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  5. GPT-5.4 NanovsLing 3.0 Flash Fin (free)
Updated Aug 2026

GPT-5.4 NanovsLing 3.0 Flash Fin (free)

Ling 3.0 Flash Fin (free) is cheaper than GPT-5.4 Nano at $0/M vs $0.2/M input tokens.

GPT-5.4 Nano and Ling 3.0 Flash Fin (free) compared across 20 shared prompts
SpecGPT-5.4 NanoLing 3.0 Flash Fin (free)
Input price$0.2/M tokensFree
Output price$1.25/M tokensFree
Context window400K tokens262K tokens
WeightsClosed—
Free API (OpenRouter)NoYes (1 provider)
ReleasedMar 2026Aug 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-5.4 Nano184 words
Dish Name: Golden Garlic Chickpea Clouds Pantry Ingredients (7 total) Chickpeas (canned or cooked) – 1 can (15 oz), drained Olive oil – 2 tbsp Garlic – 3 cloves (or 1–2 tsp minced garlic) Lemon – 1 (you’ll use the juice) Salt – 1/2 tsp (plus to taste) Black pepper – 1/4 tsp (plus to taste) Paprika – 1 tsp Estimated…
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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…

GPT-5.4 Nano1,801 words
Below is a structured list of likely failure modes, race conditions, and scaling bottlenecks in your design, with a concrete mitigation and trade-offs for each. (Some issues are “systemic” because the architecture relies on client clocks, per-server WebSockets, polling, and periodic full snapshots.)
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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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Beginner's Longevity Plan

Outline a simple, actionable 1-month plan for someone starting their journey towards better health and longevity. Focus on 3 key areas…

GPT-5.4 Nano785 words
Here’s a simple, actionable 1‑month plan focused on 3 key areas: food, movement, and sleep/stress. It’s designed for beginners and is intentionally realistic. Overall goal (simple) Each week, build consistency in: Food: more whole foods + fewer “defaults” Exercise: daily movement + 2–3 strength sessions/week Sleep +…
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Ling 3.0 Flash Fin (free)825 words
This 1-month plan is designed to build foundational habits without causing burnout. The golden rule of beginner health is "add before you subtract"—focus on adding good habits rather than punishing yourself by removing "bad" ones. Here is your simple, actionable 1-month plan focusing on Diet, Exercise, and Sleep.
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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-5.4 Nano2,226 words
Below is a counterfactual “timeline-style” trace assuming the first practical transistor (or something functionally equivalent) existed in 1920 and that from then on semiconductor device physics, fabrication know-how, and manufacturing learning proceeded along the easiest path consistent with 1920-era science and…
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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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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-5.4 Nano1,568 words
Michelin-Style Concept: “Midnight Sea & Orchard Smoke” A multi-component tasting dish with an unusual pairing: cuttlefish ink + blood orange + smoked apple wood + seaweed umami—elegant, aromatic, and deliberately “contradictory” (ocean meets orchard, darkness meets brightness).
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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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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-5.4 Nano1,050 words
Experienced software engineer (distributed systems / APIs, skeptical about “next-word”) A large language model (LLM) is trained on huge corpora by learning statistical patterns that map a sequence of tokens (text subunits) to likely continuations—so yes, at the core it’s “predict the next token.”
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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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Our Verdict
GPT-5.4 Nano
GPT-5.4 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-5.4 Nano
Input
$0.20
Output
$1.25
Ling 3.0 Flash Fin (free)
Input
$0.000
Output
$0.000
Where to run it

3 hosts

GPT-5.4 Nano2 hosts
HostInOutContextUptime
Azure AI Foundry$0.20 in·$1.25 out·400k·100% upOpenAI$0.20 in·$1.25 out·400k·100% 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-5.4 Nano is developed by OpenAI while Ling 3.0 Flash Fin (free) is developed by inclusionAI. GPT-5.4 Nano has a 400K 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-5.4 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-5.4 Nano 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 GPT-5.4 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-5.4 Nano logo
GPT-5.4 Nano53 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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