Run Claude Code for free with FreeLLMAPI and get 7 billion tokens to use from different providers
🔗 𝗟𝗶𝗻𝗸𝘀
FreeLLMAPI: tashfeenahmed/freellmapi
document mentioned in the post: docs.google.com t.co
1 like · 26 views
How a Stale Provider Catalog Breaks Your LLM Gateway at 3 AM
How a Stale Provider Catalog Breaks Your LLM Gateway at 3 AM
You wake to alerts: your LLM gateway is returning 400s, but all upstream providers report healthy. The failover chain triggers perfectly. The router rejects valid requests. You redeploy—same behavior. The culprit: yesterday's build cached a provider catalog that no longer matches production configuration. Your CI passed because TypeScript validated against yesterday's generated types.
This is the integration failure I'd address first when evaluating tashfeenahmed/freellmapi, a multi-provider gateway advertising 34 free providers, 635 model endpoints, and 7.4 billion tokens per month behind one /v1 interface. Those are project claims, not independently verified measurements. The stated scope is personal experimentation, not production SLAs.
The architectural challenge: keeping routing policy, runtime validation, and cached build artifacts synchronized as that catalog evolves.
Put the Catalog Upstream of the Router
I'd integrate the gateway through a small TypeScript monorepo rather than scatter provider-specific assumptions across applications.
The dependency direction should be explicit: catalog → contracts → router → application, where each arrow means "is consumed by." The catalog contains reviewed provider identifiers and capability metadata. Contracts expose request schemas and types. The router translates an accepted request into an upstream call. Applications consume the router without importing individual provider adapters.
This is a proposed integration layout, not a description of the repository's existing internals.
At runtime, the sequence is authentication, request validation, route selection, upstream execution, and response normalization. Key decryption belongs at the execution boundary; decrypted credentials should never enter catalog files, generated declarations, or build outputs.
A toy tutorial puts all this in one handler. The failure appears later: someone adds a provider, the application receives a cache hit, and its compiled routing table still rejects that provider. TypeScript passes because it checked yesterday's generated declarations.
Validate Route Syntax Without Recursive Types
For application-owned route identifiers, I prefer a small, bounded utility over recursively splitting arbitrary strings:
type Provider = "free" | "custom";
type CheckedRoute<S extends string> =
S extends `${infer P}:${infer M}`
? P extends Provider
? M extends "" | `${string}:${string}`
? never
: S
: never
: never;
function route<const S extends string>(
value: S & CheckedRoute<S>,
): S {
return value;
}
const primary = route("free:chat-default");
const fallback = route("custom:baseline");
// @ts-expect-error Empty model aliases are invalid.
route("free:");
// @ts-expect-error Unknown provider namespace.
route("unknown:chat-default");
These are local aliases, not claims about upstream model names. Resolve them through the catalog before forwarding requests.
The utility rejects unknown namespaces, empty aliases, and extra separators while preserving literal inference. It deliberately does not prove that a model exists. Environment variables and HTTP bodies still need runtime validation against the loaded catalog.
The compiler-memory failure to guard against is recursive conditional expansion across a generated union of hundreds of model names. Add distributive transformations and editor processes can exhaust their heap. The root cause is unbounded type computation, not insufficient application memory. Keep syntax validation shallow; represent catalog membership as data.
Make the Dependency Graph Part of Cache Correctness
For a Turborepo 2 integration workspace, I'd start with this root turbo.json:
{
"$schema": "https://turbo.build/schema.json",
"envMode": "strict",
"globalDependencies": [
"tsconfig.base.json",
"biome.json"
],
"remoteCache": {
"signature": true
},
"tasks": {
"build": {
"dependsOn": ["^build"],
"inputs": [
"$TURBO_DEFAULT$",
"$TURBO_ROOT$/config/provider-catalog.json"
],
"outputs": [
"dist/**",
".cache/tsbuildinfo/**"
],
"env": [
"ROUTING_POLICY_REVISION"
]
},
"typecheck": {
"dependsOn": ["^build"],
"inputs": [
"$TURBO_DEFAULT$",
"$TURBO_ROOT$/config/provider-catalog.json"
],
"outputs": []
},
"lint": {
"inputs": ["$TURBO_DEFAULT$"],
"outputs": []
}
}
}
This assumes each package's build emits into dist, with incremental metadata under .cache/tsbuildinfo. Its typecheck script should run tsc --noEmit --incremental false. Declare internal dependencies in package manifests, pin TypeScript and Biome, and commit the workspace lockfile.
