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  5. DeepSeek V4 FlashvsGLM 5.3
Updated Aug 2026

DeepSeek V4 FlashvsGLM 5.3

DeepSeek V4 Flash is cheaper than GLM 5.3 at $0.14/M vs $0.6538/M input tokens.

DeepSeek V4 Flash and GLM 5.3 compared across 13 shared prompts
SpecDeepSeek V4 FlashGLM 5.3
Input price$0.14/M tokens$0.6538/M tokens
Output price$0.28/M tokens$2.0548/M tokens
Context window1.0M tokens1.3M tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Aug 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 13 prompts both answered. Formatting stripped, words untouched.

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 V4 Flash967 words
This is a fantastic goal. The key for a beginner is consistency over intensity. Trying to overhaul your entire life overnight is a recipe for burnout. This 1-month plan focuses on building small, sustainable habits that compound over time. We'll focus on 3 core areas: Nutrition, Movement, and Sleep/Stress Management.
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…
Read the full answer

Historical Counterfactual Analysis

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

DeepSeek V4 Flash1,987 words
If the transistor had been invented in 1920—a full 27 years earlier—its impact would ripple through every layer of technology, economics, and geopolitics, fundamentally reshaping the 20th century. Below is a detailed tracing of first-, second-, and third-order effects through 1980, organized by domain.
Read the full answer
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…
Read the full answer

Debug This Architecture

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

DeepSeek V4 Flash1,942 words
Failure Modes, Race Conditions, and Scaling Bottlenecks The described architecture has several critical flaws that compromise reliability, consistency, and scalability. Below is a systematic analysis with proposed solutions and their trade-offs.
Read the full answer
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).
Read the full answer

Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

DeepSeek V4 Flash618 words
Based on the pitch deck summary, three claims stand out as particularly weak due to lack of specificity, unrealistic benchmarks, or unverifiable assertions. Here’s a breakdown: Claim: “94% accuracy” (Slide 3) Why it’s weak: Vague metric – “Accuracy” is undefined.
Read the full answer
GLM 5.3585 words
3 Weakest Claims in the MindMeld AI Pitch "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is neurologically incoherent. EEG sensors detect electrical activity on the scalp after neural firing occurs — you cannot decode intent "before" the brain generates it.
Read the full answer

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 V4 Flash1,120 words
To an experienced software engineer (skeptical of "next word prediction") Think of a large language model not as a brain, but as a massive, shared cache of probabilistic completions built from trillions of examples.
Read the full answer
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.
Read the full answer

Ethical Dilemma with Stakeholders

You're the CEO of a pharmaceutical company. Your best-selling drug ($2B annual revenue, used by 4 million patients for chronic pain) has…

DeepSeek V4 Flash1,972 words
CEO’s 48-Hour Action Plan Context: I’ve just learned that our flagship chronic pain drug has a rare but serious liver failure risk (1 in 8,000 patients over 5 years), previously unknown. I have 48 hours until the board meeting and 72 hours until the earnings call.
Read the full answer
GLM 5.31,143 words
The Core Decision (Made Before Hour 1) The side effect is real, serious, and known to me now. Patients are currently taking this drug without informed consent about a ~1-in-8,000 liver failure risk. Everything else — stock price, board pressure, legal exposure — is secondary.
Read the full answer
Our Verdict
DeepSeek V4 Flash
DeepSeek V4 Flash
GLM 5.3
GLM 5.3

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

DeepSeek V4 Flash costs 7.3x less per token.

Too close to call
API pricing

Cost per 1M tokens

DeepSeek V4 Flash
Input
$0.14
4.7× cheaper
Output
$0.28
7.3× cheaper
GLM 5.3
Input
$0.65
Output
$2.05

DeepSeek V4 Flash is cheaper on both: 4.7× input, 7.3× output.

Where to run it

45 hosts, cheapest first

DeepSeek V4 Flash15 hosts
HostInOutContextUptime
OOpenInferencefp8$0.04 in·$0.50 out·1M·99.6% upSStreamLakefp8$0.05 in·$0.10 out·1M·98.2% upDDeepInfrafp8$0.09 in·$0.18 out·1M·99.7% upGGMI Cloudfp8$0.09 in·$0.18 out·1M·99.4% upVVenice$0.10 in·$0.19 out·1M·97.9% upDDigitalOcean$0.10 in·$0.20 out·1M·99.9% up
9 more hostsFewer hosts
SSiliconFlowfp8$0.13 in·$0.28 out·1M·98.5% upAlibaba Cloudfp8$0.13 in·$0.27 out·1M·99.4% upAAtlasCloudfp4$0.14 in·$0.28 out·1M·99.9% upNNovitafp8$0.14 in·$0.28 out·1M·100% upPParasailfp8$0.14 in·$0.28 out·1M·99.7% upNNextBitfp8$0.15 in·$0.30 out·1M·98.3% upMMancerfp8$0.19 in·$0.50 out·1M·97.7% upAzure AI Foundry$0.21 in·$0.56 out·1M·95.7% upBaidu Qianfanfp8degraded$0.04 in·$0.08 out·1M·81.8% up
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 V4 Flash is developed by DeepSeek while GLM 5.3 is developed by Zhipu AI. DeepSeek V4 Flash has a 1.0M 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 V4 Flash 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 V4 Flash costs $0.14/M input tokens and GLM 5.3 costs $0.6538/M input tokens. DeepSeek V4 Flash is $0.51/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 V4 Flash 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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