Head-to-Head Model Comparison

DeepSeek V4.1-Flash vs GLM-5.2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

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DeepSeek V4.1-FlashProfile

GLM-5.2Profile

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DeepSeek

DeepSeek V4.1-Flash

Açık Ağırlıklı Temel Model

DeepSeek tarafından geliştirilen DeepSeek V4.1-Flash; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 110 token/sn hız, 58/100 zeka indeksi ve 1M token başın

Intelligence Index58 / 100

Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

74.2%

Zhipu AI

GLM-5.2

Açık Ağırlıklı Temel Model

Zhipu AI tarafından geliştirilen GLM-5.2; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 60 token/sn hız, 91/100 zeka indeksi ve ücretsiz açık ağırlık erişi

Intelligence Index91 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

67.5%

Bir Bakışta Temel Farklar

GLM-5.2 is 1.0x cheaper for API usage (input + output tokens combined).
DeepSeek V4.1-Flash is faster at 110 tok/s vs 60 tok/s.
DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 67.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	DeepSeek V4.1-Flash	GLM-5.2
Intelligence Index Composite reasoning & knowledge score (0-100)	58	91
Provider	DeepSeek	Zhipu AI
Input Price Cost per million input tokens	$0.30 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.18 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	384K	32K
Throughput Speed Tokens generated per second	110 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	74.2%	67.5%
GPQA Diamond Graduate-level reasoning	90.9%	73.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.00 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

DeepSeek V4.1-Flash

+6.7% SWE-bench lead

Cost

GLM-5.2

1.0x cheaper monthly runtime

Speed

DeepSeek V4.1-Flash

+83% faster generation

Context

Tied

1M tokens window

Doğrulanmış Benchmark Puanları

Official evaluation metrics verified by independent third-party research labs.

DeepSeek V4.1-FlashGLM-5.2

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

74.2%vs67.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

90.9%vs73.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

89.5%vs87%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1455ptsvs1440pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

16.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

31.2%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

48%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

92.4%vsN/A

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

93%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs60 tok/s

Gecikme Süresi ve Yanıt Hızı

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

DeepSeek V4.1-Flash0.46s

GLM-5.20.65s

Est. End-to-End Response (500 tokens)TTFT + generation time

DeepSeek V4.1-Flash5.01s

GLM-5.28.98s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

DeepSeek V4.1-Flash Monthly API Bill

$1.54 / month

$0.3/1M in · $1.2/1M out

GLM-5.2 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

GLM-5.2 saves approximately $1.54 / month (100%) under this workload.1.0x Savings

DeepSeek V4.1-Flash · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 384K çıktı tokenı kapasitesi (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
Sadece 460 ms ilk token gecikmesi (TTFT) ile saniyede 110 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (763B total (8B prefill / 16B decode ))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

GLM-5.2 · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 32K çıktı tokenı kapasitesi (SWE-bench Verified 67.5% · GPQA Diamond 73.5%)
Sadece 650 ms ilk token gecikmesi (TTFT) ile saniyede 60 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (350B MoE)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	DeepSeek V4.1-Flash	GLM-5.2
Cost per Task Estimated USD to finish one task	$0.27	$0.00
Output Tokens / Task Tokens generated per task	58K	30K
Reasoning Tokens / Task Thinking tokens burned per task	28K	12K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	DeepSeek V4.1-Flash	GLM-5.2
Official API Model ID (SDK)	deepseek-reasoner	glm-5-2
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-09	2026-06
Knowledge Cutoff	June 2026	February 2026
Architecture	Causal Encoder-Decoder (20 enc + 20 dec layers)	Massive Million-Token Open MoE Frontier Transformer
Parameters	763B total (8B prefill / 16B decode active)	350B MoE
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($0.3/1M in) across full 1000K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.006/1M (no extra cache-write fee)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text
Output Modalities	text	text

DeepSeek V4.1-Flash vs GLM-5.2: Hızlı Yanıtlar (SSS)

Which is more intelligent, DeepSeek V4.1-Flash or GLM-5.2?

GLM-5.2 is more intelligent. It scores 91 compared with 58 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

GLM-5.2 is cheaper at $0.00 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 0.65s.

Sonuç

Hem DeepSeek V4.1-Flash (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de GLM-5.2 (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: GLM-5.2 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, DeepSeek V4.1-Flash daha yüksek üretim hızı sağlar; GLM-5.2 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için DeepSeek V4.1-Flash mi yoksa GLM-5.2 mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde GLM-5.2 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

DeepSeek V4.1-Flash ve GLM-5.2 API fiyatları nasıl karşılaştırılır?+

DeepSeek V4.1-Flash 1M giriş/çıkış tokeni başına $0.30 / $1.20 ücretlendirilirken, GLM-5.2 $0.00 / $0.00 seviyesindedir. Karma maliyette GLM-5.2 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

DeepSeek V4.1-Flash 1M token bağlam penceresi sunarken GLM-5.2 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise DeepSeek V4.1-Flash öndedir.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

DeepSeek V4.1-Flash vs GLM-5.2

İstediğiniz Modelleri Seçin ve Karşılaştırın

DeepSeek V4.1-Flash

GLM-5.2

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

DeepSeek V4.1-Flash · Strengths & Key Advantages

Considerations & Trade-offs

GLM-5.2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

DeepSeek V4.1-Flash vs GLM-5.2: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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