Llama 4 Behemoth vs Aya Expanse 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Behemoth by Meta against Aya Expanse 32B by Cohere across real-world coding, reasoning, token economics, and latency.
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Llama 4 Behemoth
オープンウェイトモデルMeta が提供する Llama 4 Behemoth は、最大 10M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Aya Expanse 32B
オープンウェイトモデルCohere が提供する Aya Expanse 32B は、最大 128K トークンのコンテキストウィンドウを備え、入力 $0.25/100万トークン・出力 $0.5/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.25 / 1M
$0.50 / 1M
128K tokens
33.5%
ひと目でわかる主な違い
- 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%).
| Feature | Llama 4 Behemoth | Aya Expanse 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 44 |
Provider | Meta | Cohere |
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 text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 64K | 8K |
Throughput Speed Tokens generated per second | 45 tok/s | 85 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 |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Behemoth · Strengths & Key Advantages
- Meta の高度な Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) アーキテクチャと 10M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Aya Expanse 32B · Strengths & Key Advantages
- Cohere の高度な High-Capacity Multilingual Transformer アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.25/1M、出力 $0.5/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | Aya Expanse 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.18 |
Output Tokens / Task Tokens generated per task | 35K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 4 Behemoth | Aya Expanse 32B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | aya-expanse-32b |
| 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) | High-Capacity Multilingual Transformer |
| Parameters | 2T 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 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 ($0.25/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.06/1M 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) · 20 GB VRAM (4-bit Quantized) · 69 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 32B: よくある質問 (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.
総合評価
Choose Llama 4 Behemoth for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、meta エコシステムの統合, or choose Aya Expanse 32B for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、cohere エコシステムの統合.
よくある質問 (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.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
公的ソース: Meta & Cohere Official Documentation