o4-mini-deep-research vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o4-mini-deep-research by OpenAI against Kimi K3 by Moonshot AI 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.
o4-mini-deep-research
Chercheur économiqueun cheaper profond-recherche modèle conçu sur o4-mini pour long searches et report drafting.
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
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%
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 200K tokens).
- o4-mini-deep-research is faster at 70 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 48.5%).
| Caractéristique | o4-mini-deep-research | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 100K | 131K |
Throughput Speed Tokens generated per second | 70 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 68% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores 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.
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.
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.
o4-mini-deep-research · Strengths & Key Advantages
- Roughly five times cheaper que o3-profond-recherche
- Handles long multi-source recherche fonctionne end à end
- bon enough pour routine monitoring et briefs
- bon citations discipline pour le prix point
Considerations & Trade-offs
- moins profondeur et nuance dans synthèse que o3-profond-recherche
- plus prone à missing subtle contradictions à travers sources
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini-deep-research | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.10 | $2.00 |
Output Tokens / Task Tokens generated per task | 75K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 30K | 18K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | o4-mini-deep-research | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | o4-mini-deep-research | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-07 |
| Knowledge Cutoff | June 2024 | March 2026 |
| Architecture | Compact agentic reasoning transformer | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 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 ($2/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
o4-mini-deep-research vs Kimi K3: Réponses Rapides (FAQ)
Which is more intelligent, o4-mini-deep-research or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 48 on the composite Intelligence Index.
Which model is faster?
o4-mini-deep-research is faster, generating 70 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 $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K3 has lower latency, with a time to first token of 0.72s compared with 2.40s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 200K tokens.
Verdict
Choose o4-mini-deep-research for élevé-volume recherche briefs où coût par run dominates, or choose Kimi K3 for audits de bases de code à contexte long, analyse de dépôts complets et tâches agentiques autonomes.
Questions fréquentes
Which is cheaper: o4-mini-deep-research or Kimi K3?+
o4-mini-deep-research costs $2/1M input and $8/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o4-mini-deep-research or Kimi K3?+
o4-mini-deep-research supports 200K tokens whereas Kimi K3 supports 1.0M 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: OpenAI & Moonshot AI Official Documentation