Aya Expanse 32B vs Llama 3.2 90B Vision
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Aya Expanse 32B by Cohere and Llama 3.2 90B Vision by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.2 90B Vision holds a notable pricing advantage for token ingestion. On output generation, Llama 3.2 90B Vision is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Aya Expanse 32B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
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Aya Expanse 32B
오픈 웨이트 모델Cohere의 Aya Expanse 32B 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.25/1M 토큰, 출력 $0.5/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.25 / 1M
$0.50 / 1M
128K tokens
33.5%
Llama 3.2 90B Vision
오픈 웨이트 모델Meta의 Llama 3.2 90B Vision 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.0/1M 토큰, 출력 $0.0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
128K tokens
28.5%
한눈에 보는 핵심 차이점
- Llama 3.2 90B Vision is 1.0x cheaper for API usage (input + output tokens combined).
- Aya Expanse 32B is faster at 85 tok/s vs 75 tok/s.
- Aya Expanse 32B leads on SWE-bench coding (33.5% vs 28.5%).
| Feature | Aya Expanse 32B | Llama 3.2 90B Vision |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 44 |
Provider | Cohere | Meta |
Input Price Cost per million input tokens | $0.25 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.50 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.14 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 33.5% | 28.5% |
GPQA Diamond Graduate-level reasoning | 50% | 48.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Aya Expanse 32B · Strengths & Key Advantages
- Cohere의 첨단 High-Capacity Multilingual Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.25/1M, 출력 $0.5/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Llama 3.2 90B Vision · Strengths & Key Advantages
- Meta의 첨단 Multimodal Adapter-Fused GQA Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.0/1M, 출력 $0.0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Aya Expanse 32B | Llama 3.2 90B Vision |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $0.20 |
Output Tokens / Task Tokens generated per task | 28K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Expanse 32B | Llama 3.2 90B Vision |
|---|---|---|
| Official API Model ID (SDK) | aya-expanse-32b | meta-llama/Llama-3.2-90B-Vision |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-10 | 2024-09 |
| Knowledge Cutoff | August 2024 | December 2023 |
| Architecture | High-Capacity Multilingual Transformer | Multimodal Adapter-Fused GQA Transformer |
| Parameters | 32B | 90B |
| 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) | Flat rate ($0.25/1M in) across full 128K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 56 GB VRAM (4-bit Quantized) · 194 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 | text, vision |
| Output Modalities | text | text |
Aya Expanse 32B vs Llama 3.2 90B Vision: 핵심 요약 문답 (FAQ)
Which model is faster?
Aya Expanse 32B is faster, generating 85 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Llama 3.2 90B Vision 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 0.58s.
종합 평가
Verdict & Model Selection Recommendation: Choose Aya Expanse 32B if your priority is Global enterprise communications, international legal translation, and academic research. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.2 90B Vision if your workflow prioritizes Self-hosted OCR extraction, visual document analysis, and private enterprise multimodal agents. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Aya Expanse 32B compare to Llama 3.2 90B Vision in API token pricing and cost per token?+
Aya Expanse 32B costs $0.25 per 1M input tokens and $0.50 per 1M output tokens, whereas Llama 3.2 90B Vision costs $0.00 input and $0.00 output. Overall, Llama 3.2 90B Vision is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Aya Expanse 32B or Llama 3.2 90B Vision?+
Both Aya Expanse 32B and Llama 3.2 90B Vision offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Aya Expanse 32B or Llama 3.2 90B Vision?+
Aya Expanse 32B scores 33.5% on SWE-bench Verified while Llama 3.2 90B Vision scores 28.5%. For autonomous software engineering and multi-file debugging, Aya Expanse 32B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Aya Expanse 32B and Llama 3.2 90B Vision?+
Aya Expanse 32B is optimized for low-latency direct generation. In contrast, Llama 3.2 90B Vision focuses on standard fast inference.
How does inference speed (tokens per second) compare between Aya Expanse 32B and Llama 3.2 90B Vision?+
Aya Expanse 32B generates approximately 85 tokens per second, while Llama 3.2 90B Vision delivers 75 tokens per second. In latency-sensitive production environments, Aya Expanse 32B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Aya Expanse 32B and Llama 3.2 90B Vision to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.2 90B Vision to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Aya Expanse 32B. This tiered routing pattern cuts overall API bills by up to 70%.
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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs