Llama Nemotron Embed VL 1B v2 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama Nemotron Embed VL 1B v2 by NVIDIA against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama Nemotron Embed VL 1B v2
EmbeddingCompact 1B multimodal embedding model for joint text and image vector search, RAG retrieval, and cross-modal clustering.
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
GPT-6 Astra
Autonomous OS AgentOpenAI's late-2026 flagship foundation model featuring native Computer-Use agents, dynamic UI orchestration, and deep STEM reasoning.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Key Differences at a Glance
- Llama Nemotron Embed VL 1B v2 is 1108.1x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 32K tokens).
- Llama Nemotron Embed VL 1B v2 is faster at 280 tok/s vs 100 tok/s.
| Feature | Llama Nemotron Embed VL 1B v2 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 32 | 96 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.02 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.02 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 32K tokens~48 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 2K | 128K |
Throughput Speed Tokens generated per second | 280 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $1.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
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 text queries with diagram and document images
- Ultra-fast vector encoding speed exceeding 280 tokens/sec
- High MTEB benchmark ranking for retrieval and cross-modal search
Considerations & Trade-offs
- Outputs vector embeddings only, not conversational generative text
- 32K context window limited compared to larger document models
GPT-6 Astra · Strengths & Key Advantages
- Flawless OS-level navigation and Computer-Use automation
- Seamless real-time multimodal voice and vision interactivity
- Dynamic Chain-of-Thought that self-adjusts depth based on query difficulty
- Huge 1M context window with instant prefix caching
Considerations & Trade-offs
- Strict safety filters can occasionally throttle complex penetration testing prompts
- Slightly higher reasoning latency on high-effort math queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama Nemotron Embed VL 1B v2 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $2.15 |
Output Tokens / Task Tokens generated per task | 1K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama Nemotron Embed VL 1B v2 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | llama-nemotron-embed-vl-1b-v2 | gpt-6-astra |
| 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 | June 2026 |
| Architecture | Dense Multimodal Embedding Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.02/1M in) across full 32K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.005/1M tokens | Automatic prefix caching at $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) · Audio (~25–32 tokens/sec) |
| 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, audio |
| Output Modalities | embedding | text, audio |
Llama Nemotron Embed VL 1B v2 vs GPT-6 Astra: Quick Answers
Which is more intelligent, Llama Nemotron Embed VL 1B v2 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 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 100 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 $7.70 (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 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 32K tokens.
Verdict
Choose Llama Nemotron Embed VL 1B v2 for enterprise vector databases, multimodal rag retrieval, and semantic image search, or choose GPT-6 Astra for autonomous browser/computer workflows, real-time voice agents, and high-difficulty mathematical reasoning.
Frequently asked questions
Which is cheaper: Llama Nemotron Embed VL 1B v2 or GPT-6 Astra?+
Llama Nemotron Embed VL 1B v2 costs $0.02/1M input and $0.02/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Llama Nemotron Embed VL 1B v2 or GPT-6 Astra?+
Llama Nemotron Embed VL 1B v2 supports 32K tokens whereas GPT-6 Astra supports 272K 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.
Sources: NVIDIA & OpenAI Official Documentation