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  5. DeepSeek V3.2 SpecialevsGLM 5.3
Updated Aug 2026

DeepSeek V3.2 SpecialevsGLM 5.3

DeepSeek V3.2 Speciale is cheaper than GLM 5.3 at $0.28/M vs $0.6538/M input tokens.

DeepSeek V3.2 Speciale and GLM 5.3 compared across 13 shared prompts
SpecDeepSeek V3.2 SpecialeGLM 5.3
Input price$0.28/M tokens$0.6538/M tokens
Output price$0.42/M tokens$2.0548/M tokens
Context window131K tokens1.3M tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedDec 2025Aug 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 13 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 V3.2 Speciale170 words
"Honey-Lemon Bliss Bites" A sweet, tangy, and chewy treat that’s quick to make with pantry staples! Ingredients: 1 cup rolled oats 1/4 cup honey 2 tbsp peanut butter (or any nut/seed butter) 1 tbsp lemon zest (or 1 tsp lemon juice) 1/4 tsp vanilla extract 1/4 cup shredded coconut (optional, for coating) A pinch of salt…
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GLM 5.3210 words
Golden Pantry Fried Rice Total Time: 20 minutes (5 min prep, 15 min cook) Ingredients (7 pantry staples) Cooked white rice (day-old is best) Eggs Soy sauce Garlic (minced or garlic powder) Frozen peas and carrots Canned corn Vegetable oil Instructions Prep (5 min): Break up cold rice with a fork. Drain the corn.
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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…

DeepSeek V3.2 Speciale377 words
Here’s a simple, actionable 1-month plan focusing on diet, exercise, and sleep to kickstart your journey toward better health and longevity. Each area includes beginner-friendly tips to ensure consistency and progress. 1. Diet: Nourish Your Body Goal: Develop healthier eating habits and reduce processed foods.
Read the full answer
GLM 5.3415 words
1-Month Beginner Health & Longevity Plan Week 1: Foundation & Awareness Goal: Establish baselines, not perfection 🥗 Diet Track what you currently eat for 3 days (no judging—just observe) Drink a glass of water upon waking each morning Add ONE vegetable to one meal daily 🏃 Exercise Walk 15 minutes daily (any pace, any…
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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 V3.2 Speciale10,011 words
We need to consider a counterfactual: the transistor invented in 1920 instead of 1947. The transistor is a fundamental building block of modern electronics. Its invention revolutionized computing, communications, and many other fields. Pushing its invention back 27 years would have profound effects.
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GLM 5.31,346 words
If the Transistor Had Arrived in 1920 The Premise: Why 1920 Was Nearly Impossible Before tracing effects, note the counterfactual requires heroic assumptions. The transistor depended on quantum mechanics (solid-state band theory, ~1928-1933), ultrapure semiconductor materials (zone refining, 1950s), and wartime radar…
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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 V3.2 Speciale7,492 words
We need to analyze the given architecture for a real-time collaborative document editor, similar to Google Docs. The description includes components, data flow, sync strategy, and scaling plan. We are asked to identify potential failure modes, race conditions, and scaling bottlenecks.
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GLM 5.31,311 words
Architecture Review: Real-Time Collaborative Editor This design has serious issues across correctness, performance, and security. Let me break them down by severity. 🔴 Critical: Correctness / Data Loss Client-clock-based Last-Write-Wins (LWW) Problem: Client clocks are unreliable (skew, manual changes, timezone bugs).
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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 V3.2 Speciale2,320 words
We need to generate three explanations for how a large language model learns and generates text, each tailored to a different audience: experienced software engineer (no ML), PhD physicist (mathematical precision), and venture capitalist (business evaluation). Each explanation should be 2-3 paragraphs.
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GLM 5.31,164 words
The Software Engineer You've probably written autocomplete logic before, so you're right to be skeptical of the naive version: a Markov chain that looks up "what usually comes after 'the'" is trivially dumb.
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Advanced Longevity Plan (Biohacker)

Develop a comprehensive, cutting-edge 3-month longevity plan for a biohacker aiming to optimize longevity, physical and cognitive…

