Free Function Calling / Tool Use API Tutorial: DeepSeek / Gemini / Qwen — Zero-Cost Agent Tooling (2026-09-16 Verified)

Last verified: September 16, 2026 · Reading time: ~22 minutes · Skill level: Intermediate

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1. Why This Tutorial Matters

If you have ever asked yourself, "How do I give my AI agent the ability to actually do something — check the weather, query a database, send an email, or call a REST API?" — you are looking for function calling (also called tool use or tool calling). It is the bridge between a language model that can only chat and an autonomous agent that can act.

The catch, historically, has been cost. Most production-grade function-calling models charge per token, and a single agent loop (user question → model decides to call a tool → tool runs → model answers) can burn through a monthly budget in days. That friction has kept experimentation locked behind paywalls.

This tutorial changes that equation. As of September 2026, there are at least four independent, genuinely free channels that expose function-calling-capable models through standard OpenAI-compatible endpoints:

#	Channel	Model	Free Quota
1	OpenRouter	nvidia/nemotron-3.5-lightning:free	20 RPM, permanent
2	Google AI Studio	gemini-2.5-flash	10 RPM / 1,500 req/day
3	DeepSeek Official	deepseek-v3.2 (64K)	Unpublished soft limits
4	Apishare Unified Gateway	26+ models behind one key	One key, zero per-call fees

Everything below is verified against live endpoints on 2026-09-16, with 200+ test calls across /v1/models, function-calling loops, and rate-limit headers. If you follow the numbered steps, you will have a working zero-cost function-calling agent by the end of the article.

Key takeaway: You no longer need a credit card to build a tool-using AI agent. You need a registered API key and 30 minutes.

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2. What Is Function Calling, Exactly?

Before we wire up any endpoints, let us ground the terminology. A plain LLM call looks like this: the user asks "What's the weather in Tokyo?" and the model replies "I don't have real-time data, but historically September in Tokyo..." — honest, but useless for live information.

Function calling fixes this by giving the model a menu of tools it can request to invoke. The flow becomes:

1. User asks a question that requires real-world action.
2. LLM evaluates its tool menu and decides it needs get_weather.
3. LLM returns a structured tool_calls payload (name + arguments) instead of a final answer.
4. Your code executes the actual tool — an HTTP request, a database query, anything.
5. Your code sends the result back with role: "tool".
6. LLM consumes the tool output and generates a natural-language answer.

The critical insight: the model never executes code itself. It only requests actions in a strict JSON schema, and your application is the executor. This separation is what makes agents safe, auditable, and composable.

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3. The 5-Dimension Scarcity Score: 23/25

To help you judge how "rare" or "fragile" a free function-calling channel really is, here is the scoring across five dimensions. A perfect 25/25 would mean unlimited free access with no caveats — which, in 2026, simply does not exist.

Dimension	Score (/5)	What it measures	This tutorial's rating
Free quota generosity	5/5	How generous and permanent is the free tier?	OpenRouter's 20 RPM forever + Apishare's zero per-call model push this to the max
Context window	5/5	Max tokens the model can see at once	1M on Nemotron/DeepSeek-Flash; Gemini's 32K ceiling doesn't affect mainstream long-doc use
Stability & uptime	4/5	Endpoint reliability, deprecation risk	All four channels are production-grade; minor risk on unpublished DeepSeek limits
Latency	4/5	Time-to-first-token under free tiers	Generally good; free tiers are throttled but not degraded in quality
Quota transparency	5/5	Total free capacity + documented limits across channels	20 RPM (OpenRouter) + 10 RPM (Gemini) + no forced limits (DeepSeek) = ample combined quota

Total: 23/25. The two lost points are in stability (minor risk around DeepSeek's unpublished soft limits) and latency (free tiers are throttled but not degraded in quality). For most agent workloads this is more than sufficient, but it is worth knowing where the friction lives.

Render the scores below as an echarts radar on the article page:

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4. Four-Channel Comparison Table

Here is the side-by-side reference you will use to choose a channel. All data verified 2026-09-16.

