Nemotron Mini 4B Instruct vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Nemotron Mini 4B Instruct by NVIDIA against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Nemotron Mini 4B Instruct
Modèle de Base à Poids OuvertsNemotron Mini 4B Instruct de NVIDIA est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 128K, une vitesse de 160 tokens/s, un indice d
$0.05 / 1M
$0.15 / 1M
128K tokens
22%
GPT-6 Astra
Modèle Audio et Vocal NatifGPT-6 Astra de OpenAI est un modèle d’IA conçu pour la synthèse vocale naturelle, la transcription audio et le dialogue parlé en temps réel, offrant une fenêtre de contexte de 272K, une vitesse de 100 tokens/s, un indice d’intelligence de 9
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Différences Clés en un Coup d’Œil
- Nemotron Mini 4B Instruct is 256.3x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 128K tokens).
- Nemotron Mini 4B Instruct is faster at 160 tok/s vs 100 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 22.0%).
| Caractéristique | Nemotron Mini 4B Instruct | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 96 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.05 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 160 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 22% | 82.4% |
GPQA Diamond Graduate-level reasoning | 38% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $1.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Mini 4B Instruct · Strengths & Key Advantages
- Fenêtre de contexte de 128K et capacité de sortie maximale de 4K tokens (SWE-bench Verified 22% · GPQA Diamond 38%)
- Débit rapide de 160 tokens/seconde avec une latence au premier token (TTFT) de 250 ms
- Poids ouverts (4B) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
GPT-6 Astra · Strengths & Key Advantages
- Fenêtre de contexte de 272K et capacité de sortie maximale de 128K tokens (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Débit rapide de 100 tokens/seconde avec une latence au premier token (TTFT) de 950 ms
- Tarification API optimisée ($10/$50 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron Mini 4B Instruct | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.15 |
Output Tokens / Task Tokens generated per task | 15K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Nemotron Mini 4B Instruct | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | nemotron-mini-4b | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-08 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Pruned and Distilled Compact Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 4B | 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 ($0.05/1M in) across full 128K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | 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) | 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) · 9 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Nemotron Mini 4B Instruct vs GPT-6 Astra: Réponses Rapides (FAQ)
Which is more intelligent, Nemotron Mini 4B Instruct or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 35 on the composite Intelligence Index.
Which model is faster?
Nemotron Mini 4B Instruct is faster, generating 160 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Nemotron Mini 4B Instruct is cheaper at $0.04 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron Mini 4B Instruct has lower latency, with a time to first token of 0.25s 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.
Verdict
Choose Nemotron Mini 4B Instruct for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs., or choose GPT-6 Astra for conçu pour la synthèse vocale naturelle, la transcription audio et le dialogue parlé en temps réel, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Nemotron Mini 4B Instruct or GPT-6 Astra?+
Nemotron Mini 4B Instruct costs $0.05/1M input and $0.15/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Nemotron Mini 4B Instruct or GPT-6 Astra?+
Nemotron Mini 4B Instruct supports 128K 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: NVIDIA & OpenAI Official Documentation