“Best free LLM” is not one model that wins everywhere. For an AI bot, the best choice depends on your goal (support, lead capture, workflows, coding, vision), your latency needs, and the limits of the free endpoint you are actually using.
This guide is built around model specs and limits shown on OpenRouter model pages (context window, free pricing flags, and capability notes), so you can make a decision you will not regret two weeks after launch.
Quick answer: best free LLM picks by bot type
1) Best free LLM for a fast website support bot
If you want quick replies and a smooth chat experience, start with Gemini 2.0 Flash Experimental (free). It is positioned as a fast model and supports a very large context window on OpenRouter.
2) Best free LLM for reasoning-heavy conversations
If your bot does multi-step logic (triage, troubleshooting, decision trees), DeepSeek R1 0528 (free) is a strong default. It is positioned as an updated R1 variant on OpenRouter and supports a large context window.
3) Best free LLM for balanced “support + workflow” bots
If you want a practical middle ground, use OpenAI gpt-oss-20b (free) as your default. On OpenRouter it is described with tool use and structured output support.
4) Best free LLM for open ecosystem and broad compatibility
If you want a popular instruct model with a big ecosystem, use Meta Llama 3.3 70B Instruct (free), especially as an escalation model for harder questions.
5) Best free LLM for multimodal bots (images, screenshots, documents)
Two practical picks from the free list:
Google Gemma 3 27B (free): described as multimodal and suitable for vision-language inputs.
NVIDIA Nemotron Nano 12B 2 VL (free): positioned for multimodal reasoning and document intelligence.
6) Best free LLM for coding agents
For agentic coding, Mistral Devstral 2 2512 (free) is built for that job and supports a large context window.
What “free” means on OpenRouter (and what it does not)
On OpenRouter, many models have a “free” variant that shows $0 token pricing on the model page. “Free” still has real limits and tradeoffs.
Rate limits: free endpoints are often capped (requests per minute or per day).
Availability: free endpoints can change and may be less stable during peak traffic.
Logging: some free endpoints may log prompts and outputs. Check the model page warnings.
The safest approach is “free-first, not free-only.” Start free, measure quality and reliability, then keep a paid fallback ready for peak traffic or business-critical flows.
Real-World Testing Results: How Free LLMs Actually Behave in an AI Bot
Choosing a free LLM is not only about benchmarks or context size. In a real AI bot, what matters is how the model follows instructions, how fast it responds, and whether it correctly triggers a fallback form when it does not know the answer.
Below are results from live testing inside an AI bot setup. The same prompt rules were applied to every model. One key test question was: “What is Oscar Chat?”
We also checked whether the model correctly shows the “Ask a question / contact form” when it cannot provide an answer.
LLM
How it works in practice
Shows fallback form if bot doesn’t know
OpenAI gpt-oss-120b (free)
Works well, follows rules, answers in structured paragraphs and asks clarifying questions
Yes
OpenAI gpt-oss-20b (free)
Works well, follows instructions, good paragraph structure and clarifying questions
Yes
DeepSeek R1 0528 (free)
High-quality answers, but response time is too long for live chat
Yes
Google Gemma 3 27B (free)
Does not follow bot rules correctly
Yes
Mistral Devstral 2 2512 (free)
Stable and predictable behavior
Yes
Mistral 7B Instruct (free)
Works well, respects structure, asks clarifying questions
Yes
NVIDIA Nemotron 3 Nano 30B A3B (free)
Good answers, consistent behavior
Yes
TNG DeepSeek R1T2 Chimera (free)
Acceptable behavior, usable in production with routing
Yes
TNG R1T Chimera (free)
Good overall performance
Yes
Qwen3 Coder 480B A35B (free)
Correct behavior, but response time is too slow
Yes (slow)
Google Gemini 2.0 Flash Experimental (free)
Did not answer “What is Oscar Chat?”, ignored question
Yes (shows question form)
Google Gemma 3 4B (free)
Did not answer basic product question
Yes (shows question form)
Google Gemma 3n 2B (free)
Did not answer basic product question
Yes (shows question form)
Google Gemma 3n 4B (free)
Did not answer basic product question
Yes (shows question form)
Meta Llama 3.1 405B Instruct (free)
Did not answer basic product question
Yes (shows question form)
MoonshotAI Kimi K2 0711 (free)
Did not answer basic product question
Yes (shows question form)
Qwen2.5-VL 7B Instruct (free)
Did not answer basic product question
Yes (shows question form)
Qwen3 4B (free)
Did not answer basic product question
Yes (shows question form)
TNG DeepSeek R1T Chimera (free)
Did not answer basic product question
Yes (shows question form)
Arcee AI Trinity Mini (free)
Works well for simple replies
No
Auto Router
Routing only, not an answering model
No
Body Builder (beta)
Unstable and unreliable behavior
No
Kwaipilot KAT-Coder-Pro V1 (free)
Quick and good answers for coding tasks
No
Meta Llama 3.2 3B Instruct (free)
Not usable for this bot setup
No
Meta Llama 3.3 70B Instruct (free)
Good answers, but no fallback form trigger
No
Mistral Small 3.1 24B (free)
Acceptable behavior
No
Nex AGI DeepSeek V3.1 Nex N1 (free)
Good answers
No
Nous Hermes 3 405B Instruct (free)
Good answers
No
NVIDIA Nemotron Nano 12B 2 VL (free)
Good multimodal behavior
No
NVIDIA Nemotron Nano 9B V2 (free)
Good general behavior
No
Venice Uncensored (free)
Works, but not safe for customer-facing bots
No
Xiaomi MiMo-V2-Flash (free)
Acceptable performance
No
Z.AI GLM 4.5 Air (free)
Acceptable performance
No
Google Gemma 3 12B (free)
Does not work correctly in this setup
No
Key Takeaways from Testing
Only a subset of free LLMs correctly follow bot rules and trigger fallback forms.
Speed matters more than raw intelligence for live chat.
Large reasoning models often perform well but are too slow without routing.
Fallback behavior is as important as answer quality in production bots.
The most reliable strategy is to use a fast, rule-following model as default and route complex requests to slower reasoning models only when needed.
What to check in every LLM (the limits that actually matter)
1) Context window
Context is your budget for chat history, product catalog snippets, and retrieval (RAG) sources. On OpenRouter, context can vary widely across free variants, so confirm the exact number on each model page.
2) Tool use and structured output
If your bot calls APIs (orders, tickets, booking, lead routing), you need reliable structured output. Prefer models whose pages mention tool use, function calling, or structured outputs.
3) Multimodal support (images and documents)
If users send screenshots or photos, pick a model described as multimodal. Then test it with your real images, not demo prompts.
4) Safety and policy behavior
Customer-facing bots need predictable refusal behavior and low toxicity risk. “Uncensored” models increase moderation burden and should usually be limited to internal testing.
5) Latency and user experience
Users judge your bot on speed. Use fast models by default, then escalate only when the request is complex.
6) License and allowed use
Open-weight or free availability does not automatically mean unrestricted commercial use. Confirm the license notes on the model page.
7) Data handling and retention
If you send user data to a hosted model, review logging and retention notes. For sensitive data, consider stricter routing or self-hosted options.
Free LLM comparison table (OpenRouter specs)
Use this table as a starting point. Always verify the exact model ID you select on OpenRouter, because free variants are separate entries.
Model (free variant)
Best for
What to check on OpenRouter
Real-world limits to watch
OpenAI gpt-oss-120b (free)
High quality answers, strong escalation tier
Context size, latency, rate limits
Heavy model; use as escalation, not default
OpenAI gpt-oss-20b (free)
Default support bot, tool calling, structured outputs
Confirm data handling before using for sensitive inputs
Qwen3 4B (free)
High-volume chat, routing, lightweight tasks
Context size, “thinking” mode notes
Needs strict prompts and retrieval
Shortlist: when each model is the right choice
OpenAI gpt-oss-20b (free): best default for most AI bots
If you want one free model that can handle support chats and basic workflows, gpt-oss-20b is a clean default. Use retrieval for facts and enforce structured output for tool calls.