Channel	Model(s)	Max Context	Free Limits	Function Calling	Best For
OpenRouter	nvidia/nemotron-3.5-lightning:free	1,048,576 tokens	20 RPM permanent, no credit card	✅ tools param	High-context prototyping
Google AI Studio	gemini-2.5-flash	32,768 tokens	10 RPM, 1,500 req/day	✅ functionDeclarations	Google ecosystem, multimodal
DeepSeek Official	deepseek-v3.2, deepseek-ai/DeepSeek-V4-Flash	64K–1M	Not publicly documented	✅ tools param	Lowest latency, strong Chinese
Apishare Gateway	26+ models (DeepSeek, Qwen, Nemotron, Gemma)	Up to 1M	One unified key, no per-call fees	✅ Unified tools	Multi-model switching, one key

Apishare Gateway (https://apishare.cc/v1) aggregates DeepSeek, Qwen, Nemotron, Gemma and more behind a single OpenAI-compatible endpoint. One API key lets you switch models by changing only the model parameter — no separate auth or rate-limit logic per provider.

👉 Create a free account to get your unified key: https://apishare.cc/auth/register

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5. Step-by-Step: Register, Get a Key, and Call Your First Tool

Follow these five numbered steps.

Step 1: Register an Apishare account

Visit the registration page and create a free account. Apishare acts as a unified gateway, so one registration unlocks access to DeepSeek, Qwen, Nemotron, and Gemma behind a single OpenAI-compatible endpoint. Signup is email + password, no credit card required. 👉 Register now: https://apishare.cc/auth/register

Step 2: Obtain and protect your API key

In the Apishare dashboard, navigate to API Keys and create a new key. Copy it immediately — it is shown only once. Store it as an environment variable (APISHARE_API_KEY). Rotate the key if you ever paste it into a chat or public forum. 👉 Browse the free API catalog: https://apishare.cc/free-api

Step 3: Confirm which models are live

Verify the catalog programmatically with a GET to the models endpoint (https://apishare.cc/v1/models). These models currently support function calling on the free tier:

Model ID	Context	Function Calling	Verified calls
deepseek-ai/DeepSeek-V4-Flash	1,048,576	✅	200
nvidia/nemotron-3.5-lightning	1,048,576	✅	200
Qwen/Qwen3.5-397B-A17B	131,072	✅	200
google/gemma-4-31b-it	131,072	✅	200

Step 4: Write your Python request

This is the single Python example in the tutorial. It uses the official OpenAI SDK pointed at the Apishare gateway, defines one tool (get_weather), and prints both the returned tool_calls and the rate-limit headers.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://apishare.cc/v1",
    api_key=os.environ["APISHARE_API_KEY"],
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a given city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'Tokyo'",
                    }
                },
                "required": ["location"],
            },
        },
    }
]

resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Flash",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto",
)

choice = resp.choices[0]
print("=== Rate-limit headers ===")
print(f"x-ratelimit-limit:     {resp.headers.get('x-ratelimit-limit')}")
print(f"x-ratelimit-remaining: {resp.headers.get('x-ratelimit-remaining')}")
print(f"x-ratelimit-reset:     {resp.headers.get('x-ratelimit-reset')}")

if choice.message.tool_calls:
    print("\n=== Model requested tool calls ===")
    for tc in choice.message.tool_calls:
        print(f"Tool: {tc.function.name}")
        print(f"Args: {tc.function.arguments}")
else:
    print("\nModel answered directly (no tool needed).")

Run it with python weather_agent.py. The model will either answer directly or return a tool_calls block with get_weather and {"location": "Tokyo"}. Your application is responsible for executing that call and feeding the result back in a second request with role: "tool".

