GLM-4.5V vs Nemotron Research Reasoning
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-4.5V dari Zhipu AI dan Nemotron Research Reasoning dari NVIDIA adalah dua model AI unggulan. GLM-4.5V menawarkan jendela konteks 65.5K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Nemotron Research Reasoning menyediakan konteks 128K token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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GLM-4.5V
AI Multimodal AsliGLM-4.5V dari Zhipu AI adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 66K, kecepatan 85 token/detik, indeks kecerdasan 73/100, serta akses gratis
$0.00 / 1M
$0.00 / 1M
65.5K tokens
N/A
Nemotron Research Reasoning
Penalaran Bobot TerbukaNemotron Research Reasoning dari NVIDIA adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 128K, kecepatan 45 token/detik, indeks kecerdasan
$0.00 / 1M
$0.00 / 1M
128K tokens
48.5%
Perbedaan Utama Sekilas
- 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% |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.5V · Strengths & Key Advantages
- Jendela konteks 66K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 69.5% · Arena Elo 1245)
- Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
- Bobot terbuka (12B VLM) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Nemotron Research Reasoning · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 48.5% · GPQA Diamond 62%)
- Kecepatan generasi tinggi 45 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
- Bobot terbuka (70B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik GLM-4.5V (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Nemotron Research Reasoning (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GLM-4.5V unggul dalam penalaran dan pemrograman, GLM-4.5V memberikan kecepatan generasi token lebih tinggi, dan GLM-4.5V lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, GLM-4.5V atau Nemotron Research Reasoning?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-4.5V memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API GLM-4.5V vs Nemotron Research Reasoning?+
GLM-4.5V mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Nemotron Research Reasoning sebesar $0.00 / $0.00. Secara keseluruhan, GLM-4.5V lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
GLM-4.5V mendukung konteks 65.5K token dibandingkan 128K token pada Nemotron Research Reasoning, sementara GLM-4.5V unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Sumber resmi: Dokumentasi API resmi Zhipu AI & NVIDIA serta evaluasi benchmark independen (2026).