Llama 4 Behemoth vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Behemoth 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 4 Behemoth
Gigante abierto de 2Tde Meta gargantuan 2-billones parámetro abierto modelo pushing el de frontera de abierto-weight capacidad y 10M contexto.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
GPT-6 Astra
Agente de SO autónomode OpenAI late-2026 insignia modelo fundacional con nativo uso de computadora agentes, dynamic UI orchestration, y profundo STEM razonamiento.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Diferencias Clave de un Vistazo
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 272K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 45 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 75.0%).
| Característica | Llama 4 Behemoth | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 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 | 10M tokens~15,000 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 45 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 75% | 82.4% |
GPQA Diamond Graduate-level reasoning | 82% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Behemoth · Strengths & Key Advantages
- sin rival 2T parámetro abierto capacidad
- Staggering ~10 millones token contexto horizon
- de vanguardia codificación y razonamiento scores
Considerations & Trade-offs
- Demands alto-end datacenter multinodo GPU clústeres (8x H100 minimum)
- Slow inferencia rendimiento en comparación con smaller modelos
GPT-6 Astra · Strengths & Key Advantages
- Flawless OS-level navigation y uso de computadora automatización
- Seamless en tiempo real multimodal voz y visión interactivity
- Dynamic cadena de pensamiento que self-adjusts profundidad based en query difficulty
- enorme 1M ventana de contexto con instant prefix caching
Considerations & Trade-offs
- Strict seguridad filters puede occasionally throttle complejo penetration testing prompts
- Slightly mayor razonamiento latencia en alto-effort matemáticas queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 32K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 4 Behemoth | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-05 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 2T total / 288B active (16-of-2 MoE) | 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 | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · 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) · 1240 GB VRAM (4-bit Quantized) · 4300 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 | text | text, audio |
Llama 4 Behemoth vs GPT-6 Astra: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 4 Behemoth or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 93 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth 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?
GPT-6 Astra has lower latency, with a time to first token of 0.95s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 272K tokens.
Veredicto
Choose Llama 4 Behemoth for national laboratories, grande empresarial ai clústeres, y extreme científico modeling, or choose GPT-6 Astra for autónomo browser/computer flujos de trabajo, en tiempo real voz agentes, y alto-difficulty matemático razonamiento.
Preguntas frecuentes
Which is cheaper: Llama 4 Behemoth or GPT-6 Astra?+
Llama 4 Behemoth 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 4 Behemoth or GPT-6 Astra?+
Llama 4 Behemoth supports 10M 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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Meta & OpenAI Official Documentation