Llama 4 Behemoth vs Aion-3.0
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Behemoth by Meta against Aion-3.0 by AionLabs across real-world coding, reasoning, token economics, and latency.
Cambia y Compara Cualquier Modelo
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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%
Aion-3.0
Dúo de razonamientode frontera científico modelo de razonamiento diseñado para bioquímico investigación, formal matemático lógica, y multipaso theorem proving.
$0.50 / 1M
$2.00 / 1M
131.1K tokens
N/A
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 131K tokens).
- Aion-3.0 is faster at 80 tok/s vs 45 tok/s.
| Característica | Llama 4 Behemoth | Aion-3.0 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 83 |
Provider | Meta | AionLabs |
Input Price Cost per million input tokens | $0.00 / 1M | $0.50 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.34 / 1M |
Context Window Maximum input + output tokens | 10M tokens~15,000 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 64K | 16K |
Throughput Speed Tokens generated per second | 45 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 75% | N/A |
GPQA Diamond Graduate-level reasoning | 82% | 63.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.05 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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.
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
Aion-3.0 · Strengths & Key Advantages
- sólido proficiency en científico literatura científica, biomédico terminology, y formal lógica
- Robust multipaso deductive razonamiento capacidades
- Clean estructurado JSON outputs para laboratorio canalización integración
Considerations & Trade-offs
- Relatively compacto 131K ventana de contexto en comparación con millones-token peers
- mayor latencia en heavy razonamiento requests
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | Aion-3.0 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.16 |
Output Tokens / Task Tokens generated per task | 35K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 7K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 4 Behemoth | Aion-3.0 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | aion-3-0 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | AionLabs REST API (OpenAI-Compatible) |
| Release Date | 2026-05 | 2026-07 |
| Knowledge Cutoff | February 2026 | May 2026 |
| Architecture | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) | Deliberate Scientific Reasoning Transformer |
| 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 | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.5/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.05/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| 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) | AionLabs Production Safety & Alignment Verified |
| 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 |
| Output Modalities | text | text |
Llama 4 Behemoth vs Aion-3.0: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 4 Behemoth or Aion-3.0?
Llama 4 Behemoth is more intelligent. It scores 93 compared with 83 on the composite Intelligence Index.
Which model is faster?
Aion-3.0 is faster, generating 80 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 $0.34 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aion-3.0 has lower latency, with a time to first token of 0.52s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 131.1K tokens.
Veredicto
Choose Llama 4 Behemoth for national laboratories, grande empresarial ai clústeres, y extreme científico modeling, or choose Aion-3.0 for biotech investigación, chemical síntesis flujos de trabajo, y formal logical análisis.
Preguntas frecuentes
Which is cheaper: Llama 4 Behemoth or Aion-3.0?+
Llama 4 Behemoth costs $0/1M input and $0/1M output tokens, while Aion-3.0 costs $0.5/1M input and $2/1M output tokens.
Which model has a larger context window: Llama 4 Behemoth or Aion-3.0?+
Llama 4 Behemoth supports 10M tokens whereas Aion-3.0 supports 131K 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 & AionLabs Official Documentation