Head-to-Head Model Comparison

GLM-4.5V vs Nemotron Research Reasoning

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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GLM-4.5VProfile

Nemotron Research ReasoningProfile

Multi-Model Arena Stack:

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Popular Matchups:

Zhipu AI

GLM-4.5V

Nativ Multimodale KI

GLM-4.5V von Zhipu AI ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 66K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 73/100 und kostenlosem Open-Weights-Zuga

Intelligence Index73 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

65.5K tokens

SWE-bench Verified:

N/A

NVIDIA

Nemotron Research Reasoning

Open-Weights-Reasoning-Modell

Nemotron Research Reasoning von NVIDIA ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 128K Kontextfenster, 45 Token/s Durchsatz, einem Intelligenz-Index von 68/100 und koste

Intelligence Index68 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.5%

Wichtigste Unterschiede auf einen Blick

Both models cost the same under this workload — choose on capability, not price.
Nemotron Research Reasoning has a larger context window (128K vs 66K tokens).
GLM-4.5V is faster at 85 tok/s vs 45 tok/s.

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

Feature	GLM-4.5V	Nemotron Research Reasoning
Intelligence Index Composite reasoning & knowledge score (0-100)	73	68
Provider	Zhipu AI	NVIDIA
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	65.5K tokens~98 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	16K
Throughput Speed Tokens generated per second	85 tok/s	45 tok/s
SWE-bench Verified Real-world software engineering	N/A	48.5%
GPQA Diamond Graduate-level reasoning	N/A	62%

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

GLM-4.5V

+89% faster generation

Context

Nemotron Research Reasoning

128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GLM-4.5VNemotron Research Reasoning

SWE-bench Verified (Autonomous Coding)

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

N/Avs48.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs62%

MMLU-Pro (Multi-task Language Understanding)

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

69.5%vs82%

LMSYS Chatbot Arena Elo

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

1245ptsvs1340pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs42%

AIME (Advanced Math Competition)

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

N/Avs68%

Long-Context Reasoning

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

N/Avs78%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs45 tok/s

Latenz & Antwortzeit

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

GLM-4.5V0.48s

Nemotron Research Reasoning0.52s

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

GLM-4.5V6.36s

Nemotron Research Reasoning11.63s

Interaktiver Token-Kosten-Simulator

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)

GLM-4.5V Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Nemotron Research Reasoning Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

GLM-4.5V · Strengths & Key Advantages

66K Kontextfenster und bis zu 4K maximale Output-Token (MMLU-Pro 69.5% · Arena Elo 1245)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
Offene Modellgewichte (12B VLM) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Nemotron Research Reasoning · Strengths & Key Advantages

128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 48.5% · GPQA Diamond 62%)
Hohe Generierungsgeschwindigkeit von 45 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
Offene Modellgewichte (70B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

Metric	GLM-4.5V	Nemotron Research Reasoning
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	8K	18K
Reasoning Tokens / Task Thinking tokens burned per task	2K	12K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-4.5V	Nemotron Research Reasoning
Official API Model ID (SDK)	glm-4-5v	nemotron-research-reasoning
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2025-08
Knowledge Cutoff	June 2025	July 2025
Architecture	Open Multimodal Vision-Language Transformer	Research-Grade Dense Transformer
Parameters	12B VLM	70B
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)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	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) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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

GLM-4.5V vs Nemotron Research Reasoning: Schnelle Antworten (FAQ)

Which is more intelligent, GLM-4.5V or Nemotron Research Reasoning?

GLM-4.5V is more intelligent. It scores 73 compared with 68 on the composite Intelligence Index.

Which model is faster?

GLM-4.5V is faster, generating 85 tokens per second compared with 45 tokens per second.

Which model has lower latency?

GLM-4.5V has lower latency, with a time to first token of 0.48s compared with 0.52s.

Which model has a larger context window?

Nemotron Research Reasoning has a larger context window, supporting 128K tokens compared with 65.5K tokens.

Fazit

Sowohl GLM-4.5V (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Nemotron Research Reasoning (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GLM-4.5V führt bei komplexem Reasoning und Coding, GLM-4.5V liefert den höheren Token-Durchsatz und GLM-4.5V ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GLM-4.5V oder Nemotron Research Reasoning?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GLM-4.5V vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GLM-4.5V und Nemotron Research Reasoning?+

GLM-4.5V kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Nemotron Research Reasoning liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist GLM-4.5V günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

GLM-4.5V bietet 65.5K Tokens Kontext gegenüber 128K Tokens bei Nemotron Research Reasoning. Bei der Generierungsgeschwindigkeit liegt GLM-4.5V vorn.

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)

GLM-4.5V vs Nemotron Research Reasoning

Beliebige Modelle wechseln & vergleichen

GLM-4.5V

Nemotron Research Reasoning

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

GLM-4.5V · Strengths & Key Advantages

Considerations & Trade-offs

Nemotron Research Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

GLM-4.5V vs Nemotron Research Reasoning: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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