DeepSeek V3.2 Speciale2,085 words
3-Month Longevity Optimization Plan for the Advanced Biohacker Introduction This plan integrates cutting‑edge nutrition, supplementation, exercise, recovery, stress management, sleep optimization, and data‑driven feedback to maximize healthspan, lifespan, physical performance, and cognitive function.
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GLM 5.3851 words
3-Month Advanced Longevity & Performance Optimization Protocol Note: This is an educational framework. Get baseline bloodwork and consult a physician before starting supplements/fasting, especially with any medical conditions or medications.
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Our Verdict
GLM 5.3
GLM 5.3
DeepSeek V3.2 Speciale
DeepSeek V3.2 SpecialeRunner-up

Not enough votes to call it. On the specs, GLM 5.3 has the edge: newer, bigger context window.

DeepSeek V3.2 Speciale costs 4.9x less per token.

Too close to call
API pricing

Cost per 1M tokens

DeepSeek V3.2 Speciale
Input
$0.28
2.3× cheaper
Output
$0.42
4.9× cheaper
GLM 5.3
Input
$0.65
Output
$2.05

DeepSeek V3.2 Speciale is cheaper on both: 2.3× input, 4.9× output.

Where to run it

30 hosts, cheapest first

DeepSeek V3.2 Speciale

No hosts listed on OpenRouter.

GLM 5.330 hosts
HostInOutContextUptime
Baidu Qianfanfp8$0.65 in·$2.05 out·1M·99.8% upMMorph$0.65 in·$2.06 out·1M·99.6% upRRekafp8$0.76 in·$2.57 out·262k·98.5% upNNovitafp8$0.78 in·$2.46 out·1M·96.4% upIio.netfp8$0.82 in·$2.77 out·262k·99.4% upPPhala$0.84 in·$2.64 out·1M·99.5% up
24 more hostsFewer hosts
DDeepInfrafp4$0.90 in·$3.00 out·1M·97.9% upDDigitalOcean$0.91 in·$2.86 out·1M·80.2% upIInceptronfp4$1.01 in·$3.29 out·1M·98.8% upSSail Researchfp8$1.02 in·$3.29 out·1M·96.7% upGGMI Cloudfp8$1.05 in·$3.30 out·1M·99.1% upSSiliconFlowfp8$1.12 in·$3.52 out·1M·99.6% upAlibaba Cloud$1.19 in·$3.74 out·1M·100% upFFriendli$1.26 in·$3.96 out·1M·100% upAAkashMLfp8$1.30 in·$4.40 out·1M·99.9% upAAtlasCloudfp8$1.40 in·$4.40 out·1M·99.2% upBBasetenfp4$1.40 in·$4.40 out·1M·99.6% upCloudflare Workers AI$1.40 in·$4.40 out·1.3M·100% upCCrusoefp4$1.40 in·$4.40 out·1M·97.4% upFFireworks$1.40 in·$4.40 out·1M·99.6% upMistralnvfp4$1.40 in·$4.40 out·1M·99.8% upModal$1.40 in·$4.40 out·1M·97.7% upPParasailfp8$1.40 in·$4.40 out·1M·98.8% upTTogether$1.40 in·$4.40 out·1M·97.7% upVVenice$1.40 in·$4.40 out·1M·77.5% upWWafer$1.40 in·$4.40 out·1M·99.2% upZ.aifp8$1.40 in·$4.40 out·1M·99.9% upIInferenceNetfp4degraded$0.90 in·$3.00 out·1M·95.8% upMMakorafp4degraded$1.05 in·$4.20 out·980k·92.9% upDDecartfp4degraded$1.19 in·$3.74 out·1M·97.8% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

DeepSeek V3.2 Speciale is developed by DeepSeek while GLM 5.3 is developed by Zhipu AI. DeepSeek V3.2 Speciale has a 131K token context window vs GLM 5.3's 1.3M. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.

It depends on your use case. DeepSeek V3.2 Speciale and GLM 5.3 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.

DeepSeek V3.2 Speciale costs $0.28/M input tokens and GLM 5.3 costs $0.6538/M input tokens. DeepSeek V3.2 Speciale is $0.37/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 V3.2 Speciale and GLM 5.3 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

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