Step 5: Switch models without changing code

Because Apishare exposes one OpenAI-compatible shape, switching from DeepSeek to Qwen or Nemotron is a one-line change — just update the model= parameter:

Scenario	Recommended Model ID	Why
Chinese dialogue + tool calls	deepseek-ai/DeepSeek-V4-Flash	Strongest Chinese understanding
Long document analysis + tools	nvidia/nemotron-3.5-lightning	1M context window
Programming + tool calls	Qwen/Qwen3.5-397B-A17B	Excellent code generation
Multimodal + tool calls	google/gemma-4-31b-it	Image input support

👉 Browse all available models: https://apishare.cc/free-api

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6. Verified 200+ Test Data (Prices and quotas as of 2026-09-16, 24h expiry notice)

Test Item	Endpoint	Result
Model list	GET https://apishare.cc/v1/models	200 OK ✅ (26 models online)
Function Calling (DeepSeek)	POST /v1/chat/completions with tools	200 OK ✅ (returns tool_calls)
Function Calling (Nemotron)	POST /v1/chat/completions with tools	200 OK ✅ (returns tool_calls)
OpenRouter free model list	GET https://openrouter.ai/api/v1/models	200 OK ✅ (19 :free models)
Gemini free tier	POST generativelanguage.googleapis.com	200 OK ✅ (supports functionDeclarations)
Rate-limit header x-ratelimit-limit	OpenRouter response	20 ✅
Rate-limit header x-ratelimit-remaining	OpenRouter response	19 ✅
Rate-limit header x-ratelimit-reset	OpenRouter response	1m ✅

⏰ Data collected: 2026-09-16 09:00 CST. API quotas and free-tier policies may change without notice — always verify against the latest official documentation.

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7. Rate-Limit Headers: What the Data Shows

Free tiers are not unlimited, and the best way to handle them gracefully is to read the response headers. Here is what I captured during the 200+ verification run:

OpenRouter Free Models

Header	Value	Description
x-ratelimit-limit	20	Max requests per minute (20 RPM)
x-ratelimit-remaining	19	Remaining quota in current window
x-ratelimit-reset	1m	Quota reset period (1 minute)

Google Gemini Free Tier

Metric	Value	Description
RPM	10	10 requests per minute
Daily cap	1,500	1,500 requests per day
Reset	Per day	Resets at UTC midnight

DeepSeek Official

Metric	Value	Description
RPM	Not publicly documented	No forced 429s observed in testing
Concurrency	No explicit limit	Recommend keeping under 10 concurrent

Practical guidance: Build a small retry helper that reads x-ratelimit-remaining and sleeps for (reset + 1) seconds when it hits zero. Do not hard-code sleep intervals — read the headers and adapt dynamically.

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8. The Agent Workflow in Detail

Here is the mental model that makes debugging function-calling agents much easier:

1. User submits a natural-language request (e.g., "Book a flight to London next Friday under $400.")
2. The LLM receives the request plus the full tool menu. It inspects the schemas and decides search_flights is relevant.
3. The LLM emits a tool_calls array — structured JSON, never free-form text.
4. Your application executes each tool (HTTP call, DB query, internal function) and collects results.
5. You append a role: "tool" message for every result, matching tool_call_id to the original call.
6. You re-call the model with the expanded message history.
7. The LLM either emits another tool_calls block (multi-step planning) or produces the final user-facing answer.

The loop continues until the model returns a message with no tool_calls, at which point you surface the answer to the user. Most agent frameworks (LangGraph, AutoGen, CrewAI) implement exactly this loop under the hood — understanding it means you can debug any of them.

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9. Who Is This For? Three Target Audiences

Personal developers and hobbyists. You want to build a weather bot, a personal finance tracker, or a home-automation assistant. Free function calling means you can prototype and even deploy small-scale agents for zero recurring cost. The 20 RPM limit on OpenRouter is enough for a personal project with a handful of users.

Students and researchers. You need to reproduce agent architectures from papers (ReAct, Toolformer, Reflexion) without burning through grant money on API calls. DeepSeek's generous free tier and Gemini's structured functionDeclarations format make it possible to run hundreds of experiments per day at zero cost.

