Ling Tiny alternatives: free Flash Fin versus paid Flash

Which model should replace Ling Tiny?

For a free Ling Tiny API alternative, consider Ling 3.0 Flash Fin: as of August 28, 2026, LLMTR offers it with a daily quota. For a general application migration, paid Ling 3.0 Flash remains the successor named in the retirement policy. Adding Fin did not change that decision; the models should not be treated as equivalent.

The inclusionai/ling-3.0-tiny API identifier was retired on August 15, 2026. It returns model_retired instead of automatically forwarding requests. Tiny itself has not disappeared: InclusionAI still publishes MIT-licensed weights. Running those locally is a separate option with hardware and operating responsibilities.

Compare the three options consistently

This table reflects the LLMTR catalog on August 28, 2026. Fin is a version of Flash adapted for finance. Being a candidate for document review does not establish equivalent results on every Tiny or Flash task.

Free access and sustainable budgets are different questions

Free Fin access does not mean unlimited usage or a permanent price guarantee. A daily quota can interrupt an evaluation. This article promises no request count or RPM allowance; check the current model card and account usage before running jobs. Defer quota-blocked work or use a previously approved alternative instead of repeatedly retrying.

The pricing list checked on August 28, 2026 and the LLMTR catalog list Flash at USD 0.06 for input, USD 0.012 for cache reads and USD 0.18 for output, per million tokens. The cache rate applies only to tokens actually read from cache. Disclose any move to paid Flash; do not silently add paid continuation to a free workflow.

Keep reasoning enabled for forced Fin tool calls

Both Flash models reason by default on LLMTR. Disable reasoning with reasoning_effort set to none or a :none model suffix. Do not carry over Tiny's former :fast suffix or assume other reasoning_effort levels are supported.

Fin has a specific restriction: with reasoning disabled, required or a named function in tool_choice produces LLMTR's 400 invalid_request, with detail reason tool_choice_requires_reasoning. Keep reasoning enabled to force a tool call. Use auto when reasoning must remain disabled, but auto does not guarantee a call. Flash does not have this restriction. This distinction comes from LLMTR's contract and the recorded Fin measurement of August 27, 2026; no new live test was performed here.

Separate the context window from the output budget

LLMTR lists a 262,144-token context window and a 32,768-token response ceiling for both Flash and Fin. Do not set max_tokens to 262144. Reasoning consumes the output budget too; an overly small allowance can leave no room for the final answer.

Both models' LLMTR contracts cover text input and output. Do not assume image, audio or enforced JSON Schema support. Convert financial documents to text first, compare extracted amounts with the source, and ask the model to leave missing values unresolved.

Prepare a small Fin request through LLMTR

Requests may be processed on third-party infrastructure outside Türkiye and logged by the provider. Do not submit confidential, personal or customer data. This example asks for a generic checklist; it is not a recorded live result.

Configure LLMTR_BASE_URL with the complete API base URL and LLMTR_API_KEY with your LLMTR key. The code appends only /chat/completions. It displays the finish reason and usage rather than prompt or response text.

A Fin request with reasoning disabled and no forced tool call

import json
import os
from urllib.request import Request, urlopen

base_url = os.environ["LLMTR_BASE_URL"].rstrip("/")
payload = {
    "model": "inclusionai/ling-3.0-flash-fin",
    "messages": [{
        "role": "user",
        "content": "List five data fields to check when reviewing a public annual report."
    }],
    "reasoning_effort": "none",
    "max_tokens": 1024
}
request = Request(
    base_url + "/chat/completions",
    data=json.dumps(payload).encode("utf-8"),
    headers={
        "Authorization": "Bearer " + os.environ["LLMTR_API_KEY"],
        "Content-Type": "application/json"
    },
    method="POST"
)
with urlopen(request, timeout=60) as response:
    result = json.load(response)
print({
    "finish_reason": result["choices"][0]["finish_reason"],
    "usage": result.get("usage")
})

Make the migration decision with a small evaluation

Apply the same anonymous tasks to both models. Define expected behavior first, such as refusing to invent an amount absent from a document or actually calling a required tool.

• Record the model identifier, reasoning setting and output budget together.
• Check function names, arguments and the answer after returning tool results.
• Distinguish quota errors from invalid identifiers and unsupported parameters.
• Keep only content-free measurements such as success rate, duration and usage.
• Approve the budget before switching to paid access; do not roll back to retired Tiny.

Frequently asked questions

Is Flash Fin the new name for Tiny?

No. It is a separate model adapted for finance. Tiny's LLMTR API identifier is retired, and the policy still names paid Flash as its successor.

Why choose Flash when Fin is free?

Your evaluation may favor Flash for general tasks. A workflow requiring forced tool selection with reasoning disabled also falls outside Fin's current contract.

Can downloading Tiny weights bypass the free API quota?

Local inference does not consume LLMTR API quota, but hardware and operating costs are yours. Available weights do not reactivate the old LLMTR API identifier.

Related posts

• Can Ling Tiny run on a Mac? Ollama and MLX support status
• Ling 3.0 Tiny: migrating the retired LLMTR API route to Flash
• Ling-3.0-Tiny download guide: choosing BF16, FP8 or INT4

• inclusionai/ling-3.0-tiny
• inclusionai/ling-3.0-flash-fin
• inclusionai/ling-3.0-flash
• Model catalog
• Chat Completions guide
• OpenAI / OpenRouter