Best Free LLM APIs in 2026: Quotas, Requirements and a Paid Alternative

A free API can be useful for a first chat request and still be the wrong choice for an agent that makes dozens of calls per task. Compare the quota, model capabilities and what happens after the quota runs out—not just whether signup requires a card.

This comparison was checked against public documentation on 17 September 2026. It is a documentation review, not a five-provider performance benchmark. BazaarLink operates this blog and is included in the comparison.

Which options actually have free inference?

My recommendation: start with the service that supports your first required feature. For plain text through an existing OpenAI client, BazaarLink is an option. For a specific open model, compare its availability on OpenRouter and Groq. For Gemini's native features, start with Google's own API. Together belongs in a paid-inference comparison.

BazaarLink: simple chat setup, with a finite quota

The live free-tier page currently shows a base 10 requests/minute and 50 requests/day, with ×1/×2 account-tier multipliers. The actual tier depends on current balance/subscription eligibility and configured thresholds, not merely whether an account once topped up. These are shared free-tier limits, not a separate allowance for every model.

Two models currently appear in the free-quota pool: Qwen3.7 Flash and DeepSeek V4 Flash 0731free. That does not make every model in the paid catalog free. auto:free avoids choosing a particular pool member, but you should choose and validate a specific model when your application requires tool calling or a stable context limit.

After the free quota is exhausted, requests on free-quota models can continue at their normal paid rate if the account meets the paid-fallback conditions; otherwise they are rate-limited. Monitor usage rather than assume every auto:free request costs zero. See the current free-tier rules.

Get a key by signing in at /login, then creating it under /keys. No payment method is required to register. Keep the credential in an environment variable, not in a notebook committed to GitHub.

The following is a configuration example; this revision did not run a live inference request:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.bazaarlink.ai/v1",
    api_key=os.environ["BAZAARLINK_API_KEY"],
)
response = client.chat.completions.create(
    model="auto:free",
    messages=[{"role": "user", "content": "Explain a database index in one sentence."}],
)
print(response.choices[0].message.content)

The legacy https://bazaarlink.ai/api/v1 base URL remains supported. An API-format match does not guarantee that every model supports images, embeddings, structured output or the same tool schema.

OpenRouter: check the account allowance, not a per-model promise

OpenRouter offers a changing selection of free variants. Its current documentation describes daily free-request tiers based on all-time credits purchased; creating more keys does not create extra capacity. The authenticated GET /api/v1/key response reports free_model_daily_requests.used, limit and remaining.

That is more useful than the old claim of “200 requests/day/model.” Check the official limits reference and your key's actual allowance before scheduling a batch. Free variant availability can change, so pinning an ID still requires monitoring the catalog.

Groq: useful limits are model-specific

Groq publishes separate RPM, RPD, TPM and TPD ceilings. A small prompt may hit the request ceiling; a long conversation may hit the token ceiling first. Limits apply to the organization, not each API key.

For example, the public free-plan table currently lists openai/gpt-oss-20b at 30 RPM, 1,000 RPD, 8,000 TPM and 200,000 TPD. Your console is authoritative if the account has an exception. See Groq's rate limits.

Groq is worth evaluating for latency-sensitive applications, but this article has no equivalent-workload measurements to support a “10–20× faster” claim. Measure your own prompt length, output length, time to first token and total duration.

Google Gemini API: check both model eligibility and data use

Google's free tier covers selected models, not the whole Gemini catalog. Its pricing page separates free and paid access by model and feature. It also states that free-tier content is used to improve Google's products, subject to the linked terms. Review that before sending business documents or personal information.

The older Gemini 1.5 examples and a universal “1,500 requests/day” allowance are not a reliable current setup guide. Use the current pricing table and rate-limit reference. Google's OpenAI compatibility guide explains the supported interface; native Gemini features may need the Google SDK.

Together AI: a paid alternative

Together now says it does not offer free trials. Platform access requires at least a US$5 credit purchase and a positive balance. Purchased prepaid credits currently have no expiration date; that is a credit policy, not free inference. See Together's billing documentation.

If fine-tuning or a particular model makes Together relevant to your project, evaluate its paid prices. Do not plan around the old US$25 signup-credit claim.

Budget the task before connecting an agent

A daily request allowance is not a daily task allowance. If one agent task uses eight model calls, a 50-request budget covers at most six complete tasks before retries or other traffic. That is an illustrative calculation, not a measured agent workload.

Before moving beyond a first request:

1. Record the exact model ID and the quota shown for your account.
2. Check required capabilities with a small representative request: plain text first, then tools or structured output if needed.
3. Count calls per completed task, including retries and summarization.
4. Set application concurrency and spending limits. Handle rate-limit responses with backoff rather than a retry loop.
5. Confirm whether over-quota calls stop or become billable.

For practical integration examples, see the LangChain setup, Hermes configuration and OpenClaw configuration. Use free inference to validate a small workflow; choose a paid or self-hosted backend when the workload needs capacity the free plan cannot provide.

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