Aya Vision 32B vs DeepSeek-V3-0324
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Aya Vision 32B by Cohere and DeepSeek-V3-0324 by DeepSeek 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, DeepSeek-V3-0324 holds a notable pricing advantage for token ingestion. On output generation, Aya Vision 32B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek-V3-0324 provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, Aya Vision 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.
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Aya Vision 32B
Открытые веса (Open Weights)Aya Vision 32B от Cohere — передовая модель искусственного интеллекта с контекстным окном 16K токенов и стоимостью API $0.35/1M входящих и $0.8/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.35 / 1M
$0.80 / 1M
16K tokens
32%
DeepSeek-V3-0324
Открытые веса (Open Weights)DeepSeek-V3-0324 от DeepSeek — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0.27/1M входящих и $1.1/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.27 / 1M
$1.10 / 1M
128K tokens
52.4%
Ключевые отличия кратко
- Aya Vision 32B is 1.1x cheaper for API usage (input + output tokens combined).
- DeepSeek-V3-0324 has a larger context window (128K vs 16K tokens).
- Aya Vision 32B is faster at 75 tok/s vs 65 tok/s.
- DeepSeek-V3-0324 leads on SWE-bench coding (52.4% vs 32.0%).
| Feature | Aya Vision 32B | DeepSeek-V3-0324 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 45 | 48 |
Provider | Cohere | DeepSeek |
Input Price Cost per million input tokens | $0.35 / 1M | $0.27 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $1.10 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $0.21 / 1M |
Context Window Maximum input + output tokens | 16K tokens~24 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 75 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 32% | 52.4% |
GPQA Diamond Graduate-level reasoning | 51.5% | 63.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.07 / 1M |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Aya Vision 32B · Strengths & Key Advantages
- Современная архитектура High-Capacity Multilingual VLM от Cohere с поддержкой до 16K токенов контекста
- Оптимальная стоимость использования ($0.35/1M вход, $0.8/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
DeepSeek-V3-0324 · Strengths & Key Advantages
- Современная архитектура 671B Mixture-of-Experts (37B active) от DeepSeek с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0.27/1M вход, $1.1/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Aya Vision 32B | DeepSeek-V3-0324 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.18 |
Output Tokens / Task Tokens generated per task | 25K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Vision 32B | DeepSeek-V3-0324 |
|---|---|---|
| Official API Model ID (SDK) | aya-vision-32b | deepseek-chat |
| 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 | 671B Mixture-of-Experts (37B active) |
| Parameters | 32B | 671B (37B active MoE) |
| 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 ($0.27/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.08/1M tokens | Automatic prefix caching at $0.07/1M (no extra cache-write fee) |
| 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) |
| 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) · 416 GB VRAM (4-bit Quantized) · 1443 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 |
Aya Vision 32B vs DeepSeek-V3-0324: Быстрые ответы (FAQ)
Which is more intelligent, Aya Vision 32B or DeepSeek-V3-0324?
DeepSeek-V3-0324 is more intelligent. It scores 48 compared with 45 on the composite Intelligence Index.
Which model is faster?
Aya Vision 32B is faster, generating 75 tokens per second compared with 65 tokens per second.
Which model has lower latency?
Aya Vision 32B has lower latency, with a time to first token of 0.48s compared with 0.65s.
Which model has a larger context window?
DeepSeek-V3-0324 has a larger context window, supporting 128K tokens compared with 16K tokens.
Вердикт
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 DeepSeek-V3-0324 if your workflow prioritizes Cost-sensitive production backends, self-hosted enterprise clusters, and automated batch code translation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does Aya Vision 32B compare to DeepSeek-V3-0324 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 DeepSeek-V3-0324 costs $0.27 input and $1.10 output. Overall, DeepSeek-V3-0324 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 DeepSeek-V3-0324?+
Aya Vision 32B supports a context window of 16K tokens (~12k words), while DeepSeek-V3-0324 supports 128K tokens (~96k words). Therefore, DeepSeek-V3-0324 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Aya Vision 32B or DeepSeek-V3-0324?+
Aya Vision 32B scores 32% on SWE-bench Verified while DeepSeek-V3-0324 scores 52.4%. For autonomous software engineering and multi-file debugging, DeepSeek-V3-0324 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Aya Vision 32B and DeepSeek-V3-0324?+
Aya Vision 32B is optimized for low-latency direct generation. In contrast, DeepSeek-V3-0324 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Aya Vision 32B and DeepSeek-V3-0324?+
Aya Vision 32B generates approximately 75 tokens per second, while DeepSeek-V3-0324 delivers 65 tokens per second. In latency-sensitive production environments, Aya Vision 32B 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 DeepSeek-V3-0324 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-V3-0324 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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs