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Head-to-Head Model Comparison

Llama 4 Behemoth vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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MetaCohereopen-sourceopen-source

Compare Llama 4 Behemoth by Meta against Aya Expanse 32B by Cohere across real-world coding, reasoning, token economics, and latency.

模型直接对比 (VS)

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Meta

Llama 4 Behemoth

开源权重模型

Meta 推出的 Llama 4 Behemoth 人工智能模型，具备 10M 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

10M tokens

SWE-bench Verified:

75%

Cohere

Aya Expanse 32B

开源权重模型

Cohere 推出的 Aya Expanse 32B 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.25/百万 Token，输出价格为 $0.5/百万 Token，专为高质量生产环境、推理与自动化工作流打造。

Intelligence Index44 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

核心差异一览

  • Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
  • Llama 4 Behemoth has a larger context window (10M vs 128K tokens).
  • Aya Expanse 32B is faster at 85 tok/s vs 45 tok/s.
  • Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 33.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureLlama 4 BehemothAya Expanse 32B
Intelligence Index
Composite reasoning & knowledge score (0-100)
9344
Provider
MetaCohere
Input Price
Cost per million input tokens
$0.00 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
10M tokens~15,000 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
64K8K
Throughput Speed
Tokens generated per second
45 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
75%33.5%
GPQA Diamond
Graduate-level reasoning
82%50%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.06 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Llama 4 Behemoth
+41.5% SWE-bench lead
Cost
Llama 4 Behemoth
1.0x cheaper monthly runtime
Speed
Aya Expanse 32B
+89% faster generation
Context
Llama 4 Behemoth
10M tokens window

权威基准测试得分对比

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

Llama 4 BehemothAya Expanse 32B
SWE-bench Verified (Autonomous Coding)

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

75%vs33.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

82%vs50%
MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs73%
LMSYS Chatbot Arena Elo

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

1490ptsvs1265pts
Long-Context Reasoning

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

95%vs72%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs85 tok/s

延迟与响应速度

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 32B0.44s
Est. End-to-End Response (500 tokens)TTFT + generation time
Llama 4 Behemoth12.31s
Aya Expanse 32B6.32s

实时 Token 成本模拟器

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 32B Monthly API Bill
$0.92 / month
$0.25/1M in · $0.5/1M out
Llama 4 Behemoth saves approximately $0.92 / month (100%) under this workload.1.0x Savings

Llama 4 Behemoth · Strengths & Key Advantages

  • 由 Meta 研发的高精度 Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) 架构，支持 10M 上下文窗口
  • 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Aya Expanse 32B · Strengths & Key Advantages

  • 由 Cohere 研发的高精度 High-Capacity Multilingual Transformer 架构，支持 128K 上下文窗口
  • 极具竞争力的 API 定价（输入 $0.25/1M，输出 $0.5/1M），支持高速推理与工具调用
  • 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
  • 超长上下文请求需要规划 Token 成本与并发配额
  • 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果

Token Use & Task Economics

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

MetricLlama 4 BehemothAya Expanse 32B
Cost per Task
Estimated USD to finish one task
$0.00$0.18
Output Tokens / Task
Tokens generated per task
35K28K
Reasoning Tokens / Task
Thinking tokens burned per task
15K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureLlama 4 BehemothAya Expanse 32B
Official API Model ID (SDK)meta-llama/Llama-4-Behemothaya-expanse-32b
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)High-Capacity Multilingual Transformer
Parameters2T total / 288B active (16-of-2 MoE)32B
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 ($0.25/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Cached input at $0.06/1M 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) · 20 GB VRAM (4-bit Quantized) · 69 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 32B: 常见问题解答 (FAQ)

Which is more intelligent, Llama 4 Behemoth or Aya Expanse 32B?

Llama 4 Behemoth is more intelligent. It scores 93 compared with 44 on the composite Intelligence Index.

Which model is faster?

Aya Expanse 32B is faster, generating 85 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 $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Expanse 32B has lower latency, with a time to first token of 0.44s compared with 1.20s.

Which model has a larger context window?

Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 128K tokens.

综合结论

Choose Llama 4 Behemoth for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 meta 生态深度集成, or choose Aya Expanse 32B for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 cohere 生态深度集成.

常见问题 (FAQ)

Which is cheaper: Llama 4 Behemoth or Aya Expanse 32B?+

Llama 4 Behemoth costs $0/1M input and $0/1M output tokens, while Aya Expanse 32B costs $0.25/1M input and $0.5/1M output tokens.

Which model has a larger context window: Llama 4 Behemoth or Aya Expanse 32B?+

Llama 4 Behemoth supports 10M tokens whereas Aya Expanse 32B supports 128K 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

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

数据来源: Meta & Cohere Official Documentation