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  5. GPT-5.4 NanovsLing 3.0 Flash
Updated Jul 2026

GPT-5.4 NanovsLing 3.0 Flash

Ling 3.0 Flash is cheaper than GPT-5.4 Nano at $0.021/M vs $0.2/M input tokens.

GPT-5.4 Nano and Ling 3.0 Flash compared across 16 shared prompts
SpecGPT-5.4 NanoLing 3.0 Flash
Input price$0.2/M tokens$0.021/M tokens
Output price$1.25/M tokens$0.063/M tokens
Context window400K tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedMar 2026Jul 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 16 prompts both answered. Formatting stripped, words untouched.

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 Flash2,510 words
🍽️ TERRA NUDA — A Study in Forest Petrichor A Six-Component Dish Exploring the Ephemeral Moment After Rain Falls on a Living Forest 📖 CONCEPTUAL NARRATIVE Terra Nuda — Latin for "naked earth" — is inspired by the liminal moment immediately after a summer storm passes through a forest: the air thick with petrichor…
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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 +…
Read the full answer
Ling 3.0 Flash568 words
🌱 Your 1-Month Health & Longevity Starter Plan 🥦 1. Diet — Nourish, Don't Deprive Week 1: Awareness Keep a food journal — just track what you eat (no judgment). This alone builds mindful eating habits. Add, don't subtract — introduce one extra serving of vegetables per meal.
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Adversarial Contract Review

Here is a contract for a software consulting engagement: --- SOFTWARE CONSULTING AGREEMENT This Agreement is entered into between TechCorp…

GPT-5.4 Nano1,859 words
I’m not a lawyer, but I can flag contract terms that (a) are unusually one‑sided, (b) create open‑ended or high‑risk obligations, or (c) can be interpreted in ways that materially disadvantage the contractor. I’ll then suggest specific counter‑edits and explain the typical legal/contracting rationale.
Read the full answer
Ling 3.0 Flash2,036 words
Contract Analysis: Exploitative Clauses & Recommended Modifications Clause-by-Clause Analysis SCOPE — Unilateral Modification Without Compensation The Problem: Client can modify the scope at any time and for any reason without paying Contractor for the additional work.
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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…

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 Flash248 words
🍝 Honey Fire Twirls A sticky-sweet, spicy, savory pasta that comes together in minutes. 🧺 Ingredients (7 pantry staples) Spaghetti (or penne) — 8 oz Olive oil — 2 tablespoons Garlic, minced — 4 cloves Honey — 3 tablespoons Soy sauce — 2 tablespoons Red pepper flakes — ½ teaspoon (adjust to taste) Parmesan cheese…
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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 Flash2,291 words
Architecture Analysis: Real-Time Collaborative Document Editor WebSocket & Cross-Server Communication Issue: Siloed WebSocket Servers (Critical) Each API server only broadcasts changes to clients connected to that specific server.
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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 Flash1,129 words
For the Experienced Software Engineer Think of an LLM as a system that learns a massive, multidimensional routing table. When you built distributed systems, you learned patterns from traffic logs — which endpoints get hammered at 3am, which failure modes correlate, which request chains tend to cascade.
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Our Verdict
GPT-5.4 Nano
GPT-5.4 Nano
Ling 3.0 Flash
Ling 3.0 Flash

Not enough votes to call it. On the specs, nothing separates them.

Ling 3.0 Flash costs 20x less per token.

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
Input
$0.02
9.5× cheaper
Output
$0.06
20× cheaper

Ling 3.0 Flash is cheaper on both: 9.5× input, 20× output.

Where to run it

4 hosts, cheapest first

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 Flash2 hosts
HostInOutContextUptime
NNovita$0.02 in·$0.06 out·262k·100% upDDeepInfrabf16$0.06 in·$0.18 out·131k·99.6% 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 is developed by inclusionAI. GPT-5.4 Nano has a 400K token context window vs Ling 3.0 Flash's 262K. You can compare their actual outputs across 16 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 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 16 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 costs $0.021/M input tokens. Ling 3.0 Flash is $0.18/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 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 logo
Ling 3.0 Flash16 outputs, specs and price
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