For most developers in 2026, the “best” AI model for coding is OpenAI’s GPT‑5 series (especially GPT‑5.2 Pro / Thinking), closely followed by Anthropic’s Claude Opus 4.5 / Sonnet 4.5 and Google’s Gemini 3 Pro.¹²³⁴

Top all‑round pick

• OpenAI GPT‑5.2 (Pro / Thinking)
  • Very strong at solving algorithmic problems, generating clean syntax‑correct code, and writing tests and docs.³¹
  • Works well via VS Code, Cursor, and other IDEs, so it fits into a typical developer workflow.²⁴

Best for reasoning and design

• Claude Opus 4.5 / Sonnet 4.5
  • Excellent at system‑design, large refactors, and keeping consistency across big codebases.⁴²
  • Good for tasks where you need deep reasoning, not just autocomplete.²⁴

Best for context‑heavy projects

• Google Gemini 3 Pro
  • Huge context window lets it “see” many files at once, so it’s strong for debugging interactions across large projects.³²
  • Works well for quick prototyping and when you want to upload entire directories or long logs.²³

If you want free or local

• Mistral, CodeGemma, DeepSeek‑Coder / Qwen‑Coder
  • Good for local/self‑hosted setups or budget‑constrained teams; they cover many languages and are strong on repetitive tasks.⁵¹³

Practical recommendation for you

Given your comfort with Python, JavaScript, and advanced tooling (VS Code, web, and cloud), you’re likely best served by:

• Primary model: GPT‑5.2 Pro (via VS Code / Cursor) for daily coding, debugging, and quick prototyping.¹⁴²
• Secondary model: Claude Opus 4.5 or Gemini 3 Pro for heavier design and refactoring work.⁴³²

If you tell your main stack (backend, web, data‑science, etc.), I can narrow this down to one best‑fit model and a concrete setup (IDE + extensions). ⁶⁷⁸⁹¹⁰

How to free use these models as longer complex coding require

You can use powerful coding models for free by mixing free tiers, standalone tools, and local‑run open‑source models—you just need to be strategic about when you use each.^11121314

1. Use free tiers of big models

Big vendors offer limited free usage that works well for long but not “all‑day” coding:

• GPT‑5 / GPT‑4.1‑based tools
  • Many IDEs and wrappers (Cursor, GitHub Copilot’s chat, Warp, etc.) give you free tiers or credits (e.g., hundreds of completions/chat requests per month).¹²
  • Example: GitHub Copilot free tier includes ~50 chat requests + ~2,000 completions/month without a credit card.¹²
• Claude models (Opus / Sonnet)
  • Some platforms (Warp, Kilo Code, Rovo Dev CLI, Amazon Q, etc.) bundle Claude Sonnet/Opus with free monthly credits or signup bonuses (e.g., 10–150 “fast” requests/month).¹²
• Gemini 3 Pro / 2.5 Pro
  • Google’s free tier of Gemini 3 Pro is very limited for deep reasoning (often only ~3–5 heavy prompts per day), but you can still use it for light help; Gemini 2.5 Pro is more generous in some tools.¹⁵¹²

Practical tip: rotate models (GPT‑5 → Claude → Gemini) across different tasks instead of hammering one, so you stay inside free limits.¹⁶¹¹

2. Join “unified” free portals

Several platforms let you switch between GPT‑5, Claude, Gemini, and others in one place for free, usually with a small monthly quota:

• Tools like Admix or similar “vault”‑style apps front‑end GPT‑5, Claude, Gemini, and others with free plans (often after a quick signup).¹⁷¹¹
• These are good for long‑form coding sessions where you can try one model until it throttles, then switch to another.¹¹¹⁷

3. Run local open‑source coding models (truly free)

For long, complex coding that you want to run without hitting API limits, install local coding‑optimized models:

Popular free models you can run on a decent laptop or cloud VM:

• CodeLlama 13B / 34B – strong pure‑code model, works well via Ollama or Hugging Face.¹³
• Mistral / Codestral‑22B – efficient code‑first models that fit on a single high‑end GPU.¹⁴¹³
• Qwen‑Coder / Qwen3‑Coder – 30B–480B MoE models with large context, good for repo‑level refactors.¹³¹⁴

Tools to run them:

• Ollama (simple, works on Linux/macOS/Windows):

ollama pull codellama:13b
ollama run codellama:13b

¹³

• LM Studio / Jan – GUIs for local models that can plug into VS Code or standalone editors.¹³

Downside: local models usually need at least 16–24 GB RAM / 8–24 GB VRAM for best performance, but they’re free to run forever and keep your code private.¹⁴¹³

4. Practical workflow for you (Python + web dev)

For your stack (Python, JS, web, some data‑science / GIS), you can:

• Daily coding:
  • Use free tiers of GPT‑5‑enabled IDEs (Cursor, VS Code with Copilot, etc.) for quick refactors, boilerplate, and debugging.¹⁶¹²
• Heavy design / refactors:
  • Save your “heavy” prompts for Claude Sonnet or Gemini 3 Pro when you get free credits, e.g., re‑architecting a module or writing a long spec.¹⁵¹⁶
• Long‑run / offline projects:
  • Run a local CodeLlama or Qwen‑Coder model for tasks that might blow cloud free limits (large‑file refactors, generating many functions, docs).¹⁴¹³

If you tell your setup

If you share your machine (RAM/VRAM, OS, whether you use VS Code / JetBrains), I can give you a step‑by‑step plan like:

• which model to install locally,
• which free web tools to use, and
• how to structure prompts so you don’t waste your limited free‑tier tokens on long coding tasks. ¹⁸¹⁹²⁰

how to structure prompts so I don’t waste my limited free‑tier tokens in different model. Also structure a crazy plan with tricks using smartly all free plan tire for a whole project

To avoid blowing your free‑tier tokens, you need both prompt discipline and a “free‑tier rotation” plan that lets you use GPT‑5, Claude, Gemini, and local models strategically across an entire project.^2122232425

Below is a two‑part answer:

1. How to structure prompts smartly
2. A “crazy but smart” free‑tier plan for a whole coding project.

1. How to structure prompts to save tokens

Use these rules for every model.

a) Set clear roles and constraints

Tell each model what it is and what it must not do:

• Header pattern:
  • “You are a senior Python/JS developer.
    • Task: [refactor / debug / design].
    • Constraints:
      • No long explanations unless I ask.
      • Output only code + minimal comments.
      • Respect PEP 8 / best practices.”²²²⁵

b) Split big tasks into small atomic prompts

Instead of:

“Help me build a whole API backend with auth, DB, and tests.”

