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  5. Ling 3.0 Flash Fin (free)vsQwen3.7 Plus
Updated Aug 2026

Ling 3.0 Flash Fin (free)vsQwen3.7 Plus

Ling 3.0 Flash Fin (free) is cheaper than Qwen3.7 Plus at $0/M vs $0.4/M input tokens.

Ling 3.0 Flash Fin (free) and Qwen3.7 Plus compared across 20 shared prompts
SpecLing 3.0 Flash Fin (free)Qwen3.7 Plus
Input priceFree$0.4/M tokens
Output priceFree$1.6/M tokens
Context window262K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)Yes (1 provider)No
ReleasedAug 2026May 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…

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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Qwen3.7 Plus311 words
Creative Name: Midnight Velvet Chocolate Peanut Butter Bites These no-bake, fudgy bites taste like a cross between a chocolate peanut butter cup and a soft oatmeal cookie. They require zero baking, come together in minutes, and use strictly shelf-stable staples you likely already have.
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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…

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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Qwen3.7 Plus1,083 words
Dish Title: Verdant Tide A Study in Anticipation: Langoustine, Unripe Green Strawberry, Tarragon, and Cold-Smoked Ossetra Caviar Conceptual Narrative Verdant Tide captures the fleeting, liminal space between the briny depths of the ocean and the awakening of the early spring forest floor.
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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) –…

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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Qwen3.7 Plus741 words
Here is the simplified LBO underwriting analysis for HarborTech Services (HTS). Assumption: "1.0% annual amortization" is modeled as 1.0% of the beginning-of-year Term Loan balance, which is standard LBO convention unless "of original principal" is specified.
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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…

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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Qwen3.7 Plus1,170 words
If the transistor had been invented and commercialized in 1920 (perhaps by Julius Lilienfeld, who patented the field-effect concept in 1925, or an alternate timeline version of the Bell Labs team), the 20th century would have undergone a profound acceleration.
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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…

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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Qwen3.7 Plus863 words
Here is how a Large Language Model learns and generates text, tailored specifically to the background, skepticism, and priorities of each audience. The Experienced Software Engineer Focus: Distributed systems, latent space, emergent behavior, and addressing the "glorified autocomplete" skepticism.
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Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

Ling 3.0 Flash Fin (free)961 words
The 3 Weakest Claims in the MindMeld AI Pitch 🚨 The $180B TAM Claim (Slide 4) Why it's weak: This is the most logically incoherent claim in the entire deck. The pitch states the BCI market is projected to reach $5.3B by 2030 (citing Grand View Research) — and then immediately claims a TAM of $180B.
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Qwen3.7 Plus733 words
Here are the 3 weakest claims in the MindMeld AI pitch deck, along with an analysis of why they fail and concrete ways to fix them. (Note: These critiques are written from the perspective of a seasoned technical investor or VC, who will immediately scrutinize scientific plausibility and business model alignment).
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Our Verdict
Ling 3.0 Flash Fin (free)
Ling 3.0 Flash Fin (free)
Qwen3.7 Plus
Qwen3.7 Plus

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

Ling 3.0 Flash Fin (free)
Input
$0.000
Output
$0.000
Qwen3.7 Plus
Input
$0.40
Output
$1.60
Where to run it

2 hosts

Ling 3.0 Flash Fin (free)1 host
HostInOutContextUptime
NNovita$0 in·$0 out·262k·100% up
Qwen3.7 Plus1 host
HostInOutContextUptime
Alibaba Cloud$0.32 in·$1.28 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Ling 3.0 Flash Fin (free) is developed by inclusionAI while Qwen3.7 Plus is developed by Qwen. Ling 3.0 Flash Fin (free) has a 262K token context window vs Qwen3.7 Plus's 1.0M. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. Ling 3.0 Flash Fin (free) and Qwen3.7 Plus 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.

Ling 3.0 Flash Fin (free) costs $0/M input tokens and Qwen3.7 Plus costs $0.4/M input tokens. Ling 3.0 Flash Fin (free) is $0.40/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 Ling 3.0 Flash Fin (free) and Qwen3.7 Plus 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

Ling 3.0 Flash Fin (free) logo
Ling 3.0 Flash Fin (free)20 outputs, specs and price
Qwen3.7 Plus logo
Qwen3.7 Plus58 outputs, specs and price
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