Llama Nemotron Embed VL 1B v2 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama Nemotron Embed VL 1B v2 by NVIDIA against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Llama Nemotron Embed VL 1B v2
오픈 웨이트 모델NVIDIA의 Llama Nemotron Embed VL 1B v2 모델은 32K 토큰 컨텍스트 윈도우와 입력 $0.02/1M 토큰, 출력 $0.02/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
GPT-6.1 Sol
심층 추론 모델OpenAI의 GPT-6.1 Sol 모델은 1M 토큰 컨텍스트 윈도우와 입력 $2.0/1M 토큰, 출력 $10.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
한눈에 보는 핵심 차이점
- Llama Nemotron Embed VL 1B v2 is 218.9x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 32K tokens).
- Llama Nemotron Embed VL 1B v2 is faster at 280 tok/s vs 120 tok/s.
| Feature | Llama Nemotron Embed VL 1B v2 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 32 | 94 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.02 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.02 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 2K | 128K |
Throughput Speed Tokens generated per second | 280 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- NVIDIA의 첨단 Dense Multimodal Embedding Transformer 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.02/1M, 출력 $0.02/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
GPT-6.1 Sol · Strengths & Key Advantages
- OpenAI의 첨단 Near-Astra unified reasoning model 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $2.0/1M, 출력 $10.0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama Nemotron Embed VL 1B v2 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $2.05 |
Output Tokens / Task Tokens generated per task | 1K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama Nemotron Embed VL 1B v2 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | llama-nemotron-embed-vl-1b-v2 | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-02 | 2026-09 |
| Knowledge Cutoff | December 2025 | April 2026 |
| Architecture | Dense Multimodal Embedding Transformer | Near-Astra unified reasoning model |
| Parameters | 1B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.02/1M in) across full 32K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.005/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | embedding | text |
Llama Nemotron Embed VL 1B v2 vs GPT-6.1 Sol: 핵심 요약 문답 (FAQ)
Which is more intelligent, Llama Nemotron Embed VL 1B v2 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 32 on the composite Intelligence Index.
Which model is faster?
Llama Nemotron Embed VL 1B v2 is faster, generating 280 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Llama Nemotron Embed VL 1B v2 is cheaper at $0.01 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama Nemotron Embed VL 1B v2 has lower latency, with a time to first token of 0.08s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 32K tokens.
종합 평가
Choose Llama Nemotron Embed VL 1B v2 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 nvidia 기반 프로덕션 파이프라인, or choose GPT-6.1 Sol for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: Llama Nemotron Embed VL 1B v2 or GPT-6.1 Sol?+
Llama Nemotron Embed VL 1B v2 costs $0.02/1M input and $0.02/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama Nemotron Embed VL 1B v2 or GPT-6.1 Sol?+
Llama Nemotron Embed VL 1B v2 supports 32K tokens whereas GPT-6.1 Sol supports 1M tokens.
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.
공식 출처: NVIDIA & OpenAI Official Documentation