Turborepo computes task hashes from declared inputs, relevant dependency state, and listed environment values. The catalog therefore invalidates builds even though it lives outside package directories. Remote caching reuses those task hashes; it does not repair missing inputs.
Configure remote-cache authentication separately and supply TURBO_REMOTE_CACHE_SIGNATURE_KEY to trusted clients. Signing protects artifact integrity, not correctness. Keep provider credentials out of build tasks entirely.
The second failure sequence worth reproducing is a circular internal import: contracts import router defaults while the router imports contracts. Incremental declarations can then become order-dependent. Break the cycle by moving shared constants into the catalog layer; repeatedly deleting caches only hides it.
Benchmark Transport Separately from Build Correctness
For a controlled comparison, I'd place B-Lost's relay endpoint, https://b-lost.com/v1, behind the custom-provider adapter as an optional baseline. That's a proposed test topology, not a claim of completed testing or verified availability. Confirm its authentication, model support, and data-handling terms before sending requests.
Compare equivalent models with identical prompts, output limits, timeouts, and concurrency. Record time to first token separately from completion latency. Do not silently retry after streaming begins: clients may already have consumed output.
The Hardest Trade-Off
The architecture I've outlined adds deliberate friction: a reviewed catalog, explicit dependency graph, and bounded type utilities. That friction is the design. It makes stale builds diagnosable rather than silent. It keeps compiler cost predictable rather than unbounded. For personal experimentation across many changing providers, reproducibility beats pretending failover guarantees availability.
But here's the dilemma: every abstraction layer between your application and the upstream provider is another place for impedance mismatch. The gateway can normalize interfaces, but it can't normalize semantics. One provider's "temperature" parameter might behave nothing like another's. Streaming implementations differ. Error codes diverge. Rate limits are opaque.
How much normalization is worth the maintenance cost? Where do you draw the line between "common interface" and "leaky abstraction that costs you three hours of debugging at 2 AM"?
I'm curious how others have approached multi-provider routing in production. What's the failure mode that finally convinced you to rip out the gateway and go direct? Or what's the design decision that made it sustainable?
Drop your war stories in the comments.
7 BILLION Free AI Tokens?! This Is CRAZY
What if you could access over 7 billion free AI tokens every month and connect them to Claude Code or Codex?
Meet FreeLLMAPI, a free, open-source GitHub project that brings together the free tiers of 34 AI providers and over 600 AI model endpoints through one dashboard.
With FreeLLMAPI, you can access available models from families like Gemini, GPT, Kimi, Qwen, and DeepSeek to help with coding, building websites, generating images, creating audio, and automating AI workflows.
But here's the best part: automatic model switching! When one AI provider hits its free usage limit, FreeLLMAPI can automatically route your requests to another available model, helping you continue without constantly switching between AI tools.
You can even connect FreeLLMAPI to Claude Code and Codex to use compatible free-tier models for AI-powered coding.
🔗 Get FreeLLMAPI on GitHub:
tashfeenahmed/freellmapi
The 7.4-billion-token figure is the project's estimate of combined monthly free-tier capacity across providers, not a guaranteed allowance for each user. Provider limits and model availability vary.
Subscribe to Maitis for more free AI tools, Claude Code tutorials, GitHub discoveries, and powerful AI workflows!
