model** (optional): ```json { "agents": { "defaults": { "model": { "primary": "jatevo/qwen3.5-plus" } } } } ``` 5. **Restart the gateway:** ```bash openclaw gateway restart ``` ## Available Models ### Qwen 3.5 Plus — `jatevo/qwen3.5-plus` The best all-rounder. Handles everything from general conversation to complex analysis. | Spec | Value | |------|-------| | Context Window | **1,000,000 tokens** (1M) | | Max Output | 65,536 tokens | | Speed | **~50 tokens/sec** | | Input Types | Text + Image (vision) | | Reasoning | ✅ Yes | | Best For | General purpose, long documents, vision tasks, daily driver | **Performance**: Benchmarked at ~95% of Claude Opus 4.6 quality. Handles 1M context effortlessly — feed it entire codebases, books, or document collections. Vision capability lets it analyze images, charts, and screenshots. **Speed breakdown** (tested March 2026): - Short prompts: 38 tok/s - Code generation: 65 tok/s - Long-form writing: 41 tok/s - Average across tasks: **50.7 tok/s** --- ### Kimi K2.5 — `jatevo/kimi-k2.5` Moonshot AI's reasoning powerhouse. Excels at step-by-step logic and complex problem solving. | Spec | Value | |------|-------| | Context Window | **262,144 tokens** (262K) | | Max Output | 32,768 tokens | | Speed | **~40 tokens/sec** | | Input Types | Text + Image (vision) | | Reasoning | ✅ Yes (strong) | | Best For | Reasoning, math, coding, structured analysis | **Performance**: Kimi K2.5 shines on tasks requiring chain-of-thought reasoning. Particularly strong at math, logic puzzles, and multi-step coding problems. Vision support for analyzing diagrams and technical drawings. **Speed breakdown** (tested March 2026): - Short prompts: 29 tok/s - Code generation: 42 tok/s - Long-form writing: 41 tok/s - Average across tasks: **39.5 tok/s** --- ### GLM 4.7 — `jatevo/glm-4.7` The speed demon. Zhipu AI's GLM family delivers the fastest inference of any model in this tier. | Spec | Value | |------|-------| | Context Window | **128,000 tokens** (128K) | | Max Output | 40,000 tokens | | Speed | **~448 tokens/sec** 🔥 | | Input Types | Text | | Reasoning | ✅ Yes | | Best For | Fast responses, bilingual (EN/ZH), batch processing, real-time apps | **Performance**: GLM 4.7 is **9x faster** than Qwen 3.5 Plus and **11x faster** than Kimi K2.5. Best bilingual model for Chinese and English content. Ideal for latency-sensitive applications, chatbots, and high-throughput batch processing. **Speed breakdown** (tested March 2026): - Short prompts: 327 tok/s - Code generation: 672 tok/s 🔥 - Long-form writing: 375 tok/s - Average across tasks: **448.3 tok/s** ## Model Comparison | | Qwen 3.5 Plus | Kimi K2.5 | GLM 4.7 | |---|---|---|---| | **Speed** | 50 tok/s | 40 tok/s | **448 tok/s** 🔥 | | **Context** | **1M tokens** | 262K | 128K | | **Max Output** | **65K** | 32K | 40K | | **Vision** | ✅ | ✅ | ❌ | | **Quality** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐½ | ⭐⭐⭐⭐ | | **Reasoning** | Strong | **Strongest** | Good | | **Speed Rank** | 3rd | 2nd* | **1st** | | **Cost** | Free | Free | Free | ### When to Use Which - **"I need the smartest model"** → `jatevo/qwen3.5-plus` - **"I need deep reasoning"** → `jatevo/kimi-k2.5` - **"I need it NOW"** → `jatevo/glm-4.7` (448 tok/s is near-instant) - **"I have a huge document"** → `jatevo/qwen3.5-plus` (1M context) - **"I need to analyze an image"** → `jatevo/qwen3.5-plus` or `jatevo/kimi-k2.5` - **"I'm building a chatbot"** → `jatevo/glm-4.7` (speed matters for UX) ## Aliases Add these to your OpenClaw config for quick model switching: ```json { "agents": { "defaults": { "models": { "jatevo/qwen3.5-plus": { "alias": "qwen" }, "jatevo/kimi-k2.5": { "alias": "kimi" }, "jatevo/glm-4.7": { "alias": "glm" } } } } } ``` Then switch models with `/model qwen`, `/model kimi`, or `/model glm`. ## Rate Limits - ~1,200 prompts per 5 hours (~4 req/min sustained) - Fully parallel — tested 32 concurrent requests successfully - No additional rate limits from Jatevo ## Troubleshooting - **"Invalid API key"** → Make sure your key starts with `jk_` - **Rate limited** → Wait a few minutes, the window resets on a rolling basis - **Model not found** → Run `openclaw models list` to verify the provider loaded - **Gateway didn't pick up changes** → Run `openclaw gateway restart` ## Support - Website: [jatevo.id](https://jatevo.id) - Questions: Open an issue or ask in the [OpenClaw Discord](https://discord.com/invite/clawd) --- > Source: [lucacadalora/jatevo-openclaw](https://github.com/lucacadalora/jatevo-openclaw) — distributed by [TomeVault](https://tomevault.io).