Break it into:

1. “Design a high‑level architecture for a REST API in Python (FastAPI) with auth, Postgres, and tests.”
2. “Generate a minimal FastAPI main.py with a /users endpoint and auth stubs.”
3. “Now write only the auth middleware, no extra explanation.”

Smaller prompts = fewer tokens per request and less chance of model drifting off‑topic.²³²²

c) Re‑use contexts and “remember this”

Always keep your sessions short but consistent:

• Start each coding session with:
  • “Remember:
    • Project: [name]
    • Stack: Python 3.11, FastAPI, PostgreSQL, Docker.
    • Style: PEP 8, no comments unless necessary.”

Then, follow up with:

• “Continue from previous context, but only write the next file: auth.py.”

This keeps the model in the same “mental state” without re‑sending your whole codebase every time.²⁵²²

d) Use “return only code” mode

Add this line at the end of code‑focused prompts:

• “Return only code. Do not explain or repeat instructions.”

This cuts down ~30–50% of the token usage in each reply.²²²³

e) Let one model do “one job” well

Assign roles:

• GPT‑5 / GPT‑4.1 → fast code completion, small refactors, Python/JS boilerplate.²³²²
• Claude Opus/Sonnet → design, big refactors, docs, and tests.²⁶²²
• Gemini 3/2.5 Pro → quick debugging, log analysis, or lighter design.²⁷²²

This way you don’t waste your best‑quality model tokens on trivial tasks (like “convert nested loop to list comprehension”).

2. “Crazy but smart” free‑tier plan for a whole project

Assume:

• You have free tiers of GPT‑5, Claude, Gemini, and one local model (e.g., CodeLlama or Qwen‑Coder).^24212523
• You’re building a medium‑size backend (e.g., Python API + tests + Docker + some frontend glue).

Phase 0: Setup and token‑budgeting

• Decide:
  • “I’ll spend ~20% of my free tokens on GPT‑5, 30% on Claude, 30% on Gemini, 20% on local models.”²¹²³
• Always:
  • Use local model for heavy‑rework tasks (multi‑file refactors, docs, bulk generation).
  • Use cloud models for high‑quality, short‑cycle tasks (quick fixes, small features).

Phase 1: Project scaffolding (use GPT‑5)

Goal: Get structure fast, under tight token budget.

Steps:

1. Architecture prompt (1 long prompt)
   • “Design a Python FastAPI project with auth, PostgreSQL, async, Docker, and tests.
     • Output: directory structure as JSON, plus a short README.md sketch.”
   • You paste this once; re‑use it as a “single source of truth.”
2. Generate skeleton files (small prompts)
   • “Based on that structure, write only main.py for a minimal API with /health.”
   • Then: “Now write only models.py for a User model (FastAPI + SQLAlchemy).”
3. Use GPT‑5 for “first passes” Each file is one small prompt; review and trim its output locally.²²²³

Token‑saving trick:

• After each file, copy‑paste only the changed lines into your local repo, then delete the chat history and start fresh in the next model to avoid context bloat.

Phase 2: Heavy design and refactoring (use Claude Sonnet)

Goal: Create a solid, maintainable design and large‑scale refactors.

How to use Claude:

1. Feed it only the important parts
   • Paste only:
     • README.md choices.
     • Core modules (auth.py, models.py, main.py).
   • Prompt:
     • “Analyze this API design.
       • Suggest 3 refactors to improve maintainability and testability.
       • Return only bullet points and file names to change.”
2. Let Claude do “one refactor at a time”
   • Prompt:
     • “Now, refactor only auth.py to be modular and testable.
       • Return only updated auth.py code.”
3. Use it for docs and tests plans
   • “From this project, write a high‑level test plan and a short Sphinx doc structure.”

This keeps expensive Claude tokens focused on high‑value design, not trivial edits.²⁶²²

Phase 3: Debugging and small‑scale fixes (use Gemini)

Goal: Use Gemini as your “cheap” debugger and quick‑fix worker.

Strategy:

• Logs and errors
  • Paste only the error stack trace plus a few surrounding lines.
  • Prompt:
    • “Here’s an error in my FastAPI app.
      • What is the most likely cause and a minimal fix?”
• Micro‑tasks only
  • “Fix only this SQL query in models.py to support async.”
  • “Change all snake_case variable names to kebab_case in this function.”

Avoid using Gemini for full‑file rewrites; save that for Claude or your local model.²⁷²²

Phase 4: Long‑running / heavy work (use local models)

Goal: Do bulk, multi‑file, or repetitive work that would burn through cloud free tiers.

Examples:

• Bulk documentation
  • Local model prompt:
    • “Annotate each function in auth.py with a short docstring.”
• Multi‑file refactors
  • “Rewrite all PostgreSQL‑related functions to use async SQLAlchemy.”
• Code migration
  • “Convert this old Flask‑style API to FastAPI‑style endpoints.”

Because you run this locally, you can make as many iterative prompts as needed without worrying about quotas.²⁴²⁵

Phase 5: Final polish and review (rotate models)

• Ask each model to review the same final file, but with different focus:
  • GPT‑5: “Review this Python file for bugs and style issues. Return only line numbers and fixes.”
  • Claude: “Review this file for maintainability and suggest 2–3 improvements.”
  • Gemini: “Find any obvious performance issues or slow queries.”

This spreads your final‑review cost across multiple free tiers instead of overloading one.²³²²

3. Extra “tricks” to stretch free tiers

• Cache and reuse:
  • Save model outputs in a notes.md or model‑output/ folder.
  • When you switch models, paste the last 5–10 lines of that file, not the entire project.²⁵
• Use “diff” style prompts
  • Instead of pasting full files:
    • “Here’s the diff of my auth.py (I added these lines):

...  

        - Suggest improvements to only the new code.”

• Work offline first
  • Write your own first draft, then ask the AI to “fix” or “polish” it instead of generating from scratch; this massively reduces token usage.²⁵²²

What to do next for your setup

If you tell:

• your OS, RAM/VRAM,
• which IDE you use (VS Code / JetBrains), and
• your current project (Python API, web app, GIS tool, etc.),

I can give you a concrete “token‑budget sheet” plus a step‑by‑step prompt‑template set tailored for your exact stack and hardware, so you can execute this “crazy plan” as a real workflow.

Which Ai offers delete specific chat. I can’t see options to remove immediate chat.

Also tell me how can I use ollama cloud in mobile?

Several AI services let you delete specific chats, and you can absolutely use Ollama Cloud / local‑hosted Ollama models on mobile with a few tweaks.^28293031

1. Which AI lets you delete specific chats?

Most major AI chat platforms now let you delete individual threads/conversations, not just “clear all”.

Perplexity (this app)

• You can delete specific threads but not “one line” inside a chat; you delete the whole thread.
  • Go to Library at https://www.perplexity.ai/library.
  • Click the three‑dots menu next to any thread and choose “Delete thread”.³¹³²
  • You can also delete all threads at once from the top‑right menu.³¹

ChatGPT / OpenAI

• In the web or desktop app:
  • Hover over a chat in the sidebar, click the three dots, and choose “Delete”.³¹
• You cannot delete a single message inside a chat; only the whole thread.

Claude (Anthropic)

• On the web app:
  • Open the History sidebar, click the three dots next to a project / chat, and select “Delete”.³³

Gemini (Google)

• In the Gemini web or mobile app:
  • Long‑tap a chat (mobile) or click the three dots (web) and choose “Delete”.³⁴

If you tell me which specific AI app you’re using (e.g., “the Perplexity mobile app” or “Claude Android”), I can give exact taps for that UI.

2. How to use Ollama Cloud / Ollama‑hosted models on mobile

There are two main ways:

Option A: Use Ollama Cloud via a mobile client (Android/iOS)

Ollama Cloud itself is accessed via the Ollama API (/api/generate, /api/chat), so you connect to it from a mobile app that supports Ollama servers.³⁵²⁸

Popular choices:

• Off Grid (Android & iOS)
  • Lets you scan and connect to a local or cloud Ollama server on your network.
  • Steps:

1. Run Ollama on your PC/laptop (or self‑hosted server).
2. On your phone, open Off Grid → Remote Models → Scan Network.
3. Tap the detected Ollama server and start chatting.²⁹

• LMSA for LM Studio & Ollama (Android)
  • Supports Ollama servers and also cloud APIs like OpenRouter.
  • Steps:

1. Enable Ollama’s local server and allow network access (OLLAMA_HOST=0.0.0.0:11434).³⁰
2. In LMSA, add your server IP (http://YOUR_IP:11434) and pick a model.³⁶

• Third‑party Ollama‑style apps
  • Apps like “MyOllama” or other open‑source clients let you connect to an Ollama server and talk to CodeLlama, Qwen‑Coder, etc. directly from your phone.³⁷

Option B: Use a web UI on mobile (no install)

• Run Open WebUI or similar on your PC/server, pointing at Ollama.
• Access it from your phone browser at http://YOUR_IP:port (same network) or via a secure tunnel (e.g., Pinggy, Cloudflare Tunnel).³⁰

This is the “cheapest” way if you already have a laptop running Ollama at home.

Concrete mobile setup example (you + Ollama)

If you’re on Android and already run Ollama on your laptop:

1. On laptop:
   • Ensure Ollama listens on the network (e.g., OLLAMA_HOST=0.0.0.0:11434).³⁰
2. On Android:
   • Install Off Grid from GitHub Releases or the Play Store.
   • Open Remote Models → Scan Network → pick your Ollama machine.
   • Select a coding model (e.g., codellama:13b, qwen:32b) and start chatting.²⁹

If you tell your OS (Android/iOS) and whether Ollama is on laptop vs cloud server, I can give you a step‑by‑step command + exact settings for your exact setup. ^38394041

Are there any way in online/web I can run ollama free

Yes, there are ways to use Ollama‑style models for free on the web, but you cannot fully “run Ollama” in the browser the same way you run it locally—instead, you either:

• Use Ollama‑compatible web UIs that talk to your own Ollama server, or
• Use platforms that integrate Ollama‑compatible models and let you interact with them via a web UI.

1. Free Ollama‑compatible web UIs (you host, you browse)

These are tools that let you browse Ollama models in a browser, but you still need Ollama running somewhere (laptop, home server, or cheap cloud VM):

• Open WebUI
  • A popular web‑based UI that connects to your Ollama instance and lets you chat like ChatGPT.
  • You install it once (Docker or native) and then open it in your browser at http://localhost:3000.
  • Works free forever; you only pay for the VM if you host it in the cloud.
• Ollama UI / “LlamaPen”‑style lightweight web GUIs
  • Some folks have built simple no‑install‑needed web‑based GUIs that connect to a running Ollama server.
  • You run Ollama on your machine, enable network access, then open the web UI in your phone browser and use it anywhere.

These are free to run, but the compute cost only vanishes if you run Ollama on your own hardware.

