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.
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Llama Nemotron Embed VL 1B v2
Incrustacióncompacto 1B multimodal embedding modelo para joint texto y imagen vector búsqueda, RAG recuperación, y cross-modal clustering.
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
GPT-6.1 Sol
Valor cercano a Astraofrece results close un GPT-6 Astra un roughly one fifth del precio — el latest 2026 lanzamiento.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Diferencias Clave de un Vistazo
- 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.
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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.
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- Joint multimodal embedding space uniting texto queries con diagram y documento imágenes
- ultra-rápido vector encoding velocidad exceeding 280 tokens/sec
- alto MTEB benchmark ranking para recuperación y cross-modal búsqueda
Considerations & Trade-offs
- Outputs vector embeddings solo, no conversacional generative texto
- 32K ventana de contexto limitado en comparación con larger documento modelos
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results un roughly un fifth del costo
- Excellent codificación y de largo alcance agente rendimiento
- sólido reliability con far menor latencia que Astra
- Near-insignia results un roughly un fifth del precio
Considerations & Trade-offs
- Still marginally behind Astra en el muy hardest razonamiento
- Same precio as GPT-6 Sol, so el upgrade decision es easy
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Choose Llama Nemotron Embed VL 1B v2 for empresarial vector databases, multimodal rag recuperación, y semantic imagen búsqueda, or choose GPT-6.1 Sol for el mejor valor modelo de frontera para profesional y agentic trabajo.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: NVIDIA & OpenAI Official Documentation