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Aya Vision 32B vs DeepSeek-V3-0324

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Aya Vision 32B vs DeepSeek-V3-0324: Benchmarks, Pricing & Speed Comparison

Aya Vision 32B von Cohere und DeepSeek-V3-0324 von DeepSeek gehören zu den führenden KI-Modellen. Aya Vision 32B bietet ein Kontextfenster von 16K Tokens bei API-Preisen von $0.35/$0.80 pro 1M Tokens, während DeepSeek-V3-0324 128K Tokens Kontext zu $0.27/$1.10 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Cohere

Aya Vision 32B

Mehrsprachiges KI-Modell

Aya Vision 32B von Cohere ist ein KI-Modell für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen mit 16K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 45/100 und $0.35/$0.8 pro 1M

Intelligence Index45 / 100
Input Token:

$0.35 / 1M

Output Token:

$0.80 / 1M

Context Window:

16K tokens

SWE-bench Verified:

32%

DeepSeek

DeepSeek-V3-0324

Open-Weights-Basismodell

DeepSeek-V3-0324 von DeepSeek ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $0.27/$1.1 pro 1M Token

Intelligence Index48 / 100
Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.4%

Wichtigste Unterschiede auf einen Blick

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureAya Vision 32BDeepSeek-V3-0324
Intelligence Index
Composite reasoning & knowledge score (0-100)
4548
Provider
CohereDeepSeek
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
4K8K
Throughput Speed
Tokens generated per second
75 tok/s65 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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
DeepSeek-V3-0324
+20.4% SWE-bench lead
Cost
Aya Vision 32B
1.1x cheaper monthly runtime
Speed
Aya Vision 32B
+15% faster generation
Context
DeepSeek-V3-0324
128K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Aya Vision 32BDeepSeek-V3-0324
SWE-bench Verified (Autonomous Coding)

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

32%vs52.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

51.5%vs63.5%
MMLU-Pro (Multi-task Language Understanding)

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

74%vs78.4%
LMSYS Chatbot Arena Elo

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

1280ptsvs1320pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs10.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs34.8%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs32.1%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs54.8%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs72%
Long-Context Reasoning

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

N/Avs71.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs65 tok/s

Latenz & Antwortzeit

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 Vision 32B0.48s
DeepSeek-V3-03240.65s
Est. End-to-End Response (500 tokens)TTFT + generation time
Aya Vision 32B7.15s
DeepSeek-V3-03248.34s

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Aya Vision 32B Monthly API Bill
$1.36 / month
$0.35/1M in · $0.8/1M out
DeepSeek-V3-0324 Monthly API Bill
$1.50 / month
$0.27/1M in · $1.1/1M out
Aya Vision 32B saves approximately $0.14 / month (9%) under this workload.1.1x Savings

Aya Vision 32B · Strengths & Key Advantages

  • 16K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 32% · GPQA Diamond 51.5%)
  • Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (32B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

DeepSeek-V3-0324 · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 52.4% · GPQA Diamond 63.5%)
  • Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 650 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (671B (37B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricAya Vision 32BDeepSeek-V3-0324
Cost per Task
Estimated USD to finish one task
$0.22$0.18
Output Tokens / Task
Tokens generated per task
25K45K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureAya Vision 32BDeepSeek-V3-0324
Official API Model ID (SDK)aya-vision-32bdeepseek-chat
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-032025-03
Knowledge CutoffJanuary 2025January 2025
ArchitectureHigh-Capacity Multilingual VLM671B Mixture-of-Experts (37B active)
Parameters32B671B (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 CloudUnlimited (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 windowFlat rate ($0.27/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.08/1M tokensAutomatic prefix caching at $0.07/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Aya Vision 32B vs DeepSeek-V3-0324: Schnelle Antworten (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.

Fazit

Sowohl Aya Vision 32B (Optimal für präzise mehrsprachige Übersetzung, globale Kommunikation und sprachübergreifende Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch DeepSeek-V3-0324 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: DeepSeek-V3-0324 führt bei komplexem Reasoning und Coding, Aya Vision 32B liefert den höheren Token-Durchsatz und Aya Vision 32B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Aya Vision 32B oder DeepSeek-V3-0324?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt DeepSeek-V3-0324 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Aya Vision 32B und DeepSeek-V3-0324?+

Aya Vision 32B kostet $0.35 / $0.80 pro 1M Input-/Output-Tokens, DeepSeek-V3-0324 liegt bei $0.27 / $1.10. Beim gewichteten Gesamtpreis ist Aya Vision 32B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Aya Vision 32B bietet 16K Tokens Kontext gegenüber 128K Tokens bei DeepSeek-V3-0324. Bei der Generierungsgeschwindigkeit liegt Aya Vision 32B vorn.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Offizielle Quellen: Offizielle API-Dokumentation von Cohere und DeepSeek, Systemkarten und unabhängige Benchmark-Auswertungen (2026).