2. Ollama‑style cloud models (limited free tier)

Ollama now offers cloud‑hosted models that you can use via their API or some integrated tools:

• Ollama Cloud models
  • You can log in to Ollama and use a few cloud‑hosted models for free (limited usage).
  • Heavier usage or many requests will push you into paid territory.
  • You can access these via CLI or connect them to tools like Open WebUI or Lobe Chat so they feel like a web app.
• Third‑party “free‑tier‑first” platforms
  • Some tools let you wire Ollama‑compatible models (including Ollama Cloud) and give you a web UI on top, with a free tier.
  • Examples include certain LLM‑frontend dashboards that support Ollama‑style backends.

3. Truly “free run Ollama” in a browser (no install)?

At the moment there’s no mainstream, fully‑hosted “Ollama in the browser” where you get free heavy GPU usage forever. What you can do:

• Spin up a cheap cloud VM (e.g., $5–10/month) with Ollama + Open WebUI, then use it via your phone or laptop browser; effectively “online Ollama” for coding.
• Use Ollama‑fronted web apps that expose a small number of models for free, but usually with rate limits.

Practical path for you (free‑focused)

Given you’re comfortable with Python and cloud tools:

1. Option A – DIY “online Ollama”:
   • Rent a cheap Linux VM, install Ollama + Open WebUI, then access it from your browser like a web app.
   • Run CodeLlama / Qwen‑Coder / Mistral for free (you pay only for the VM).
2. Option B – Use Ollama‑fronted web tools:
   • Pick a free‑tier‑friendly Ollama‑compatible web UI (e.g., Open WebUI or Lobe Chat) and connect it to your Ollama instance or Ollama Cloud.

If you tell your preferred cloud (e.g., AWS, DigitalOcean, or home‑laptop‑only), I can give you a step‑by‑step command‑line setup tailored to your hardware and budget. ^{42434445464748495051}

Can I host ollama in GitHub and run in mobile freely, or already exist such feature?

Short answer: You cannot host Ollama “on GitHub Pages” (the static site host), but you can run Ollama inside a GitHub Codespace and then access it from your mobile browser—effectively a “free (within limits) online Ollama you can use from phone”.^52535455

There is no official “Ollama on GitHub” button that gives you a ready‑made mobile‑friendly web UI for free forever, but you can build this pattern yourself.

1. Why you can’t put Ollama on GitHub Pages

• GitHub Pages is for static sites only (HTML, JS, CSS, no backend).⁵²
• Ollama is a server process that needs CPU/GPU, RAM, and a persistent runtime; Pages can’t run that.⁵²

So you cannot host Ollama itself directly on GitHub Pages and expect it to run models there.

2. What you can do: Ollama + GitHub Codespace + mobile

You can:

1. Run Ollama inside a GitHub Codespace (cloud‑based dev environment)
   • GitHub Codespaces give you a Linux VM with CPU/RAM and a VS Code–like editor in the browser.⁵³⁵⁴
   • You can install Ollama in the Codespace and run models like llama3‑8b, phi‑3, etc. inside it.⁵⁴⁵³
2. Expose it as a web endpoint
   • Install a simple web UI (e.g., Open WebUI or a minimal chat server) in the Codespace.⁵⁶⁵⁷
   • GitHub Codespace provides a public URL (e.g., your‑codespace‑xxx-443.github.dev), which you can open on your phone browser.⁵⁵⁵⁴
3. Connect your phone to that URL
   • From your Android/iOS browser, go to the Codespace URL → open the web UI → chat with Ollama, just like a web app.⁵⁴⁵⁵

Limits:

• GitHub’s free Codespace tier has limited hours and compute; heavy Ollama usage will either exhaust your free tier or push you into paid.⁵⁵⁵⁴
• You run small‑to‑medium models (7B–13B) best; big 30B+ models will be slow or fail in Codespaces.⁵³⁵⁴

3. “Already exist such feature?” (community workarounds)

• GitHub Codespace + Ollama + web UI
  • Guides like “Run Ollama in GitHub Codespace” and community repos (Ollama‑in‑GitHub‑Codespaces) show exactly how to set this up.⁵³⁵⁴⁵⁵
• Distributed Ollama APIs
  • Projects like OllamaFreeAPI provide a free, community‑run API gateway to Ollama‑style models, but this is not “hosted on GitHub” and still has usage limits.⁵⁸

4. Practical “free mobile‑friendly” flow for you

If you want to use Ollama freely from mobile via GitHub‑adjacent tech:

1. Create a repo on GitHub and enable Codespaces.
2. In the Codespace .devcontainer.json, add the Ollama feature so it installs on startup.⁵⁴⁵³
3. Run:

ollama run llama3:8b

and optionally install a web UI (e.g., Open WebUI) that listens on a port. 4. Open the Codespace URL in your phone browser and use the web UI as your “mobile Ollama client”.

If you tell me:

• your OS (Windows/Linux/macOS) and
• whether you already use GitHub Codespaces,

I can give you a copy‑paste commands + devcontainer.json snippet so you can spin this up in under 10 minutes and access it from your phone. ^59606162

Free alternatives to host Ollama for mobile access

Yes—there are several free‑tier ways to host Ollama and access it from your mobile browser without paying for a full‑time VPS. Here are the best options:

1. Free cloud VPS / “Always Free” tiers

These give you a real Linux server where you install Ollama + a web UI and then open it in your phone’s browser.

• Oracle Cloud “Always Free” ARM VM
  • 4–6 ARM cores, 24 GB RAM, 200 GB storage, free forever if you don’t exceed limits.
  • Perfect for 7B–13B models (CodeLlama, Qwen‑Coder, Mistral) with Ollama + Open WebUI.⁶³
  • Steps:

1. Create an Oracle Cloud account and launch an Always‑Free ARM VM.
2. Install Ollama and a web UI (Open WebUI / Lobe Chat).
3. Open the server’s IP or domain in your phone browser.

