Llama 3.2 1B vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.2 1B by Meta against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Llama 3.2 1B
Minuscule sur appareilultra-compact ouvert modèle conçu pour sur-device périphérie computing, mobile apps, et faible-latence résumé.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
GPT-6 Astra
Agent d'OS autonomed'OpenAI late-2026 phare modèle de fondation intégrant natif utilisation de l'ordinateur agents, dynamic UI orchestration, et profond STEM raisonnement.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Différences Clés en un Coup d’Œil
- Llama 3.2 1B is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 128K tokens).
- Llama 3.2 1B is faster at 320 tok/s vs 100 tok/s.
| Caractéristique | Llama 3.2 1B | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 96 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 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 | 320 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 | $0.00 / 1M | $1.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores 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.
Llama 3.2 1B · Strengths & Key Advantages
- fonctionne easily sur mobile processors et périphérie microcomputers
- 128K fenêtre de contexte
- Lightning rapide débit
Considerations & Trade-offs
- Narrow cognitif capacité
- Struggles avec nuanced logique et complexe syntax
GPT-6 Astra · Strengths & Key Advantages
- Flawless OS-level navigation et utilisation de l'ordinateur automatisation
- Seamless en temps réel multimodal voix et vision interactivity
- Dynamic chaîne de pensée qui self-adjusts profondeur based sur query difficulty
- immense 1M fenêtre de contexte avec instant prefix caching
Considerations & Trade-offs
- Strict sécurité filters peut occasionally throttle complexe penetration testing prompts
- Slightly supérieur raisonnement latence sur élevé-effort mathématiques queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 1B | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 2K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Llama 3.2 1B | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-1B | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | Pruned and distilled ultra-compact 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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) · 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 | text, vision, audio |
| Output Modalities | text | text, audio |
Llama 3.2 1B vs GPT-6 Astra: Réponses Rapides (FAQ)
Which is more intelligent, Llama 3.2 1B or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 42 on the composite Intelligence Index.
Which model is faster?
Llama 3.2 1B is faster, generating 320 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Llama 3.2 1B is cheaper at $0.00 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.2 1B has lower latency, with a time to first token of 0.12s 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 Llama 3.2 1B for local mobile texte correction, offline classification, et battery-sensitive applications, or choose GPT-6 Astra for autonome browser/computer flux de travail, en temps réel voix agents, et élevé-difficulty mathématique raisonnement.
Questions fréquentes
Which is cheaper: Llama 3.2 1B or GPT-6 Astra?+
Llama 3.2 1B costs $0/1M input and $0/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Llama 3.2 1B or GPT-6 Astra?+
Llama 3.2 1B 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: Meta & OpenAI Official Documentation