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
オープンウェイトモデルNVIDIA が提供する Llama Nemotron Embed VL 1B v2 は、最大 32K トークンのコンテキストウィンドウを備え、入力 $0.02/100万トークン・出力 $0.02/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.02 / 1M
$0.02 / 1M
32K tokens
N/A
GPT-6 Astra
高度な推論モデルOpenAI が提供する GPT-6 Astra は、最大 1M トークンのコンテキストウィンドウを備え、入力 $10.0/100万トークン・出力 $50.0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
ひと目でわかる主な違い
- 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 |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama Nemotron Embed VL 1B v2 · Strengths & Key Advantages
- NVIDIA の高度な Dense Multimodal Embedding Transformer アーキテクチャと 32K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.02/1M、出力 $0.02/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
GPT-6 Astra · Strengths & Key Advantages
- OpenAI の高度な Omni-Adaptive Autoregressive with Dynamic CoT アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $10.0/1M、出力 $50.0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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: よくある質問 (FAQ)
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.
総合評価
Choose Llama Nemotron Embed VL 1B v2 for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、nvidia エコシステムの統合, or choose GPT-6 Astra for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、openai エコシステムの統合.
よくある質問 (FAQ)
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.
公的ソース: NVIDIA & OpenAI Official Documentation