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  5. GPT-4.1 NanovsLing 3.0 Flash Fin (free)
Updated Aug 2026

GPT-4.1 NanovsLing 3.0 Flash Fin (free)

Ling 3.0 Flash Fin (free) is cheaper than GPT-4.1 Nano at $0/M vs $0.1/M input tokens.

GPT-4.1 Nano and Ling 3.0 Flash Fin (free) compared across 20 shared prompts
SpecGPT-4.1 NanoLing 3.0 Flash Fin (free)
Input price$0.1/M tokensFree
Output price$0.4/M tokensFree
Context window1.0M tokens262K tokens
WeightsClosed—
Free API (OpenRouter)NoYes (1 provider)
ReleasedApr 2025Aug 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 prompts both answered. Formatting stripped, words untouched.

Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

GPT-4.1 Nano199 words
Dish Name: Savory Sweet Potato & Chickpea Hash Ingredients: Sweet potatoes (2 medium) Canned chickpeas (1 can, drained and rinsed) Olive oil (2 tablespoons) Garlic powder (1 teaspoon) Smoked paprika (1 teaspoon) Salt and pepper (to taste) Honey (1 tablespoon) Estimated Prep/Cook Time: 30 minutes Instructions: Prepare…
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Ling 3.0 Flash Fin (free)166 words
Midnight Molten Lava Cake Ingredients: All-purpose flour (4 tbsp) Granulated sugar (4 tbsp) Unsweetened cocoa powder (2 tbsp) Vegetable oil (3 tbsp) Water (2 tbsp) Vanilla extract (¼ tsp) Baking powder (¼ tsp) Instructions: Combine Dry Ingredients: In a standard microwave-safe mug, whisk together the flour, sugar…
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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

GPT-4.1 Nano742 words
Let's proceed step-by-step. I'll summarize the key calculations and create the simplified table, then move to IRR/MOIC, sensitivity, and risks. Initial Deal Assumptions Recap Entry EBITDA FY2025: $120m Entry EV: 12.0x EBITDA = 12.0 * 120 = $1,440m Transaction fees: 2.0% * $1,440m = $28.8m (paid from equity) Leverage at…
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Ling 3.0 Flash Fin (free)556 words
Entry & Sources & Uses FY2025 EBITDA: $120m Purchase Price (EV): 12.0x × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Total Leverage: 5.5x × $120m = $660m Term Loan: 4.0x × $120m = $480m (9.0% cash interest, 1.0% mandatory amortization = $4.8m/yr) Mezzanine: 1.5x ×…
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Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

GPT-4.1 Nano868 words
This is a fascinating hypothetical scenario. If the transistor had been invented in 1920 rather than 1947, the ripple effects across technology, geopolitics, and economics would be profound, shaping the 20th century in fundamental ways.
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Ling 3.0 Flash Fin (free)2,066 words
The 1920 Transistor: A Counterfactual Analysis Through 1980 I. Premise and Plausibility For a transistor to emerge in 1920, we must assume that either the theoretical framework of semiconductor physics was accelerated (quantum mechanics was only being formulated in the mid-1920s), or that empirical discovery—building…
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Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

GPT-4.1 Nano629 words
1. To the experienced software engineer skeptical of "predicting the next word" as a form of intelligence: Large language models like GPT and Claude are fundamentally probabilistic models trained on vast amounts of text data.
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Ling 3.0 Flash Fin (free)928 words
For the Experienced Software Engineer Here's the thing: the way you build a distributed system that "does the right thing" isn't by writing rules for every possible scenario — it's by designing the system so that correct behavior emerges from many simple, well-specified interactions.
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Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

GPT-4.1 Nano776 words
Let's analyze the architecture step-by-step to identify potential failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs. 1. Potential Failure Modes a.
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Ling 3.0 Flash Fin (free)2,560 words
Comprehensive Architecture Analysis: Real-Time Collaborative Document Editor CRITICAL: WebSocket Broadcasting Is Siloed Per-Server Problem: Each server maintains its own WebSocket connections. When User A (on Server 1) types, the change is only broadcast to clients connected to Server 1.
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Michelin Star Recipe Design

Design an innovative recipe for a multi-component dish worthy of a Michelin 3-star restaurant. The recipe should feature an unusual…

GPT-4.1 Nano685 words
Dish Name: Celestial Harmony – A Deconstructed Black Garlic & Blueberry Soufflé with Yuzu Foam and Edible Silver Dust Conceptual Narrative: This dish embodies the cosmic dance of contrasts—earth and sky, sweetness and umami, tradition and innovation.
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Ling 3.0 Flash Fin (free)2,253 words
L'Archipel des Ombres (The Archipelago of Shadows) Michelin-Caliber Multi-Component Dish 7 Components Conceptual Narrative: Memory, Mineral Essence, and the Meeting of Land and Sea CONCEPTUAL NARRATIVE This dish is an edible cartography—a map of an imaginary archipelago where volcanic soil meets oceanic tides.
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Our Verdict
GPT-4.1 Nano
GPT-4.1 Nano
Ling 3.0 Flash Fin (free)
Ling 3.0 Flash Fin (free)

Not enough votes to call it. On the specs, nothing separates them.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-4.1 Nano
Input
$0.10
Output
$0.40
Ling 3.0 Flash Fin (free)
Input
$0.000
Output
$0.000
Where to run it

3 hosts

GPT-4.1 Nano2 hosts
HostInOutContextUptime
Azure AI Foundry$0.10 in·$0.40 out·1M·99.7% upOpenAI$0.10 in·$0.40 out·1M·99.9% up
Ling 3.0 Flash Fin (free)1 host
HostInOutContextUptime
NNovita$0 in·$0 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GPT-4.1 Nano is developed by OpenAI while Ling 3.0 Flash Fin (free) is developed by inclusionAI. GPT-4.1 Nano has a 1.0M token context window vs Ling 3.0 Flash Fin (free)'s 262K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. GPT-4.1 Nano and Ling 3.0 Flash Fin (free) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 20 challenges so you can judge which fits your needs best.

GPT-4.1 Nano costs $0.1/M input tokens and Ling 3.0 Flash Fin (free) costs $0/M input tokens. Ling 3.0 Flash Fin (free) is $0.10/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

This page shows a side-by-side comparison of GPT-4.1 Nano and Ling 3.0 Flash Fin (free) across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.

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Model pages

GPT-4.1 Nano logo
GPT-4.1 Nano55 outputs, specs and price
Ling 3.0 Flash Fin (free) logo
Ling 3.0 Flash Fin (free)20 outputs, specs and price
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