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Head-to-Head Model Comparison

Llama 4 Behemoth vs Aya Expanse 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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MetaCohereopen-sourceopen-source

Compare Llama 4 Behemoth by Meta against Aya Expanse 8B by Cohere across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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Llama 4 Behemoth

Géant open source de 2T

de Meta gargantuan 2-billions paramètre ouvert modèle pushing le de pointe d'ouvert-weight capacité et 10M contexte.

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

10M tokens

SWE-bench Verified:

75%

Cohere

Aya Expanse 8B

Multilingue

compact ouvert multilingue modèle méticuleusement trained à travers 23 langues, bridging langue divides dans faible-resource linguistic environments.

Intelligence Index36 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.16 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

24%

Différences Clés en un Coup d’Œil

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueLlama 4 BehemothAya Expanse 8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
9336
Provider
MetaCohere
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 text8.2K tokens~12 A4 pages of text
Max Output Tokens
Per single request
64K4K
Throughput Speed
Tokens generated per second
45 tok/s135 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

Qui gagne où ?

At-a-glance decision framework
Code
Llama 4 Behemoth
+51.0% SWE-bench lead
Coût
Llama 4 Behemoth
1.0x cheaper monthly runtime
Vitesse
Aya Expanse 8B
+200% faster generation
Contexte
Llama 4 Behemoth
10M tokens window

Scores de Benchmarks Vérifiés

Official evaluation metrics verified by independent third-party research labs.

Llama 4 BehemothAya Expanse 8B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

75%vs24%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

82%vs40%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

88.5%vs62%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1490ptsvs1205pts
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

95%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs135 tok/s

Latence et Temps de Réponse

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Llama 4 Behemoth1.20s
Aya Expanse 8B0.30s
Est. End-to-End Response (500 tokens)TTFT + generation time
Llama 4 Behemoth12.31s
Aya Expanse 8B4.00s

Simulateur Interactif de Coût de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Llama 4 Behemoth Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Aya Expanse 8B Monthly API Bill
$0.29 / month
$0.08/1M in · $0.16/1M out
Llama 4 Behemoth saves approximately $0.29 / month (100%) under this workload.1.0x Savings

Llama 4 Behemoth · Strengths & Key Advantages

  • sans égal 2T paramètre ouvert capacité
  • Staggering ~10 millions token contexte horizon
  • d'avant-garde codage et raisonnement scores
Considerations & Trade-offs
  • Demands élevé-end datacenter multinœud GPU clusters (8x H100 minimum)
  • Slow inférence débit par rapport à smaller modèles

Aya Expanse 8B · Strengths & Key Advantages

  • spécialement conçu dataset covering 23 diverse langues avec supérieur cultural contexte
  • léger 8B empreinte running smoothly sur grand public et laptop GPUs
  • solide multilingue traduction et cross-lingual texte résumé
Considerations & Trade-offs
  • fenêtre de contexte limité à 8K tokens
  • inférieur capacité sur advanced codage et competition mathematics

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricLlama 4 BehemothAya Expanse 8B
Cost per Task
Estimated USD to finish one task
$0.00$0.04
Output Tokens / Task
Tokens generated per task
35K18K
Reasoning Tokens / Task
Thinking tokens burned per task
15K0K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueLlama 4 BehemothAya Expanse 8B
Official API Model ID (SDK)meta-llama/Llama-4-Behemothaya-expanse-8b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-052024-10
Knowledge CutoffFebruary 2026August 2024
ArchitectureColossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)Compact Multilingual Dense Transformer
Parameters2T 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 CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Cached input at $0.02/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Llama 4 Behemoth vs Aya Expanse 8B: Réponses Rapides (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.

Verdict

Choose Llama 4 Behemoth for national laboratories, grand d'entreprise ai clusters, et extreme scientifique modeling, or choose Aya Expanse 8B for multilingue client interaction, faible-resource langue traduction, et périphérie déploiements.

Questions fréquentes

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.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Meta & Cohere Official Documentation