• Railway.app
  • Gives new users $5 free monthly credits, enough to run a small Ollama‑style app or Ollama + web UI for light usage.⁶⁴
  • Good if you want a managed Docker‑style setup rather than managing a raw VM.
• Other “free‑trial‑first” hosts (Kamatera, Koyeb, etc.)
  • Services like Koyeb and Kamatera offer free credits or short‑term trials (e.g., up to $200 in credit).⁶⁵⁶⁶
  • You can deploy Ollama as a one‑click app or custom service and stop it when you hit your free cap.

2. Developer‑cloud free tiers (GitHub‑adjacent)

These are not “Ollama as a service” but places where you can run Ollama‑compatible setups for free within limits:

• GitHub Codespaces
  • Free‑tier credits let you spin up a Linux VM in the browser, install Ollama, and run 7B–13B models for light work.
  • You can then expose a web UI (e.g., Open WebUI) and access it from your phone via the Codespace URL.⁶⁷⁶⁸
• Fly.io / Heroku‑style platforms with free tiers
  • Some platforms let you deploy Ollama‑compatible backends (often via Docker) and give you a free subdomain.
  • Once running, you can open that domain in your phone’s browser like a web chat app.

3. “Free” community‑run Ollama‑style APIs

These are not Ollama per se, but Ollama‑compatible ways to get models for free on mobile:

• OllamaFreeAPI
  • Community‑run distributed API that lets you query Ollama‑style models without paying for infrastructure yourself.⁶⁹
  • You connect a mobile client (e.g., Off Grid, LMSA, or your own web UI) to this API instead of a self‑hosted server.
• Other Ollama‑fronted hubs
  • A few platforms aggregate open‑source models behind an Ollama‑compatible API and offer a free tier for light usage.⁷⁰⁶⁴

4. Local‑hosted + “free” mobile access

You can also run Ollama on your own hardware and make it reachable from your phone:

• Home‑laptop or mini‑PC
  • Install Ollama + Open WebUI, enable network access (OLLAMA_HOST=0.0.0.0:11434), and open http://your‑laptop‑ip:3000 from your phone on the same Wi‑Fi.⁷¹⁷²
• Use a secure tunnel (e.g., Pinggy, Cloudflare Tunnel)
  • Forward your local Ollama‑web‑UI to a public URL, then open that URL from anywhere on your phone.⁷⁰⁷¹

Simple “free‑mobile” recommendation for you

Given you’re comfortable with Linux and cloud tools, the cheapest and most “free” path is:

1. Pick a free‑tier host:
   • Oracle Cloud Always Free (long‑term free) or Railway (low‑cost, easy).⁶³⁶⁴
2. Deploy:
   • Ollama + Open WebUI (or similar web UI) on that machine.⁷³⁶³
3. Access from mobile:
   • Open the server IP/domain in your phone’s browser; use it as a mobile‑friendly Ollama chat app.⁶⁴⁶³

If you tell me:

• which host you prefer (Oracle / Railway / GitHub Codespaces / home‑PC), I can give you a step‑by‑step command set (what to run on the server and what to type in your phone) so you can be up and running in 10–15 minutes. ^7475767778

If I HOST OLLAMA IN my office pc, how can I access it at home in pc/mobile?

Yes—you can host Ollama on your office PC and securely access it from home on your PC and mobile. There are two main safe patterns: same‑network tunneling and public‑accessible‑only‑via‑tunnel.

1. Basic setup on your office PC

1. Make Ollama listen on the network
   • Set the environment variable:
     • Windows:

set OLLAMA_HOST=0.0.0.0
ollama serve

    - Linux/macOS:

export OLLAMA_HOST=0.0.0.0
ollama serve

- Now Ollama listens on `0.0.0.0:11434` instead of just `127.0.0.1`.[^8_1][^8_2][^8_3]

2. Optionally add a web UI
   • Install Open WebUI or Lobe Chat on the same machine and point it to http://your‑office‑ip:11434; it will act as a nice chat frontend.⁷⁹⁸⁰

2. Access from home (PC + mobile)

Option A: Via a secure tunnel (easiest and safe)

You don’t expose Ollama directly to the internet; instead you use a tunnel service that forwards your office 11434 to a public URL:

• Tools like Pinggy, Cloudflare Tunnel, or localtunnel
  • Example with Pinggy (free tier exists):⁸¹⁷⁹

1. On your office PC (behind any firewall), run:

ssh -R 0:localhost:11434 qr@free.pinggy.io

2. Pinggy gives you a public URL (like https://xxxxx.pinggy.io).
3. From home: - On PC: open https://xxxxx.pinggy.io → use Ollama API or web UI at that URL. - On mobile: open the same URL in your phone browser or an Ollama‑compatible app (e.g., Off Grid, LMSA).⁸²⁸¹

This is the safest option because:

• Your office PC is not exposed to the open internet.
• Only the tunnel endpoint is public.