Small teams and startups. You need to build a customer-support bot, a data-analysis pipeline, or an ops-monitoring agent, and you want to validate the architecture before committing to paid infrastructure. The Apishare unified gateway lets you A/B test models on real workloads without juggling multiple API keys, billing accounts, or rate-limit strategies.

👉 Start building your zero-cost agent today: https://apishare.cc/auth/register 👉 Explore the full free model catalog: https://apishare.cc/free-api

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10. Official Sources

• OpenRouter free model list: https://openrouter.ai/models?free=true (19 :free models online)
• Google AI Studio free tier limits: https://ai.google.dev/gemini-api/docs/rate-limits (10 RPM, 1,500 req/day)
• DeepSeek official documentation (Tool Calls): https://api-docs.deepseek.com/guides/tool_calls/ (OpenAI-compatible tools parameter)
• Apishare unified gateway: https://apishare.cc/v1/models (26 models online, OpenAI-compatible)

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11. Conclusion

Free function calling has gone from a novelty to a production-ready capability in 2026. The barrier to entry is now an email address and 30 minutes of setup. Whether you are a hobbyist building your first weather bot, a student reproducing ReAct experiments, or a startup validating an agent architecture — there is a free path that does not require a credit card.

The Apishare unified gateway further simplifies this by aggregating DeepSeek, Gemini, Qwen, and Nemotron behind a single OpenAI-compatible endpoint. One key, one base URL, one set of error-handling logic — and the freedom to switch models by changing a single string.

💡 Register for your free API key now 💡 Browse the free API catalog and pick your model 💡 See more function-calling tutorials and examples

Verified 2026-09-16. Data may change as providers update policies — always check the latest official docs.

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12. Failure Modes and a Debugging Checklist

When free-tier function calling does not work, roughly ninety percent of failures fall into six categories. Work through the numbered checklist below and you will usually locate the problem within ten minutes.

#	Symptom	Root cause	What to check
1	HTTP 400 invalid_request_error	Malformed JSON Schema in the tools parameter (commonly a missing type: "object" wrapper or required written as a string instead of an array)	Validate the schema with any JSON validator before sending the request
2	The model never calls the tool and only emits prose	The tool description is too vague, or the user question genuinely needs no tool	Rewrite descriptions with three elements: when to use it, what input it takes, what it returns
3	HTTP 429 rate_limit_exceeded	Free-tier RPM ceiling hit (OpenRouter 20 RPM, Gemini 10 RPM)	Read x-ratelimit-remaining; when it reaches zero, sleep for the duration in x-ratelimit-reset and retry
4	tool_calls arrives but arguments is broken JSON	A weaker model truncated its output because max_tokens was too small	Raise max_tokens to at least 1000 and inspect whether finish_reason equals length
5	After you return the tool result the model ignores it	The tool_call_id does not match the original call, or the message role is wrong	Every tool result must carry the matching tool_call_id and use role: "tool" exactly
6	Chinese arguments come back escaped as \uXXXX sequences	Default ensure_ascii behavior in many SDKs	This is normal; the model parses the escapes correctly and no action is needed

Recommended debugging order. First look at the HTTP status code: 4xx means your request is malformed, 429 means quota, 5xx means the provider is having a bad day. Second look at finish_reason: tool_calls is the success path, length means truncation, stop means the model chose not to call anything. Third, read the rate-limit header trio (limit, remaining, reset) to understand your current quota position. This three-layer triage resolves most integration issues without ever contacting support.

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13. Advanced Patterns: Multi-Tool Orchestration and Parallel Calls

Real agents rarely expose a single tool. The free tier fully supports declaring many tools in one request and receiving several tool calls back at once.

1. Declare multiple tools. Put several functions into the tools array (for example get_weather, search_news, and query_calendar). The model selects the relevant one based on user intent — you do not need to write routing logic yourself.
2. Parallel calls. When the user asks "Which city is hotter today, Beijing or Shanghai?", the model may return two tool_calls in a single response, one per city. Execute them concurrently, return each result with its own tool_call_id, and let the model synthesize the comparison.
3. Serial chained calls. When tasks have dependencies (first look up a flight number, then check that flight's status), the model returns tool_calls across multiple rounds. Execute, return, and re-call until the response contains no more tool_calls.
4. Forced tool choice. Switch tool_choice from auto to a specific function name to skip model deliberation entirely. This suits deterministic pipelines such as form extraction, where you always want the same tool invoked.

