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  5. DeepSeek V3.1vsLing 3.0 Flash Fin (free)
Updated Aug 2026

DeepSeek V3.1vsLing 3.0 Flash Fin (free)

Ling 3.0 Flash Fin (free) is cheaper than DeepSeek V3.1 at $0/M vs $0.2/M input tokens.

DeepSeek V3.1 and Ling 3.0 Flash Fin (free) compared across 20 shared prompts
SpecDeepSeek V3.1Ling 3.0 Flash Fin (free)
Input price$0.2/M tokensFree
Output price$0.8/M tokensFree
Context window164K tokens262K tokens
WeightsOpen—
Free API (OpenRouter)NoYes (1 provider)
ReleasedAug 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…

DeepSeek V3.1354 words
Of course! Here is a simple, delicious recipe using common pantry staples. Pantry Perfection Pasta This dish is a celebration of simplicity. The humble can of chickpeas is transformed into a crispy, savory topping, while a silky, garlic-infused oil coats the pasta for a meal that feels luxurious but is incredibly easy…
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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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Debug This Architecture

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

DeepSeek V3.11,333 words
Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would lead to data loss, poor performance, and a broken user experience. Here is a detailed breakdown of the failure modes, race conditions, and bottlenecks, along with specific solutions and their trade-offs.
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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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Historical Counterfactual Analysis

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

DeepSeek V3.11,370 words
Of course. This is a fascinating counterfactual. An invention as foundational as the transistor arriving 27 years earlier would have sent shockwaves through every aspect of human society. Here is a detailed trace of the likely implications, focusing on second and third-order effects.
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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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Mini LBO Underwrite

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

DeepSeek V3.1732 words
Of course. Here is the underwriting analysis for the HarborTech Services LBO. Financial Model (FY2026–FY2030) Entry Calculations: FY2025 EBITDA = $120.0m Enterprise Value (EV) at Entry = 12.0 x $120.0m = $1,440.0m Transaction Fees (Uses) = 2.0% x $1,440.0m = $28.8m Total Debt at Close = 5.5 x $120.0m = $660.0m Term…
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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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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…

DeepSeek V3.1687 words
Of course. Here are three tailored explanations of how large language models learn and generate text. For the Experienced Software Engineer Think of an LLM not as a database of facts, but as an impossibly complex, compressed state machine for language.
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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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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…

DeepSeek V3.11,083 words
Of course. Here is an innovative recipe designed to the specifications of a Michelin 3-star restaurant. Conceptual Narrative: "Petrichor" The dish is an edible evocation of the first rain on a sun-baked forest floor—the scent known as petrichor.
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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
DeepSeek V3.1
DeepSeek V3.1
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

DeepSeek V3.1
Input
$0.20
Output
$0.80
Ling 3.0 Flash Fin (free)
Input
$0.000
Output
$0.000
Where to run it

9 hosts, cheapest first

DeepSeek V3.18 hosts
HostInOutContextUptime
DDeepInfrafp4$0.25 in·$0.95 out·164k·99.9% upNNovitafp8$0.27 in·$1.00 out·131k·100% upAAtlasCloudfp8$0.30 in·$0.95 out·131k·98.8% upCCoreWeavefp8$0.55 in·$1.65 out·161k·100% upMMara$0.60 in·$1.70 out·131k·96.9% upSSambaNovafp8$0.65 in·$1.50 out·131k·99.6% up
2 more hostsFewer hosts
SSiliconFlowfp8degraded$0.27 in·$1.00 out·164k·97.9% upGoogle Vertex AIdegraded$0.60 in·$1.70 out·164k·0% 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 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

DeepSeek V3.1 is developed by DeepSeek while Ling 3.0 Flash Fin (free) is developed by inclusionAI. DeepSeek V3.1 has a 164K 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. DeepSeek V3.1 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.

DeepSeek V3.1 costs $0.2/M input tokens and Ling 3.0 Flash Fin (free) costs $0/M input tokens. Ling 3.0 Flash Fin (free) is $0.20/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 DeepSeek V3.1 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

DeepSeek V3.1 logo
DeepSeek V3.154 outputs, specs and price
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Ling 3.0 Flash Fin (free)20 outputs, specs and price
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