GetInfoUs
Head-to-Head Model Comparison

Nemotron Nano 12B v2 VL vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
साझा करें
Related topics (4)
NVIDIAOpenAIopen-sourceflagship

Compare Nemotron Nano 12B v2 VL by NVIDIA against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

किसी भी मॉडल को बदलें और तुलना करें

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench

Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
NVIDIA

Nemotron Nano 12B v2 VL

ओपन-वेट्स मॉडल

NVIDIA द्वारा विकसित Nemotron Nano 12B v2 VL एक उन्नत AI मॉडल है, जिसमें 128K टोकन का कॉन्टेक्स्ट विंडो और $0.12/1M इनपुट तथा $0.3/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index43 / 100
Input Token:

$0.12 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

31%

OpenAI

GPT-6 Astra

डीप रीजनिंग मॉडल

OpenAI द्वारा विकसित GPT-6 Astra एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $10.0/1M इनपुट तथा $50.0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

एक नज़र में मुख्य अंतर

  • Nemotron Nano 12B v2 VL is 124.2x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 128K tokens).
  • Nemotron Nano 12B v2 VL is faster at 110 tok/s vs 100 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 31.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron Nano 12B v2 VLGPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
4396
Provider
NVIDIAOpenAI
Input Price
Cost per million input tokens
$0.12 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$0.30 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.07 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
110 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
31%82.4%
GPQA Diamond
Graduate-level reasoning
47%80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$1.00 / 1M

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding
GPT-6 Astra
+51.4% SWE-bench lead
Cost
Nemotron Nano 12B v2 VL
124.2x cheaper monthly runtime
Speed
Nemotron Nano 12B v2 VL
+10% faster generation
Context
GPT-6 Astra
272K tokens window

सत्यापित बेंचमार्क स्कोर तुलना

Official evaluation metrics verified by independent third-party research labs.

Nemotron Nano 12B v2 VLGPT-6 Astra
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

31%vs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

47%vs80.9%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

70%vs89.9%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1250ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

27%vs63.8%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.8%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

52%vs93.6%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

66%vs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs100 tok/s

लेटेंसी और रिस्पॉन्स समय

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Nemotron Nano 12B v2 VL0.35s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron Nano 12B v2 VL4.90s
GPT-6 Astra5.95s

रियल-टाइम टोकन लागत सिम्युलेटर

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Nemotron Nano 12B v2 VL Monthly API Bill
$0.49 / month
$0.12/1M in · $0.3/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Nemotron Nano 12B v2 VL saves approximately $61.01 / month (99%) under this workload.124.2x Savings

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

  • NVIDIA की उन्नत Compact Vision-Language Transformer आर्किटेक्चर और 128K कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($0.12/1M इनपुट, $0.3/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

GPT-6 Astra · Strengths & Key Advantages

  • OpenAI की उन्नत Omni-Adaptive Autoregressive with Dynamic CoT आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($10.0/1M इनपुट, $50.0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricNemotron Nano 12B v2 VLGPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.09$2.15
Output Tokens / Task
Tokens generated per task
24K58K
Reasoning Tokens / Task
Thinking tokens burned per task
8K32K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron Nano 12B v2 VLGPT-6 Astra
Official API Model ID (SDK)nemotron-nano-12b-v2-vlgpt-6-astra
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2025-102026-09
Knowledge CutoffAugust 2025June 2026
ArchitectureCompact Vision-Language TransformerOmni-Adaptive Autoregressive with Dynamic CoT
Parameters12BConfidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.12/1M in) across full 128K windowFlat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTLCached input at $0.03/1M tokensAutomatic prefix caching at $1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision, audio
Output Modalitiestexttext, audio

Nemotron Nano 12B v2 VL vs GPT-6 Astra: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, Nemotron Nano 12B v2 VL or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 43 on the composite Intelligence Index.

Which model is faster?

Nemotron Nano 12B v2 VL is faster, generating 110 tokens per second compared with 100 tokens per second.

Which model is cheaper to run?

Nemotron Nano 12B v2 VL is cheaper at $0.07 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron Nano 12B v2 VL has lower latency, with a time to first token of 0.35s compared with 0.95s.

Which model has a larger context window?

GPT-6 Astra has a larger context window, supporting 272K tokens compared with 128K tokens.

निष्कर्ष

Choose Nemotron Nano 12B v2 VL for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और nvidia आधारित प्रोडक्शन सिस्टम, or choose GPT-6 Astra for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और openai आधारित प्रोडक्शन सिस्टम.

अक्सर पूछे जाने वाले प्रश्न (FAQ)

Which is cheaper: Nemotron Nano 12B v2 VL or GPT-6 Astra?+

Nemotron Nano 12B v2 VL costs $0.12/1M input and $0.3/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.

Which model has a larger context window: Nemotron Nano 12B v2 VL or GPT-6 Astra?+

Nemotron Nano 12B v2 VL supports 128K tokens whereas GPT-6 Astra supports 272K tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

स्रोत: NVIDIA & OpenAI Official Documentation