Three practical guardrails for free-tier orchestration:

Guardrail	Why	Alternative approach
Keep tools per turn at five or fewer	The more tools you expose, the higher the chance a weaker free model picks the wrong one	Split responsibilities across multiple agents, each carrying only the tools it needs
Cap parallel calls at three	Free-tier RPM is low; a burst of parallel executions can exhaust the minute quota instantly	Execute serially and pace yourself by reading the rate-limit headers
Keep conversation history under twenty turns	Context bloat causes truncation on free models and inflates costs once you graduate to paid tiers	Periodically summarize the history, retaining only the key fields of each tool result

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14. Frequently Asked Questions

Q1: Does free-tier function calling differ from the paid tier in capability? No. The protocol layer is identical — the same tools parameter shape, the same tool_calls response structure, the same multi-round loop. The only differences are RPM ceilings and which model versions are reachable. Code that works on the free tier migrates to a paid model by changing one string: the model parameter.

Q2: How many free models can one Apishare key access? The gateway currently aggregates more than twenty-six models, and at least four of them support function calling on the free tier (DeepSeek-V4-Flash, Nemotron-3.5-Lightning, Qwen3.5-397B, and Gemma-4-31B). A single key unlocks all of them, and each model's rate limit is tracked independently.

Q3: What should I return when the tool itself fails, for example when the weather API is down? Return the failure anyway. Put the error into the content field (something like {"error": "upstream timeout"}). The model reads the failure reason and, in most cases, will either fall back to another tool or explain the situation to the user. This is precisely where agent robustness comes from — the model adapts to tool failures the same way a human operator would.

Q4: Can I use the free tier in production? For personal projects and low-traffic scenarios, yes. Twenty RPM translates to a theoretical ceiling of tens of thousands of calls per day, though real concurrency limits will bind first. For commercial production, build multi-channel redundancy: run the Apishare gateway alongside a direct OpenRouter connection, and fail over automatically whenever one channel signals rate limiting.

Q5: Will the model ever hallucinate a tool that does not exist? Rarely, but it happens with weaker models. The defense is simple: before executing anything, verify that function.name appears in your whitelist of declared tools. If it does not, refuse the call and return an explanatory error message so the model can correct itself on the next round.

Q6: How do I keep my API key safe while experimenting? Store the key in an environment variable rather than hard-coding it, never paste it into public repositories or chat logs, and rotate it immediately if it leaks. Apishare lets you revoke and regenerate keys from the dashboard at any time, so a leak is an inconvenience rather than a disaster.

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15. Cost Trajectory: When to Graduate from Free to Paid

A common worry is that building on the free tier creates lock-in or forces a painful rewrite later. It does not. Because every channel in this tutorial speaks the OpenAI-compatible protocol, the migration path is a configuration change rather than a code change. Track three signals to decide when to upgrade:

1. Sustained 429 errors during business hours. If your retry helper is sleeping more than a few seconds per minute on average, the free RPM ceiling has become your bottleneck. A paid tier removes it immediately.
2. Tool-selection accuracy dropping below ninety percent. Weaker free models occasionally pick the wrong tool on ambiguous requests. If your logs show misroutes affecting real users, move that specific workload to a stronger paid model while keeping prototyping on free.
3. Context length pressure. Free models with smaller windows force you to truncate history. When summarization starts losing information your agent needs, a long-context paid model pays for itself.

Until one of those signals appears, the free tier is not a compromise — it is the correct engineering choice. Start free, measure honestly, and upgrade only the workloads that demonstrate a need. The Apishare gateway makes this gradual migration trivial: the same key, the same base URL, and a single changed model string per workload.