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  5. Ling 3.0 Flash Fin (free)vsQwen3.6 35B A3B
Updated Aug 2026

Ling 3.0 Flash Fin (free)vsQwen3.6 35B A3B

Ling 3.0 Flash Fin (free) is cheaper than Qwen3.6 35B A3B at $0/M vs $0.1612/M input tokens.

Ling 3.0 Flash Fin (free) and Qwen3.6 35B A3B compared across 20 shared prompts
SpecLing 3.0 Flash Fin (free)Qwen3.6 35B A3B
Input priceFree$0.1612/M tokens
Output priceFree$0.9653/M tokens
Context window262K tokens262K tokens
Weights—Open
Free API (OpenRouter)Yes (1 provider)No
ReleasedAug 2026Apr 2026
Side by side

Same prompt, both answers

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

Debug This Architecture

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

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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Qwen3.6 35B A3B1,016 words
Here’s a systematic breakdown of the critical failure modes, race conditions, and scaling bottlenecks in this architecture, along with specific solutions and their trade-offs. 🔴 1. WebSocket Routing & Load Balancing Problem: Round-robin LB + per-server WebSocket state + no sticky sessions.
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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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Qwen3.6 35B A3B538 words
Simplified Year-by-Year Model (FY2026–FY2030) All figures in $m. Assumptions: TL amort = 1.0% of beginning balance; Mezz PIK compounds to principal; ΔNWC = 0.5% of incremental revenue; Cash Tax = 25% × max(0, EBITDA – Cash Interest).
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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.
Read the full answer
Qwen3.6 35B A3B1,014 words
Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of why they fail scrutiny and how to fix them. The Technical Claim: "94% Accuracy" & "Any Language" on EEG Where: Slide 3 (Solution) The Claim: The headband decodes neural patterns into text with 94% accuracy and works with any…
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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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Qwen3.6 35B A3B236 words
🍜 Golden Honey-Garlic Snap Noodles A quick, sticky-yet-chewy noodle dish that balances sweet, salty, and spicy flavors. It's comforting, fast, and uses ingredients you likely already have.
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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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Qwen3.6 35B A3B775 words
For the Experienced Software Engineer Training an LLM is essentially a massively parallelized optimization job. You feed billions of text tokens into a transformer architecture, which computes a differentiable function across ~100B parameters.
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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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Qwen3.6 35B A3B1,149 words
To trace this counterfactual plausibly, we must first acknowledge a critical constraint: invention ≠ adoption. In 1920, semiconductor physics, crystal growth, doping techniques, photolithography, and clean-room manufacturing did not exist.
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Our Verdict
Ling 3.0 Flash Fin (free)
Ling 3.0 Flash Fin (free)
Qwen3.6 35B A3B
Qwen3.6 35B A3BRunner-up

Not enough votes to call it. On the specs, Ling 3.0 Flash Fin (free) has the edge: bigger model tier, newer.

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
Qwen3.6 35B A3B
Input
$0.16
Output
$0.97
Where to run it

10 hosts, cheapest first

Ling 3.0 Flash Fin (free)1 host
HostInOutContextUptime
NNovita$0 in·$0 out·262k·100% up
Qwen3.6 35B A3B9 hosts
HostInOutContextUptime
DDarkbloomfp4$0.05 in·$0.70 out·262k·99.9% upAAkashMLfp8$0.10 in·$0.90 out·262k·100% upVVenicefp8$0.10 in·$1.00 out·256k·99.7% upPParasailfp8$0.15 in·$1.00 out·262k·99.6% upAAtlasCloudfp8$0.19 in·$1.11 out·262k·99.8% upPPhala$0.20 in·$1.27 out·262k·97.6% up
3 more hostsFewer hosts
SSiliconFlowfp8$0.24 in·$1.80 out·262k·94.5% upCCoreWeavefp8$0.25 in·$1.25 out·262k·100% upDDeepInfrafp8degraded$0.10 in·$0.95 out·262k·85.3% 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 Qwen3.6 35B A3B is developed by Qwen. Ling 3.0 Flash Fin (free) has a 262K token context window vs Qwen3.6 35B A3B'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. Ling 3.0 Flash Fin (free) and Qwen3.6 35B A3B 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 Qwen3.6 35B A3B costs $0.1612/M input tokens. Ling 3.0 Flash Fin (free) is $0.16/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 Qwen3.6 35B A3B 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
Qwen3.6 35B A3B logo
Qwen3.6 35B A3B58 outputs, specs and price
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