Use it for: customer support, lead qualification, simple API actions
Avoid it for: deep multi-step reasoning when accuracy is critical
Gemini 2.0 Flash Experimental (free): best for speed and long context
If your bot needs to keep long chat history or handle big retrieval payloads, Gemini Flash stands out. Treat it as your “fast lane” model and escalate only when needed.
DeepSeek R1 0528 (free): best reasoning tier
Use R1-style models when the user request is multi-step, ambiguous, or needs careful reasoning. Keep it as an escalation model to protect latency.
Llama 3.3 70B Instruct (free): best strong-answer tier with wide ecosystem
If you want a reliable instruct model and broad ecosystem support, Llama 3.3 70B is a great escalation model for tougher questions.
Gemma 3 27B (free) and Nemotron Nano 2 VL (free): best for vision and documents
If your bot needs to read screenshots and documents, use a model described as multimodal. Also decide how you will handle sensitive documents, because some free endpoints may have logging warnings.
Devstral 2 2512 (free): best for coding agents
If your bot writes code, reads repos, or fixes build errors, use a coding model. Devstral is a good fit for agentic coding workflows.
Qwen2.5-VL 7B (free) and Qwen3 4B (free): best lightweight options
If you need smaller and cheaper inference patterns (or you are building a router), use Qwen3 4B for text tasks and Qwen2.5-VL 7B when you need multimodal on a smaller footprint.
How to choose the best free LLM for your bot in 10 minutes
Step 1: Pick your bot category
Support bot: policies, FAQs, product questions
Lead bot: qualification, routing, contact capture
Workflow bot: function calling, structured actions
Reasoning bot: troubleshooting, triage, decision support
Step 2: Choose a default model (answers 80% of messages)
Pick a fast, stable model. For most bots, that means a Flash-class model or a tool-capable general model.
Step 3: Add an escalation model (only for hard questions)
Add a reasoning-first model (like DeepSeek R1 variants) or a large instruct model (like 70B class) and route only complex requests to it.
Step 4: Decide if you need multimodal
If users can upload images, you need a model described as multimodal. Otherwise you are paying complexity for nothing.
Step 5: Check data handling warnings
Review model page notes for logging warnings and restrictions. Avoid using free endpoints for sensitive user data unless you are confident in the data handling policy.
Bot setup advice that makes free models work in production
1) Use retrieval (RAG) for facts
Free LLMs can be extremely capable, but they can still hallucinate. If your bot answers about pricing, policies, shipping, refunds, or account data, do not rely on “memory.” Use retrieval from your knowledge base and instruct the model to answer only from sources.
Rule: if the answer is not in retrieval results, ask a clarifying question or hand off.
Trust: show short citations or quoted snippets from your docs.
2) Force structured output for workflows
If the bot triggers actions (create lead, open ticket, update customer record), require structured output: JSON only, strict schema, and validation on your backend.
3) Route requests instead of betting on one model
A simple routing policy can outperform any single-model setup:
Default: fast model for normal questions
Escalate: reasoning model when confidence is low or the user asks multi-step questions
Escalate: multimodal model only when an image is present
Escalate: coding model only when the user asks for code or debugging
4) Plan for “free endpoint” reality
Free variants can be rate-limited and can change in availability. Keep a fallback path in your bot:
Retry once
Switch model
Offer human handoff for business-critical requests
FAQ
What is the single best free LLM for most AI bots?
If you need one practical default, start with a fast, tool-capable model and add a reasoning escalation tier for complex messages.
Which free model should I use if I need long context?
Pick a model with a large context window and confirm the number on its OpenRouter model page.
Which free models are best for screenshots and documents?
Use a model described as multimodal, then verify it on your real screenshots and documents before launching.
Are free endpoints safe for sensitive customer data?
It depends. Some free endpoints may have logging warnings. Review the model page and use strict routing for sensitive inputs.
What is the best free LLM for coding agents?
Use a coding-tuned model for code generation and debugging. Keep it as a specialist tier rather than your default.