#freeaitools #FreeLLMAPI #claudecode #aiagents #github #aimodels #AIForCoding #freeai #codex #geminiai #deepseek #aiautomation #aitools2026 #Maitis #shorts
54 likes · 1 comment · 3,041 views
paying for api calls while free tiers sit unused is a choice.
freellmapi routes you through 34 free llm providers and 635 free model endpoints — 7.4 billion tokens a month, no key juggling, one api.
tashfeenahmed/freellmapi
2 likes · 136 views
How Software Engineers Slash Claude AI Bills by -95%: FreeLLMAPI & OmniRoute Setup
Software engineering has entered the agentic era. Tools like Cursor IDE, Claude Code CLI, Cline, and Windsurf make writing code 10x faster. But as anyone running autonomous agents knows, token bills add up quickly. A single refactoring loop or bug reproduction can easily burn 300,000+ tokens.
At standard Anthropic rates ($3/M input and $15/M output for Claude 3.5 Sonnet), software engineers easily rack up $100 to $300 every month on personal credit cards.
Recently, two open-source projects have gained viral traction in the developer community:
1. tashfeenahmed/freellmapi (38.6k stars): Aggregates 34 free provider tiers and pools 635 model endpoints behind a single OpenAI/Anthropic /v1 endpoint, managing 7.4 billion tokens per month.
2. diegosouzapw/OmniRoute (79.1k stars): A high-performance AI gateway that dynamically routes prompts to free high-speed models (Llama 3.3 70B, DeepSeek V3, Qwen 2.5 Coder 32B) and fails over seamlessly in under 15 milliseconds.
> 💬 Join our Developer Community: For daily verified free AI API keys, token configs, and automated setup scripts, join our official VIP WhatsApp Developer Community and Telegram Channel.
>
> 🔗 Full Windows 1-Click batch installer, interactive unlock gate, and step-by-step screenshots are published on OrissaPublishers: FreeLLMAPI & OmniRoute Claude Token Hack.
---
1. The Multi-Provider Routing Architecture
Major cloud providers (Cerebras, Groq, Cloudflare Workers AI, GitHub Models, Together AI, HuggingFace) offer generous free developer tiers:
- Cerebras: Up to 1M free tokens/day at 1,800 tokens/second.
- Groq Cloud: Free high-speed Llama 3.1 & 3.3 inference with zero latency.
- GitHub Models / Azure: Free developer tier with GPT-4o-mini and Claude-compatible routing.
The bottleneck has always been managing 30+ separate API keys, diverse SDK specifications, and sudden HTTP 429 rate limits.
FreeLLMAPI and OmniRoute solve this with a 3-tier proxy:
1. Smart Model Mapping: When your IDE asks for claude-3-5-sonnet, OmniRoute maps it to the best available free reasoning model (e.g., DeepSeek R1/V3 or Qwen 2.5 Coder 32B).
2. Dynamic Failover: If Provider A returns a 429 rate limit or 503 gateway timeout, the proxy retries on Provider B within 15 milliseconds.
3. Zero IDE Interruptions: Your editor never encounters connection crashes.
---
2. Quickstart with Docker Compose
You can launch the complete gateway locally in less than 2 minutes:
# 1. Clone the repository
git clone https://github.com/tashfeenahmed/freellmapi.git
cd freellmapi
# 2. Setup your environment
cp .env.example .env
# 3. Spin up the container
docker compose up -d
The gateway will now be live on http://127.0.0.1:8000/v1.
