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  5. Ling 3.0 Flash Fin (free)vsLlama 3.1 70B (Instruct)
Updated Aug 2026

Ling 3.0 Flash Fin (free)vsLlama 3.1 70B (Instruct)

Ling 3.0 Flash Fin (free) is cheaper than Llama 3.1 70B (Instruct) at $0/M vs $0.59/M input tokens.

Ling 3.0 Flash Fin (free) and Llama 3.1 70B (Instruct) compared across 20 shared prompts
SpecLing 3.0 Flash Fin (free)Llama 3.1 70B (Instruct)
Input priceFree$0.59/M tokens
Output priceFree$0.79/M tokens
Context window262K tokens128K tokens
Weights—Open
Free API (OpenRouter)Yes (1 provider)No
ReleasedAug 2026Jul 2024
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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Llama 3.1 70B (Instruct)311 words
Here is a simple recipe for a delicious dish using only 7 common pantry ingredients: Dish Name: "Sunset Crunch Bites" Ingredients: 1 cup rolled oats 1/2 cup peanut butter 1/4 cup honey 1/4 cup chopped peanuts 1/2 teaspoon salt 1/4 teaspoon cinnamon 1/4 cup chocolate chips (semi-sweet) Instructions: Preheat your oven to…
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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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Llama 3.1 70B (Instruct)336 words
Year-by-Year Table (FY2026–FY2030) Year Revenue EBITDA Cash Interest Cash Taxes Capex ΔNWC FCF Ending Debt 2026 $972m $136m $36m ($32m TL, $4m Mezz) $2.8m $29m $4.9m $63m $1,050m ($720m TL, $330m Mezz) 2027 $1,038m $155m $37m ($33m TL, $4m Mezz) $6.3m $31m $5.2m $76m $974m ($654m TL, $320m Mezz) 2028 $1,103m $176m $38m…
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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…

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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Llama 3.1 70B (Instruct)612 words
Potential Failure Modes: Single Point of Failure (SPOF) in Load Balancer: If the load balancer fails, all incoming traffic will be lost. Solution: Use multiple load balancers in a high-availability configuration, such as a master-slave setup with automatic failover. Trade-off: Additional complexity and cost.
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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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Llama 3.1 70B (Instruct)653 words
Dish Name: "Aurora Australis" - A Harmonious Union of Smoked Eel, Saffron-Infused Sunchokes, and Frozen Finger Lime Pearls Conceptual Narrative: Inspired by the breathtaking display of the Southern Lights, "Aurora Australis" is a dish that embodies the ethereal beauty of the Antarctic night sky.
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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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Llama 3.1 70B (Instruct)729 words
For the experienced software engineer: You're likely familiar with the concept of prediction in distributed systems, where a model predicts the likelihood of a certain event or outcome. Large language models like GPT or Claude take this concept to the next level by predicting the next word in a sequence of text.
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Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

Ling 3.0 Flash Fin (free)771 words
INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: Equity Research Date: October 24, 2025 Subject: LedgerLift (LLLT) – Short Recommendation Recommendation & Thesis Recommendation: Short 12-Month PT Range: $30 – $38 Thesis: LedgerLift’s 123% NRR and 18-month CAC payback mask an eventual growth…
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Llama 3.1 70B (Instruct)574 words
Investment Committee Memo Recommendation: Long 12-month PT range: $60-$75 Thesis: LedgerLift's strong B2B spend management and AP automation SaaS platform, with a loyal customer base and efficient growth model, positions the company for sustained success, driven by increasing adoption of digital payment solutions and…
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Our Verdict
Ling 3.0 Flash Fin (free)
Ling 3.0 Flash Fin (free)
Llama 3.1 70B (Instruct)
Llama 3.1 70B (Instruct)

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
Llama 3.1 70B (Instruct)
Input
$0.59
Output
$0.79
Where to run it

3 hosts, cheapest first

Ling 3.0 Flash Fin (free)1 host
HostInOutContextUptime
NNovita$0 in·$0 out·262k·100% up
Llama 3.1 70B (Instruct)2 hosts
HostInOutContextUptime
DDeepInfrafp8$0.40 in·$0.40 out·131k·96.7% upAmazon Bedrock$0.72 in·$0.72 out·131k·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 Llama 3.1 70B (Instruct) is developed by Meta AI. Ling 3.0 Flash Fin (free) has a 262K token context window vs Llama 3.1 70B (Instruct)'s 128K. 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 Llama 3.1 70B (Instruct) 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 Llama 3.1 70B (Instruct) costs $0.59/M input tokens. Ling 3.0 Flash Fin (free) is $0.59/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 Llama 3.1 70B (Instruct) 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
Llama 3.1 70B (Instruct) logo
Llama 3.1 70B (Instruct)56 outputs, specs and price
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