Aya Vision 32B vs Aya Vision 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Aya Vision 32B by Cohere and Aya Vision 8B by Cohere 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, Aya Vision 8B holds a notable pricing advantage for token ingestion. On output generation, Aya Vision 8B 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 16K tokens. In latency-sensitive production environments, Aya Vision 8B 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 Vision 32B
오픈 웨이트 모델Cohere의 Aya Vision 32B 모델은 16K 토큰 컨텍스트 윈도우와 입력 $0.35/1M 토큰, 출력 $0.8/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.35 / 1M
$0.80 / 1M
16K tokens
32%
Aya Vision 8B
오픈 웨이트 모델Cohere의 Aya Vision 8B 모델은 16K 토큰 컨텍스트 윈도우와 입력 $0.1/1M 토큰, 출력 $0.25/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.10 / 1M
$0.25 / 1M
16K tokens
22%
한눈에 보는 핵심 차이점
- Aya Vision 8B is 3.2x cheaper for API usage (input + output tokens combined).
- Aya Vision 8B is faster at 110 tok/s vs 75 tok/s.
- Aya Vision 32B leads on SWE-bench coding (32.0% vs 22.0%).
| Feature | Aya Vision 32B | Aya Vision 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 38 |
Provider | Cohere | Cohere |
Input Price Cost per million input tokens | $0.35 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $0.25 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $0.07 / 1M |
Context Window Maximum input + output tokens | 16K tokens~24 A4 pages of text | 16K tokens~24 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 75 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 32% | 22% |
GPQA Diamond Graduate-level reasoning | 51.5% | 41% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.03 / 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.
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 Vision 32B · Strengths & Key Advantages
- Cohere의 첨단 High-Capacity Multilingual VLM 아키텍처와 최대 16K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.35/1M, 출력 $0.8/1M)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
Aya Vision 8B · Strengths & Key Advantages
- Cohere의 첨단 Multilingual Vision-Language Transformer 아키텍처와 최대 16K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.1/1M, 출력 $0.25/1M)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 Vision 32B | Aya Vision 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.06 |
Output Tokens / Task Tokens generated per task | 25K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Vision 32B | Aya Vision 8B |
|---|---|---|
| Official API Model ID (SDK) | aya-vision-32b | aya-vision-8b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-03 | 2025-03 |
| Knowledge Cutoff | January 2025 | January 2025 |
| Architecture | High-Capacity Multilingual VLM | Multilingual Vision-Language Transformer |
| Parameters | 32B | 8B |
| 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 ($0.35/1M in) across full 16K window | Flat rate ($0.1/1M in) across full 16K window |
| Prompt Cache Policy & TTL | Cached input at $0.08/1M tokens | Cached input at $0.03/1M tokens |
| 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) · Vision (~258–765 tokens per image tile) | 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) · 5 GB VRAM (4-bit Quantized) · 17 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, vision |
| Output Modalities | text | text |
Aya Vision 32B vs Aya Vision 8B: 핵심 요약 문답 (FAQ)
Which is more intelligent, Aya Vision 32B or Aya Vision 8B?
Aya Vision 32B is more intelligent. It scores 45 compared with 38 on the composite Intelligence Index.
Which model is faster?
Aya Vision 8B is faster, generating 110 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Aya Vision 8B is cheaper at $0.07 per 1M tokens blended, compared with $0.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Vision 8B has lower latency, with a time to first token of 0.35s compared with 0.48s.
종합 평가
Verdict & Model Selection Recommendation: Choose Aya Vision 32B if your priority is International patent document inspection, global medical chart OCR, and multilingual enterprise analytics. Its performance in demanding tasks justifies its operational token cost. Choose Aya Vision 8B if your workflow prioritizes Global document triage, multilingual receipt and invoice OCR, and mobile visual apps. 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 Vision 32B compare to Aya Vision 8B in API token pricing and cost per token?+
Aya Vision 32B costs $0.35 per 1M input tokens and $0.80 per 1M output tokens, whereas Aya Vision 8B costs $0.10 input and $0.25 output. Overall, Aya Vision 8B 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 Vision 32B or Aya Vision 8B?+
Both Aya Vision 32B and Aya Vision 8B offer an identical context window of 16K tokens (~12k 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 Vision 32B or Aya Vision 8B?+
Aya Vision 32B scores 32% on SWE-bench Verified while Aya Vision 8B scores 22%. For autonomous software engineering and multi-file debugging, Aya Vision 32B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Aya Vision 32B and Aya Vision 8B?+
Aya Vision 32B is optimized for low-latency direct generation. In contrast, Aya Vision 8B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Aya Vision 32B and Aya Vision 8B?+
Aya Vision 32B generates approximately 75 tokens per second, while Aya Vision 8B delivers 110 tokens per second. In latency-sensitive production environments, Aya Vision 8B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Aya Vision 32B and Aya Vision 8B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Aya Vision 8B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Aya Vision 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