Llama 4 Behemoth vs Aya Expanse 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Behemoth by Meta against Aya Expanse 8B by Cohere 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%
Aya Expanse 8B
Multilingüecompacto abierto multilingüe modelo meticulosamente trained un través de 23 idiomas, bridging idioma divides en bajo-resource linguistic environments.
$0.08 / 1M
$0.16 / 1M
8.2K tokens
24%
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 8K tokens).
- Aya Expanse 8B is faster at 135 tok/s vs 45 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 24.0%).
| Característica | Llama 4 Behemoth | Aya Expanse 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 36 |
Provider | Meta | Cohere |
Input Price Cost per million input tokens | $0.00 / 1M | $0.08 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.16 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 10M tokens~15,000 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 64K | 4K |
Throughput Speed Tokens generated per second | 45 tok/s | 135 tok/s |
SWE-bench Verified Real-world software engineering | 75% | 24% |
GPQA Diamond Graduate-level reasoning | 82% | 40% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.02 / 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.
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
Aya Expanse 8B · Strengths & Key Advantages
- diseñado específicamente dataset covering 23 diverse idiomas con superior cultural contexto
- ligero 8B huella running smoothly en consumidor y laptop GPUs
- sólido multilingüe traducción y cross-lingual texto resumen
Considerations & Trade-offs
- ventana de contexto limitado un 8K tokens
- menor capacidad en advanced codificación y competition mathematics
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | Aya Expanse 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.04 |
Output Tokens / Task Tokens generated per task | 35K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 0K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 4 Behemoth | Aya Expanse 8B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | aya-expanse-8b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2024-10 |
| Knowledge Cutoff | February 2026 | August 2024 |
| Architecture | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) | Compact Multilingual Dense Transformer |
| Parameters | 2T total / 288B active (16-of-2 MoE) | 8B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.08/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.02/1M 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) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Llama 4 Behemoth vs Aya Expanse 8B: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 4 Behemoth or Aya Expanse 8B?
Llama 4 Behemoth is more intelligent. It scores 93 compared with 36 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 8B is faster, generating 135 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 $0.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Expanse 8B has lower latency, with a time to first token of 0.30s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 8.2K tokens.
Veredicto
Choose Llama 4 Behemoth for national laboratories, grande empresarial ai clústeres, y extreme científico modeling, or choose Aya Expanse 8B for multilingüe cliente interaction, bajo-resource idioma traducción, y perimetral implementaciones.
Preguntas frecuentes
Which is cheaper: Llama 4 Behemoth or Aya Expanse 8B?+
Llama 4 Behemoth costs $0/1M input and $0/1M output tokens, while Aya Expanse 8B costs $0.08/1M input and $0.16/1M output tokens.
Which model has a larger context window: Llama 4 Behemoth or Aya Expanse 8B?+
Llama 4 Behemoth supports 10M tokens whereas Aya Expanse 8B supports 8K 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 & Cohere Official Documentation