GLM-4.5V vs Nemotron Research Reasoning
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-4.5V da Zhipu AI e Nemotron Research Reasoning da NVIDIA são dois modelos de IA de destaque. O GLM-4.5V oferece uma janela de contexto de 65.5K tokens a $0.00/$0.00 por milhão de tokens, enquanto o Nemotron Research Reasoning oferece 128K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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GLM-4.5V
IA Multimodal NativaO GLM-4.5V da Zhipu AI é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 66K, velocidade de 85 tokens/s, índice de inteligência de 73/100 e tari
$0.00 / 1M
$0.00 / 1M
65.5K tokens
N/A
Nemotron Research Reasoning
Raciocínio de Pesos AbertosO Nemotron Research Reasoning da NVIDIA é um modelo de IA projetado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, oferecendo janela de contexto de 128K, velocidade de 45 tokens/s, índice de inteligência de 68/
$0.00 / 1M
$0.00 / 1M
128K tokens
48.5%
Principais Diferenças em Resumo
- 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.
| 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% |
Quem vence em cada categoria?
At-a-glance decision frameworkEvaluations ongoing
Pontuações de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.5V · Strengths & Key Advantages
- Janela de contexto de 66K com limite máximo de saída de 4K tokens (MMLU-Pro 69.5% · Arena Elo 1245)
- Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 480 ms
- Pesos abertos (12B VLM) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Nemotron Research Reasoning · Strengths & Key Advantages
- Janela de contexto de 128K com limite máximo de saída de 16K tokens (SWE-bench Verified 48.5% · GPQA Diamond 62%)
- Taxa de transferência rápida de 45 tokens/segundo com latência inicial (TTFT) de 520 ms
- Pesos abertos (70B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
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 |
Especificações Técnicas
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: Respostas Rápidas (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.
Veredito
Tanto GLM-4.5V (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) quanto Nemotron Research Reasoning (Indicado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GLM-4.5V lidera em inteligência geral e programação, GLM-4.5V oferece geração mais rápida e GLM-4.5V apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, GLM-4.5V ou Nemotron Research Reasoning?+
Nas avaliações de inteligência e código, GLM-4.5V leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, GLM-4.5V ou Nemotron Research Reasoning?+
O GLM-4.5V custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Nemotron Research Reasoning custa $0.00 / $0.00. No custo combinado, GLM-4.5V é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O GLM-4.5V suporta 65.5K tokens de contexto contra 128K tokens do Nemotron Research Reasoning, enquanto GLM-4.5V entrega maior velocidade de geração.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fontes oficiais: Documentação oficial de API da Zhipu AI e NVIDIA e benchmarks independentes (2026).