Head-to-Head Model Comparison

Aya Expanse 32B vs Llama 3.2 90B Vision

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Aya Expanse 32BProfile

Llama 3.2 90B VisionProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Cohere

Aya Expanse 32B

Model Multibahasa Global

Aya Expanse 32B dari Cohere adalah model AI yang dirancang untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, menghadirkan jendela konteks 128K, kecepatan 85 token/detik, indeks kecerdasan 44/100, serta tarif $0.25

Intelligence Index44 / 100

Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

Llama 3.2 90B Vision

AI Multimodal Asli

Llama 3.2 90B Vision dari Meta adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 128K, kecepatan 75 token/detik, indeks kecerdasan 44/100, serta akse

Intelligence Index44 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

28.5%

Perbedaan Utama Sekilas

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Aya Expanse 32B

+5.0% SWE-bench lead

Cost

Llama 3.2 90B Vision

1.0x cheaper monthly runtime

Speed

Aya Expanse 32B

+13% faster generation

Context

Tied

128K tokens window

Skor Benchmark Terverifikasi

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

Aya Expanse 32BLlama 3.2 90B Vision

SWE-bench Verified (Autonomous Coding)

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

33.5%vs28.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

50%vs48.2%

MMLU-Pro (Multi-task Language Understanding)

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

73%vs73.5%

LMSYS Chatbot Arena Elo

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

1265ptsvs1260pts

Long-Context Reasoning

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

72%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs75 tok/s

Latensi & Waktu Respons

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

Aya Expanse 32B0.44s

Llama 3.2 90B Vision0.58s

Est. End-to-End Response (500 tokens)TTFT + generation time

Aya Expanse 32B6.32s

Llama 3.2 90B Vision7.25s

Simulator Biaya Token Interaktif

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Aya Expanse 32B Monthly API Bill

$0.92 / month

$0.25/1M in · $0.5/1M out

Llama 3.2 90B Vision Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.2 90B Vision saves approximately $0.92 / month (100%) under this workload.1.0x Savings

Aya Expanse 32B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 33.5% · GPQA Diamond 50%)
Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 440 ms
Bobot terbuka (32B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Llama 3.2 90B Vision · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 28.5% · GPQA Diamond 48.2%)
Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
Bobot terbuka (90B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Aya Expanse 32B (Sangat cocok untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 3.2 90B Vision (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Aya Expanse 32B unggul dalam penalaran dan pemrograman, Aya Expanse 32B memberikan kecepatan generasi token lebih tinggi, dan Llama 3.2 90B Vision lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Aya Expanse 32B atau Llama 3.2 90B Vision?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Aya Expanse 32B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Aya Expanse 32B vs Llama 3.2 90B Vision?+

Aya Expanse 32B mengenakan biaya $0.25 / $0.50 per 1M token input/output, sedangkan Llama 3.2 90B Vision sebesar $0.00 / $0.00. Secara keseluruhan, Llama 3.2 90B Vision lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Aya Expanse 32B mendukung konteks 128K token dibandingkan 128K token pada Llama 3.2 90B Vision, sementara Aya Expanse 32B unggul dalam kecepatan output token per detik.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Aya Expanse 32B vs Llama 3.2 90B Vision

Ganti & Bandingkan Model Apa Saja

Aya Expanse 32B

Llama 3.2 90B Vision

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Aya Expanse 32B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.2 90B Vision · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Aya Expanse 32B vs Llama 3.2 90B Vision: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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