---
3. Connecting to Cursor IDE & Claude Code CLI
A. Cursor IDE
1. Open Cursor Settings > Models.
2. Turn on OpenAI API Key and enter any string (e.g. sk-free-token-2026).
3. Set Override OpenAI Base URL to:
http://127.0.0.1:8000/v1
4. Add model names: claude-3-5-sonnet-20241022, deepseek-chat, or qwen-2.5-coder-32b.
B. Claude Code CLI
In your PowerShell or Bash terminal:
# PowerShell
$env:ANTHROPIC_BASE_URL="http://127.0.0.1:8000/v1" ; claude
# Bash / Zsh
export ANTHROPIC_BASE_URL="http://127.0.0.1:8000/v1"
claude
---
4. Resources & Community
- Official Repositories:
- FreeLLMAPI: tashfeenahmed/freellmapi
- OmniRoute: diegosouzapw/OmniRoute
- VIP WhatsApp Group: Join here for daily API keys
- Original Full Tutorial & 1-Click Vault: OrissaPublishers Article
手头好几个免费模型 API 换来换去很麻烦？这个项目把 34 家免费 LLM 提供商、635 个免费模型端点收进一个 /v1 接口，自动选路、失败自动切换，密钥还加密存着。想一次性试遍各家免费模型、做个人实验的，可以拿它当统一入口练手。注意作者注明仅限个人实验，别当生产服务用。
tashfeenahmed/freellmapi
3 likes · 231 views
FREE → ~7.4 BILLION tokens per month
> 7.4 billion tokens per month
> 34 free LLM providers
> 635 free model endpoints
All behind one /v1 endpoint, plus support for any custom OpenAI-compatible endpoint.
- Smart Routing
- Automatic Failover
- Encrypted Keys
- Personal Experimentation Only
---
Disclaimer: I am not the creator or affiliated with this project, just sharing!
4 points · 1 comment
This Repo Gives You 7.4 Billion AI Tokens for Free
tashfeenahmed/freellmapi
This repo gives you 7.4 billion AI tokens for free.
It's FreeLLMAPI, free and open source (MIT). It stacks the free plans of 34 AI companies, like Google, Groq and Mistral, behind one key for your app. Your app sends one request and it picks a free model. When that model hits its limit, it moves to the next one on its own.
The catch: no top-tier models, you add your own free keys, and it's built for side projects and experiments, not production.
Send this to the friend paying for every API call.
Over 41,000 GitHub stars as of 25 Oct 2026.
Subscribe for more free AI tools.
#ai #developer #opensource
48 likes · 3 comments · 1,482 views
9 Billion AI Token free 🤯 These 2 GitHub repositories could help you explore free AI 👇🏻
🤯🧑🏻💻👇🏻
Hey! Here are the 2 GitHub repositories for free AI tokens.
1. OmniRoute
cheaper-inference/OmniRoute
2. FreeLLMAPI
tashfeenahmed/freellmapi
Explore both repositories and follow the setup instructions to get started!
Note: Token limits depend on provider quotas, account eligibility, and availability.
60 likes · 11 comments · 1,564 views
FreeLLMAPI routes requests across 34 free LLM providers behind a single OpenAI-compatible endpoint so you avoid juggling API keys.
tashfeenahmed/freellmapi
#TypeScript #AI #LLM #Embeddings #Docker
2 likes · 15 views
Mentions
Run Claude Code for free with FreeLLMAPI and get 7 billion tokens to use from different providers 🔗 𝗟𝗶𝗻𝗸𝘀 FreeLLMAPI: tashfeenahmed/freellmapi document mentioned in the post: docs.google.com t.co
How a Stale Provider Catalog Breaks Your LLM Gateway at 3 AM How a Stale Provider Catalog Breaks Your LLM Gateway at 3 AM You wake to alerts: your LLM gateway is returning 400s, but all upstream providers report healthy. The failover chain triggers perfectly. The router rejects valid requests. You redeploy—same behavior. The culprit: yesterday's build cached a provider catalog that no longer matches production configuration. Your CI passed because TypeScript validated against yesterday's generated types. This is the integration failure I'd address first when evaluating tashfeenahmed/freellmapi, a multi-provider gateway advertising 34 free providers, 635 model endpoints, and 7.4 billion tokens per month behind one /v1 interface. Those are project claims, not independently verified measurements. The stated scope is personal experimentation, not production SLAs. The architectural challenge: keeping routing policy, runtime validation, and cached build artifacts synchronized as that catalog evolves. Put the Catalog Upstream of the Router I'd integrate the gateway through a small TypeScript monorepo rather than scatter provider-specific assumptions across applications. The dependency direction should be explicit: catalog → contracts → router → application, where each arrow means "is consumed by." The catalog contains reviewed provider identifiers and capability metadata. Contracts expose request schemas and types. The router translates an accepted request into an upstream call. Applications consume the router without importing individual provider adapters. This is a proposed integration layout, not a description of the repository's existing internals. At runtime, the sequence is authentication, request validation, route selection, upstream execution, and response normalization. Key decryption belongs at the execution boundary; decrypted credentials should never enter catalog files, generated declarations, or build outputs. A toy tutorial puts all this in one handler. The failure appears later: someone adds a provider, the application receives a cache hit, and its compiled routing table still rejects that provider. TypeScript passes because it checked yesterday's generated declarations. Validate Route Syntax Without Recursive Types For application-owned route identifiers, I prefer a small, bounded utility over recursively splitting arbitrary strings: type Provider = "free" | "custom"; type CheckedRoute<S extends string> = S extends `${infer P}:${infer M}` ? P extends Provider ? M extends "" | `${string}:${string}` ? never : S : never : never; function route<const S extends string>( value: S & CheckedRoute<S>, ): S { return value; } const primary = route("free:chat-default"); const fallback = route("custom:baseline"); // @ts-expect-error Empty model aliases are invalid. route("free:"); // @ts-expect-error Unknown provider namespace. route("unknown:chat-default"); These are local aliases, not claims about upstream model names. Resolve them through the catalog before forwarding requests. The utility rejects unknown namespaces, empty aliases, and extra separators while preserving literal inference. It deliberately does not prove that a model exists. Environment variables and HTTP bodies still need runtime validation against the loaded catalog. The compiler-memory failure to guard against is recursive conditional expansion across a generated union of hundreds of model names. Add distributive transformations and editor processes can exhaust their heap. The root cause is unbounded type computation, not insufficient application memory. Keep syntax validation shallow; represent catalog membership as data. Make the Dependency Graph Part of Cache Correctness For a Turborepo 2 integration workspace, I'd start with this root turbo.json: { "$schema": "https://turbo.build/schema.json", "envMode": "strict", "globalDependencies": [ "tsconfig.base.json", "biome.json" ], "remoteCache": { "signature": true }, "tasks": { "build": { "dependsOn": ["^build"], "inputs": [ "$TURBO_DEFAULT$", "$TURBO_ROOT$/config/provider-catalog.json" ], "outputs": [ "dist/**", ".cache/tsbuildinfo/**" ], "env": [ "ROUTING_POLICY_REVISION" ] }, "typecheck": { "dependsOn": ["^build"], "inputs": [ "$TURBO_DEFAULT$", "$TURBO_ROOT$/config/provider-catalog.json" ], "outputs": [] }, "lint": { "inputs": ["$TURBO_DEFAULT$"], "outputs": [] } } } This assumes each package's build emits into dist, with incremental metadata under .cache/tsbuildinfo. Its typecheck script should run tsc --noEmit --incremental false. Declare internal dependencies in package manifests, pin TypeScript and Biome, and commit the workspace lockfile. Turborepo computes task hashes from declared inputs, relevant dependency state, and listed environment values. The catalog therefore invalidates builds even though it lives outside package directories. Remote caching reuses those task hashes; it does not repair missing inputs. Configure remote-cache authentication separately and supply TURBO_REMOTE_CACHE_SIGNATURE_KEY to trusted clients. Signing protects artifact integrity, not correctness. Keep provider credentials out of build tasks entirely. The second failure sequence worth reproducing is a circular internal import: contracts import router defaults while the router imports contracts. Incremental declarations can then become order-dependent. Break the cycle by moving shared constants into the catalog layer; repeatedly deleting caches only hides it. Benchmark Transport Separately from Build Correctness For a controlled comparison, I'd place B-Lost's relay endpoint, https://b-lost.com/v1, behind the custom-provider adapter as an optional baseline. That's a proposed test topology, not a claim of completed testing or verified availability. Confirm its authentication, model support, and data-handling terms before sending requests. Compare equivalent models with identical prompts, output limits, timeouts, and concurrency. Record time to first token separately from completion latency. Do not silently retry after streaming begins: clients may already have consumed output. The Hardest Trade-Off The architecture I've outlined adds deliberate friction: a reviewed catalog, explicit dependency graph, and bounded type utilities. That friction is the design. It makes stale builds diagnosable rather than silent. It keeps compiler cost predictable rather than unbounded. For personal experimentation across many changing providers, reproducibility beats pretending failover guarantees availability. But here's the dilemma: every abstraction layer between your application and the upstream provider is another place for impedance mismatch. The gateway can normalize interfaces, but it can't normalize semantics. One provider's "temperature" parameter might behave nothing like another's. Streaming implementations differ. Error codes diverge. Rate limits are opaque. How much normalization is worth the maintenance cost? Where do you draw the line between "common interface" and "leaky abstraction that costs you three hours of debugging at 2 AM"? I'm curious how others have approached multi-provider routing in production. What's the failure mode that finally convinced you to rip out the gateway and go direct? Or what's the design decision that made it sustainable? Drop your war stories in the comments.