Option B: Port‑forwarding your router (more advanced, riskier)

If your office network allows it, you can expose Ollama more directly:

1. In your office router, set up port forwarding:
   • External port: 11434 → Internal IP of your office PC: 11434.⁸³⁷⁹
2. From home, access:
   • http://YOUR_OFFICE_PUBLIC_IP:11434 (API) or http://YOUR_OFFICE_PUBLIC_IP:3000 if you run Open WebUI on port 3000.⁸⁴⁷⁹

Security warnings:

• This exposes Ollama to the entire internet unless you add:
  • A reverse proxy (e.g., Nginx) with basic auth or API‑key protection.⁸⁵⁸⁶
  • A VPN or IP‑whitelist (e.g., Cloudflare Access or Tailscale) so only your home IP can reach it.⁸⁶⁸⁵

3. Practical workflow for you (safe + simple)

Given your tech comfort, here’s a recommended setup:

• At the office:
  • Install Ollama + Open WebUI on your office PC.
  • Run Ollama with OLLAMA_HOST=0.0.0.0 and keep it on your internal network.⁸⁷⁸⁴
• At home:
  • Use Pinggy (or similar tunnel) from your office PC so it gives you a public URL.⁸⁸⁸¹
  • On your home PC / laptop: open https://xxxxx.pinggy.io like a normal web app.
  • On your mobile:
    • Install an Ollama‑compatible client (e.g., Off Grid or LMSA) and point it to the Pinggy URL.⁸⁹⁸²

If you tell me:

• your OS at the office (Windows/Linux) and
• whether you can install SSH‑style tunnels on that machine, I can give you the exact commands for both the office PC and your home/mobile side so you can be up and running in 10 minutes. ⁹⁰⁹¹

Pinggy free tire 60 minutes tunnel timeout, are there alternatives

Yes, there are several free tunnel alternatives that either give you longer‑lived or persistent free tunnels than Pinggy’s 60‑minute limit, or are free‑forever but with small trade‑offs.⁹²⁹³⁹⁴

Here are the best options for keeping your Ollama‑on‑office‑PC tunnel active longer.

1. Cloudflare Tunnel (free + “no time limit” feel)

• Pros:
  • Free tier has no hard time limit (ephemeral or named tunnels can stay up as long as you run cloudflared on your office PC).
  • High‑performance global network, HTTPS by default, good for serving Ollama + a web UI.⁹⁵⁹³⁹²
• Setup:
  • Install cloudflared on your office PC.
  • Run something like:

cloudflared tunnel --hostname myollama.your‑domain.com --url http://localhost:11434

- You access `https://myollama.your‑domain.com` from home on PC/mobile.[^9_4][^9_1]

This is one of the strongest free‑forever options if you own a domain (or can use a free one via Cloudflare).

2. Localtunnel (free, simple, short‑lived)

• Pros:
  • Very simple: npx localtunnel --port 11434 and it assigns a public URL.
  • No account or payment required.⁹³⁹⁴
• Limit:
  • URLs are ephemeral; the tunnel dies when you stop the process or your PC reboots.
  • Not ideal for “always on”, but great for sporadic remote Ollama access.

3. Serveo (free, no‑setup style)

• Serveo is a free SSH‑based tunnel like Pinggy, with no signup and no hard 60‑min cap in the free tier (just resource‑based eviction).⁹⁴⁹³
• Example:

ssh -R 80:localhost:11434 serveo.net

- Gives you a public URL you can open in your phone browser.

• Trade‑off: less stable than Pinggy/Cloudflare, and may be taken down or throttled if heavily used.⁹⁴

4. Tunnelmole (free + DIY‑friendly)

• Tunnelmole is a self‑hostable tunnel server; you can run it yourself on a cheap VM and point your office PC at it.
• The public instance has a free tier with reasonable uptime, and you can build your own “forever free” setup if you rent a minimal VPS.⁹⁶⁹⁴

5. Self‑hosted “tunnel gateways” (Zrok, frp, etc.)

• Tools like Zrok or frp let you deploy a tunnel gateway on a cheap VM (or even always‑free Oracle), then connect your office Ollama to it.⁹⁶⁹⁴
• You get no time‑out limit as long as the gateway is up.
• Best if you’re comfortable with Linux + Docker/VMs and want a true “forever‑free” tunnel stack.⁹⁴⁹⁶

Practical recommendation for you

For your use case (Ollama on office PC → PC + mobile at home):

• Best “free & stable”:
  • Use Cloudflare Tunnel if you can configure a domain (even a cheap/free one). It avoids Pinggy’s 60‑min limit and still stays free.⁹²⁹³⁹⁵
• If you don’t want a domain + need simple:
  • Use Serveo or Localtunnel for on‑demand Ollama access from home.⁹³⁹⁴

If you tell me:

• whether you have a domain (even a cheap .ml / free CF‑hosted one) and
• your OS at the office (Windows/Linux),

I can give you exact commands for Cloudflare Tunnel or Serveo‑style setup so you can keep your Ollama tunnel alive much longer than 60 minutes. ^979899100101

How to set up Cloudflare Tunnel for Ollama access

Here’s a step‑by‑step plan to set up Cloudflare Tunnel so you can access your Ollama server (on your office PC) securely from home on PC and mobile, via HTTPS.^102103104

Assumptions:

• Ollama is already running on your office PC at http://localhost:11434.
• You have a Cloudflare account and a domain (e.g., yourdomain.com).

1. Set up Ollama to listen on the network

On your office PC:

• Set Ollama to listen on all interfaces:
  • Linux/macOS:

export OLLAMA_HOST=0.0.0.0
ollama serve

- Windows:

set OLLAMA_HOST=0.0.0.0
ollama serve

• Ollama now accepts connections on http://127.0.0.1:11434 and http://YOUR_OFFICE_IP:11434.¹⁰⁵¹⁰⁶

2. Install cloudflared on your office PC

1. Download cloudflared
   • Go to: https://github.com/cloudflare/cloudflared/releases
   • Pick the binary for your OS (e.g., cloudflared-windows-amd64.exe, cloudflared-linux‑amd64).¹⁰³¹⁰²
2. Put it in PATH / a folder
   • Example on Linux:

sudo install cloudflared-linux-amd64 /usr/local/bin/cloudflared

- On Windows: place `cloudflared.exe` in a folder like `C:\cloudflared` and add that folder to your PATH.[^10_1]

3. Authenticate cloudflared with your domain

Run this on your office PC once:

cloudflared tunnel login

• A browser window will open; choose the domain (e.g., yourdomain.com) you want to use.
• cloudflared saves a credentials file (e.g., ~/.cloudflared/cert.pem).¹⁰³¹⁰²

