Kimi K3 vs Aion-3.0
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Kimi K3 by Moonshot AI against Aion-3.0 by AionLabs across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Kimi K3
Raisonneur open source de 2.8TLe géant modèle MoE open source de 2,8T de Moonshot AI avec une fenêtre de contexte de 1 million de tokens et des capacités révolutionnaires de codage.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Aion-3.0
Duo de raisonnementde pointe scientifique modèle de raisonnement conçu pour biochimique recherche, formel mathématique logique, et multi-étapes theorem proving.
$0.50 / 1M
$2.00 / 1M
131.1K tokens
N/A
Différences Clés en un Coup d’Œil
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 131K tokens).
- Aion-3.0 is faster at 80 tok/s vs 58 tok/s.
| Caractéristique | Kimi K3 | Aion-3.0 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 83 |
Provider | Moonshot AI | AionLabs |
Input Price Cost per million input tokens | $0.00 / 1M | $0.50 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.34 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 131K | 16K |
Throughput Speed Tokens generated per second | 58 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | 74.5% | N/A |
GPQA Diamond Graduate-level reasoning | 79% | 63.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.05 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K3 · Strengths & Key Advantages
- Largest à poids ouverts modèle de fondation dans existence (2.8T paramètres)
- 1480 LMSYS Chatbot Arena Elo rivaling propriétaire fermé de pointe leaders
- complet 1M token fenêtre de contexte paired avec massif 131K max sortie tokens
Considerations & Trade-offs
- Extreme matériel prerequisites (distributed multinœud cluster déploiement)
- génère extensive raisonnement traces qui demand careful token économique management
Aion-3.0 · Strengths & Key Advantages
- solide proficiency dans scientifique littérature scientifique, biomédical terminology, et formel logique
- Robust multi-étapes deductive raisonnement capacités
- Clean structuré JSON outputs pour laboratoire pipeline intégration
Considerations & Trade-offs
- Relatively compact 131K fenêtre de contexte par rapport à millions-token peers
- supérieur latence sur heavy raisonnement requests
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K3 | Aion-3.0 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $0.16 |
Output Tokens / Task Tokens generated per task | 35K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 7K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Kimi K3 | Aion-3.0 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k3 | aion-3-0 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | AionLabs REST API (OpenAI-Compatible) |
| Release Date | 2026-07 | 2026-07 |
| Knowledge Cutoff | March 2026 | May 2026 |
| Architecture | Colossus-Scale Multimodal Sparse Mixture-of-Experts | Deliberate Scientific Reasoning Transformer |
| Parameters | 2.8T total (MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.5/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.05/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) | API Prompt Engineering & RAG Distillation |
| 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) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | AionLabs Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Kimi K3 vs Aion-3.0: Réponses Rapides (FAQ)
Which is more intelligent, Kimi K3 or Aion-3.0?
Kimi K3 is more intelligent. It scores 93 compared with 83 on the composite Intelligence Index.
Which model is faster?
Aion-3.0 is faster, generating 80 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $0.34 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aion-3.0 has lower latency, with a time to first token of 0.52s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.
Verdict
Choose Kimi K3 for audits de bases de code à contexte long, analyse de dépôts complets et tâches agentiques autonomes, or choose Aion-3.0 for biotech recherche, chemical synthèse flux de travail, et formel logical analyse.
Questions fréquentes
Which is cheaper: Kimi K3 or Aion-3.0?+
Kimi K3 costs $0/1M input and $0/1M output tokens, while Aion-3.0 costs $0.5/1M input and $2/1M output tokens.
Which model has a larger context window: Kimi K3 or Aion-3.0?+
Kimi K3 supports 1.0M tokens whereas Aion-3.0 supports 131K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Moonshot AI & AionLabs Official Documentation