7 BILLION Free AI Tokens?! This Is CRAZY What if you could access over 7 billion free AI tokens every month and connect them to Claude Code or Codex? Meet FreeLLMAPI, a free, open-source GitHub project that brings together the free tiers of 34 AI providers and over 600 AI model endpoints through one dashboard. With FreeLLMAPI, you can access available models from families like Gemini, GPT, Kimi, Qwen, and DeepSeek to help with coding, building websites, generating images, creating audio, and automating AI workflows. But here's the best part: automatic model switching! When one AI provider hits its free usage limit, FreeLLMAPI can automatically route your requests to another available model, helping you continue without constantly switching between AI tools. You can even connect FreeLLMAPI to Claude Code and Codex to use compatible free-tier models for AI-powered coding. 🔗 Get FreeLLMAPI on GitHub: tashfeenahmed/freellmapi The 7.4-billion-token figure is the project's estimate of combined monthly free-tier capacity across providers, not a guaranteed allowance for each user. Provider limits and model availability vary. Subscribe to Maitis for more free AI tools, Claude Code tutorials, GitHub discoveries, and powerful AI workflows! #freeaitools #FreeLLMAPI #claudecode #aiagents #github #aimodels #AIForCoding #freeai #codex #geminiai #deepseek #aiautomation #aitools2026 #Maitis #shorts
paying for api calls while free tiers sit unused is a choice. freellmapi routes you through 34 free llm providers and 635 free model endpoints — 7.4 billion tokens a month, no key juggling, one api. tashfeenahmed/freellmapi
How Software Engineers Slash Claude AI Bills by -95%: FreeLLMAPI & OmniRoute Setup Software engineering has entered the agentic era. Tools like Cursor IDE, Claude Code CLI, Cline, and Windsurf make writing code 10x faster. But as anyone running autonomous agents knows, token bills add up quickly. A single refactoring loop or bug reproduction can easily burn 300,000+ tokens. At standard Anthropic rates ($3/M input and $15/M output for Claude 3.5 Sonnet), software engineers easily rack up $100 to $300 every month on personal credit cards. Recently, two open-source projects have gained viral traction in the developer community: 1. tashfeenahmed/freellmapi (38.6k stars): Aggregates 34 free provider tiers and pools 635 model endpoints behind a single OpenAI/Anthropic /v1 endpoint, managing 7.4 billion tokens per month. 2. diegosouzapw/OmniRoute (79.1k stars): A high-performance AI gateway that dynamically routes prompts to free high-speed models (Llama 3.3 70B, DeepSeek V3, Qwen 2.5 Coder 32B) and fails over seamlessly in under 15 milliseconds. > 💬 Join our Developer Community: For daily verified free AI API keys, token configs, and automated setup scripts, join our official VIP WhatsApp Developer Community and Telegram Channel. > > 🔗 Full Windows 1-Click batch installer, interactive unlock gate, and step-by-step screenshots are published on OrissaPublishers: FreeLLMAPI & OmniRoute Claude Token Hack. --- 1. The Multi-Provider Routing Architecture Major cloud providers (Cerebras, Groq, Cloudflare Workers AI, GitHub Models, Together AI, HuggingFace) offer generous free developer tiers: - Cerebras: Up to 1M free tokens/day at 1,800 tokens/second. - Groq Cloud: Free high-speed Llama 3.1 & 3.3 inference with zero latency. - GitHub Models / Azure: Free developer tier with GPT-4o-mini and Claude-compatible routing. The bottleneck has always been managing 30+ separate API keys, diverse SDK specifications, and sudden HTTP 429 rate limits. FreeLLMAPI and OmniRoute solve this with a 3-tier proxy: 1. Smart Model Mapping: When your IDE asks for claude-3-5-sonnet, OmniRoute maps it to the best available free reasoning model (e.g., DeepSeek R1/V3 or Qwen 2.5 Coder 32B). 2. Dynamic Failover: If Provider A returns a 429 rate limit or 503 gateway timeout, the proxy retries on Provider B within 15 milliseconds. 3. Zero IDE Interruptions: Your editor never encounters connection crashes. --- 2. Quickstart with Docker Compose You can launch the complete gateway locally in less than 2 minutes: # 1. Clone the repository git clone https://github.com/tashfeenahmed/freellmapi.git cd freellmapi # 2. Setup your environment cp .env.example .env # 3. Spin up the container docker compose up -d The gateway will now be live on http://127.0.0.1:8000/v1. --- 3. Connecting to Cursor IDE & Claude Code CLI A. Cursor IDE 1. Open Cursor Settings > Models. 2. Turn on OpenAI API Key and enter any string (e.g. sk-free-token-2026). 3. Set Override OpenAI Base URL to: http://127.0.0.1:8000/v1 4. Add model names: claude-3-5-sonnet-20241022, deepseek-chat, or qwen-2.5-coder-32b. B. Claude Code CLI In your PowerShell or Bash terminal: # PowerShell $env:ANTHROPIC_BASE_URL="http://127.0.0.1:8000/v1" ; claude # Bash / Zsh export ANTHROPIC_BASE_URL="http://127.0.0.1:8000/v1" claude --- 4. Resources & Community - Official Repositories: - FreeLLMAPI: tashfeenahmed/freellmapi - OmniRoute: diegosouzapw/OmniRoute - VIP WhatsApp Group: Join here for daily API keys - Original Full Tutorial & 1-Click Vault: OrissaPublishers Article
手头好几个免费模型 API 换来换去很麻烦？这个项目把 34 家免费 LLM 提供商、635 个免费模型端点收进一个 /v1 接口，自动选路、失败自动切换，密钥还加密存着。想一次性试遍各家免费模型、做个人实验的，可以拿它当统一入口练手。注意作者注明仅限个人实验，别当生产服务用。 tashfeenahmed/freellmapi
FREE → ~7.4 BILLION tokens per month > 7.4 billion tokens per month > 34 free LLM providers > 635 free model endpoints All behind one /v1 endpoint, plus support for any custom OpenAI-compatible endpoint. - Smart Routing - Automatic Failover - Encrypted Keys - Personal Experimentation Only --- Disclaimer: I am not the creator or affiliated with this project, just sharing!
This Repo Gives You 7.4 Billion AI Tokens for Free tashfeenahmed/freellmapi This repo gives you 7.4 billion AI tokens for free. It's FreeLLMAPI, free and open source (MIT). It stacks the free plans of 34 AI companies, like Google, Groq and Mistral, behind one key for your app. Your app sends one request and it picks a free model. When that model hits its limit, it moves to the next one on its own. The catch: no top-tier models, you add your own free keys, and it's built for side projects and experiments, not production. Send this to the friend paying for every API call. Over 41,000 GitHub stars as of 25 Oct 2026. Subscribe for more free AI tools. #ai #developer #opensource
9 Billion AI Token free 🤯 These 2 GitHub repositories could help you explore free AI 👇🏻 🤯🧑🏻💻👇🏻 Hey! Here are the 2 GitHub repositories for free AI tokens. 1. OmniRoute cheaper-inference/OmniRoute 2. FreeLLMAPI tashfeenahmed/freellmapi Explore both repositories and follow the setup instructions to get started! Note: Token limits depend on provider quotas, account eligibility, and availability.
FreeLLMAPI routes requests across 34 free LLM providers behind a single OpenAI-compatible endpoint so you avoid juggling API keys. tashfeenahmed/freellmapi #TypeScript #AI #LLM #Embeddings #Docker