4. Create a tunnel for Ollama

Create a named tunnel (e.g., ollama-tunnel):

cloudflared tunnel create ollama-tunnel

• This generates a ollama-tunnel.json file with an ID.¹⁰⁴¹⁰²

Now configure it:

1. Create a config file (e.g., ~/.cloudflared/config.yaml or C:\Users\YourUser\.cloudflared\config.yaml):

tunnel: ollama-tunnel
credentials-file: C:\Users\YourUser\.cloudflared\ollama-tunnel.json
ingress:
  - hostname: ollama.yourdomain.com
    service: http://localhost:11434
  - service: http_status:404

- `ollama.yourdomain.com` is the public URL you’ll use.
- `http://localhost:11434` is your Ollama API.[^10_1][^10_2]

2. Add DNS record in Cloudflare
   • In Cloudflare dashboard → DNS → add:
     • Type: CNAME
     • Name: ollama
     • Target: (Cloudflare will show a random ID like xxxxx.cfargotunnel.com after you create the tunnel.)
   • Or let cloudflared auto‑configure DNS:

cloudflared tunnel route dns ollama-tunnel ollama.yourdomain.com

¹⁰³¹⁰²

5. Start the tunnel (keep it running)

On your office PC:

cloudflared tunnel run ollama-tunnel

• This keeps the tunnel open as long as the process runs.
• For “always‑on”, you can turn it into a system service (Windows service or Linux systemd).¹⁰⁴¹⁰²

6. Access Ollama from home (PC + mobile)

Once the tunnel is up:

• From home PC:
  • Open: https://ollama.yourdomain.com/v1/models or https://ollama.yourdomain.com (if you also run Open WebUI there).
• From mobile:
  • In your browser, open https://ollama.yourdomain.com and use it like a web app, or
  • In an Ollama‑compatible mobile app (e.g., Off Grid, LMSA), set the server URL to https://ollama.yourdomain.com.¹⁰⁷¹⁰⁸

7. Optional: Add a web UI in front of Ollama

If you want a nicer chat interface:

1. Run Open WebUI (or similar) on your office PC, pointed to http://localhost:11434.¹⁰⁹
2. Expose the Open WebUI port (e.g., http://localhost:3000) via Cloudflare Tunnel instead, or alongside the Ollama API:

ingress:
  - hostname: ollama-ui.yourdomain.com
    service: http://localhost:3000
  - service: http_status:404

Then on mobile you just open https://ollama-ui.yourdomain.com and chat as if it were a hosted AI app.¹⁰⁹¹⁰²

If you share your OS (Windows/Linux) and domain, I can:

• Give you exact commands (including how to create a Windows service or Linux systemd unit) so the tunnel starts automatically on boot and stays up 24/7.

Instead local pc, can I host ollama at cloudfare freely?

Short answer: you cannot run Ollama itself on Cloudflare for free, but you can use Cloudflare‑hosted AI models (Workers AI) and Cloudflare Tunnel in a free tier so it feels like you’re hosting an AI backend “in Cloudflare” while Ollama stays on your own machine.^110111112113

Below I’ll split this for you:

1. Why “host Ollama on Cloudflare” isn’t possible

• Cloudflare Workers run serverless JavaScript (or WASM), not full OS‑level processes like Ollama.
  • You cannot ssh into a Cloudflare machine, install CUDA, or run ollama serve.¹¹¹¹¹⁰
• Cloudflare Workers AI is a managed LLM API (Llama 3, 4, etc.), not a place where you install Ollama yourself.¹¹⁰¹¹¹

So you cannot simply “move Ollama to Cloudflare” the way you could move it to a VPS.

2. What you can do for free

A) Use Cloudflare Workers AI as your “cloud LLM”

• Cloudflare Workers AI lets you call large models (e.g., Llama 3, 4 Scout, etc.) from a Worker.¹¹¹¹¹⁰
• You can:
  • Write a small Worker that exposes an OpenAI‑style API endpoint.
  • From your PC or mobile app, call that Worker URL as if it were an LLM backend.¹¹⁴¹¹⁰

The free tier of Cloudflare includes:

• Free SSL, CDN, DDoS protection, and a limited free quota for Workers AI (a few hundred‑thousand tokens per month, details in Cloudflare’s docs).¹¹³¹¹⁰

B) Use Cloudflare Tunnel to expose your real Ollama

• You still run Ollama on your PC or home server, but you expose it via Cloudflare Tunnel so it’s reachable from anywhere over https://ollama.yourdomain.com.¹¹²¹¹⁵
• The Cloudflare Tunnel piece is free (you only pay if you exceed Workers CPU/request limits at very high scale).¹¹⁶¹¹³

This is the closest to “hosting Ollama in Cloudflare freely”:

• Ollama runs on your hardware.
• Cloudflare Tunnel gives you a free, secure HTTPS endpoint.

3. Concrete “free‑ish” setup for your use case

Given you want to access Ollama from mobile:

1. Keep Ollama on your office PC
   • export OLLAMA_HOST=0.0.0.0 && ollama serve.¹¹⁷¹¹⁸
2. Expose it via Cloudflare Tunnel
   • Set up cloudflared and tunnel localhost:11434 to ollama.yourdomain.com (exact steps in the previous reply).¹¹⁵¹¹²
3. (Optional) Replace Ollama with Workers AI for some tasks
   • If you don’t strictly need Ollama, you can:
     • Write a Cloudflare Worker that calls @llama‑3 or @llama‑4 via Workers AI.
     • Use that URL in your mobile app instead of an Ollama‑hosted one.¹¹⁰¹¹¹

4. If you tell me

• your domain (or whether you’re okay buying a cheap one),
• and whether you prefer true Ollama on your PC vs a Cloudflare‑only API,

I can give you:

• two copy‑paste examples:
  • One worker.js for a Cloudflare‑only LLM API (Workers AI), and
  • One config.yaml for Cloudflare Tunnel + Ollama so you can seamlessly switch between them. ^119120121

What is the limit of free tire for cloudfare tunnel for ollama?

Cloudflare Tunnel (via cloudflared) is free to use for exposing services like Ollama, but the real limits come from Cloudflare’s overall free plan, bandwidth, and CPU usage, not from Tunnel itself.^122123124

Here’s how it breaks down for your use case:

1. Cloudflare Tunnel itself (no hard “tunnel” limit)

• Cloudflare Tunnel (the cloudflared tunnel component) has no explicit per‑tunnel cost or timeout in the free tier.¹²³¹²⁴
• You can run cloudflared tunnel run ollama-tunnel 24/7 and it stays up as long as your PC/VM is up and within Cloudflare’s broader free limits.¹²⁵¹²³

2. Free‑plan limits that matter for Ollama

Cloudflare’s Free Plan (which you get by default with a domain):

• Bandwidth & requests
  • Cloudflare sits in front of your tunnel as a CDN; it handles a lot of requests for free, but extremely high traffic may push you into paid tiers (Cloudflare rarely charges for normal dev‑level usage).¹²⁴¹²⁶
• Rate‑limiting & abuse protection
  • Free plan includes DDoS and rate limiting; if someone scrapes your Ollama API, Cloudflare may throttle.¹²⁴

For reasonable Ollama traffic (you + maybe a few others chatting, no massive batching):

• You typically stay well under free‑plan limits.

3. Workers AI vs Tunnel (for comparison)

If you ever mix in Cloudflare Workers AI (Llama‑family models via Workers):

• Workers AI has a free‑tier credit (e.g., a few hundred‑thousand tokens per month); you pay only after you exceed that.^127128122
• This is separate from Tunnel; you can run Tunnel‑based Ollama for free while Workers AI has its own usage cap.¹²⁶¹²²

4. Practical “Ollama + Cloudflare Tunnel” limit

For your Ollama setup:

• Tunnel uptime: effectively “unlimited” as long as cloudflared runs on your PC/server and your ISP doesn’t block long‑lived TCP connections.¹²³¹²⁵
• Bandwidth:
  • Free tier is generous; you will notice no hard limit for typical coding‑chat usage (you’re moving small JSON payloads, not huge video streams).¹²⁶¹²⁴
• CPU/server side:
  • The only “limit” is on your own Ollama‑hosting machine (CPU, RAM, VRAM), not on Cloudflare.

In other words:

For one‑person Ollama + mobile access via Cloudflare Tunnel on the free plan, you can treat it as “virtually unlimited” unless you suddenly start serving thousands of users per day.¹²³¹²⁴

If you tell me:

• how many users you expect (just you, your team, or public), I can tell you whether you should stay on the Free Plan or if you’ll realistically hit limits and need a higher tier.

Can run ollama coding model in phone

Yes, you can run Ollama‑style coding models on your phone, but there are two main ways:

1. Run Ollama directly on Android (via Termux / Linux layer).
2. Run Ollama on a server (PC/VPS) and talk to it from a mobile Ollama client.

Below is a practical breakdown for coding‑use.

1. Run Ollama coding models directly on Android (phone)

If your phone has a modern chip (e.g., Snapdragon 8 series, Exynos 2400, etc.) and 8–12 GB+ RAM, you can run small‑to‑medium coding‑oriented models locally.

• How it’s done:
  • Install Termux (Linux environment on Android, usually from F‑Droid or GitHub, not Play Store).¹²⁹¹³⁰
  • In Termux, install Ollama:

pkg install ollama

- Pull a coding‑friendly model:

ollama pull codellama:7b
ollama run codellama:7b

You can also try qwen:7b, gemma2:7b, or phi‑3:mini for lighter coding‑style models.¹³¹¹³²

• Performance:
  • 7 B models run usable but slow on most phones; expect ~1–3 tokens/sec on not‑top‑end devices.¹³²¹³¹
  • Good for:
    • Small code fixes,
    • Small refactors,
    • Inline‑style help, not large‑file‑wide rewrites.
• Tutorials:
  • Several guides show this setup step‑by‑step for Android (e.g., “Run Llama‑family models on Android with Termux + Ollama”).^133131132

2. Use a mobile Ollama client plus external server

If running Ollama on the phone is too heavy or battery‑draining, you can:

• Host Ollama on your PC, home server, or VPS, then
• Use a mobile Ollama‑client app to chat with it.

Popular Android apps

• Ollama AI Chat (Google Play, requires Android 13+)
  • Connects to your Ollama server URL (e.g., http://your‑laptop‑ip:11434 or a tunnel URL).¹³⁴
  • Supports models like Llama, Mistral, CodeLlama, etc.
• MyOllama (open‑source client, iOS/Android)
  • Connects to your Ollama‑hosted server; privacy‑oriented, no telemetry.¹³⁵
• Custom / community Ollama‑for‑Android
  • GitHub apps like ollama‑app‑for‑Android let you talk to your Ollama server from phone, even with image‑input models.¹³⁶

3. Practical “coding‑on‑phone” recommendation for you

Given you already run Ollama on your office/home PC:

• Best balance:
  • Keep Ollama on PC or home server (run codellama:13b, qwen:7b, mistral:7b there).
  • Use Cloudflare Tunnel / Pinggy to expose it securely (e.g., https://ollama.yourdomain.com).
  • On your phone, install Ollama AI Chat or MyOllama and point it at that URL.^137135134
• Only run Ollama on the phone if:
  • You want offline‑only coding help (e.g., on a train or rural area).
  • You’re okay with slower 7‑B coding models and higher battery use.¹³¹¹³²

If you tell your phone model / RAM and whether you prefer “offline on phone” or “server + client”, I can pick you one exact model (codellama:x, qwen:x, etc.) plus a tailored command + app setup for your stack. ¹³⁸¹³⁹