Kimi K3 vs DeepSeek V4 Pro: AI Model Comparison
Compare Kimi K3 and DeepSeek V4 Pro on 2.8T MoE reasoning, 93.5% GPQA, coding benchmarks, 199 tok/s speed, and token cost economics.
Quick Verdict
Choose Kimi K3 for deep scientific reasoning, complex academic proofs, and advanced bilingual document synthesis. Choose DeepSeek V4 Pro for high-speed software development, CI/CD test automation, and maximum cost efficiency.
The battle for supremacy among Chinese open-weights frontier models is led by Moonshot AI's Kimi K3 and DeepSeek's V4 Pro. Kimi K3 deploys a massive 2.8T Mixture-of-Experts architecture delivering exceptional long-context scientific reasoning (93.5% GPQA), while DeepSeek V4 Pro optimizes generation velocity (199 tok/s) and coding execution at a record-low $0.48/M blended token price.
Models at a Glance
Kimi K3
by Moonshot AI
Pay-as-you-go
Moonshot Platform
DeepSeek-V4 Pro
by DeepSeek
Pay-as-you-go
DeepSeek Platform
Capabilities Comparison
| Capability | Kimi K3 | DeepSeek-V4 Pro |
|---|---|---|
| Text Generation | ||
| Code Generation | ||
| Image Generation | ||
| Vision / Image Understanding | ||
| Video Generation | ||
| Audio / Voice Generation | ||
| Web Browsing / Search | ||
| Code Execution | ||
| Function Calling | ||
| Structured Output (JSON) | ||
| Advanced Reasoning (CoT) | ||
| File Upload & Analysis | ||
| Fine-Tuning | ||
| Plugins / Extensions | ||
| Memory / History | ||
| Agentic Capabilities | ||
| Custom Bots |
Use Case Ratings
Kimi K3
DeepSeek-V4 Pro
Benchmark Scores
| Benchmark | Kimi K3 | DeepSeek-V4 Pro |
|---|---|---|
| MMLU (Knowledge) | 90.6% | 89.4% |
| MMLU-Pro | 82.4% | 81.0% |
| HumanEval (Coding) | 90.8% | 91.2% |
| GPQA (Graduate Q&A) | 93.5% | 68.5% |
| MATH (Competition) | 91.2% | 89.8% |
| GSM8K (Grade Math) | 96.8% | 96.5% |
| ARC (Reasoning) | 97.2% | 96.8% |
| HellaSwag | 96.0% | 95.2% |
| MT-Bench | 9.35 | 9.30 |
| LMSYS Arena ELO | 1820 | 1980 |
| SWE-Bench | 43.1% | 44.3% |
| AIME (Advanced Math) | 80.4% | 78.2% |
Feature-by-Feature Comparison
| Feature | Kimi K3 | DeepSeek-V4 Pro |
|---|---|---|
| GPQA Diamond Science Benchmark | 93.5% (Record) | 68.5% |
| Inference Output Speed | 88 tok/s | 199 tok/s (2.26x faster) |
| Blended Price / 1M Tokens | $4.33 | $0.48 (9x cheaper) |
| LMSYS Arena ELO Rating | 1,820 | 1,980 (Higher) |
Pricing Comparison
| Plan | Kimi K3 | DeepSeek-V4 Pro |
|---|---|---|
| Free Version | ||
| Subscription | Pay-as-you-go | Pay-as-you-go |
| API Input (1M tokens) | $1.50 | $0.14 |
| API Output (1M tokens) | $6.00 | $0.55 |
Pros & Cons
Kimi K3
✅ Pros
- Exceptional scientific reasoning score (93.5% GPQA)
- Massive 2.8T MoE parameter scale for intricate multi-step proofs
- Outstanding Chinese-English bilingual translation and nuance
- 1.0M context window with high recall across long research papers
❌ Cons
- Slower output generation rate (88 tok/s vs 199 tok/s for DeepSeek)
- 9x higher API pricing ($4.33/M vs $0.48/M blended)
DeepSeek-V4 Pro
✅ Pros
- 2.26x faster generation throughput (199 tok/s vs 88 tok/s)
- 9x cheaper blended API pricing ($0.48/M vs $4.33/M)
- Higher SWE-Bench software engineering score (44.3% vs 43.1%)
- Higher LMSYS Arena community rating (1,980 vs 1,820)
❌ Cons
- Lower score on academic scientific Q&A (68.5% vs 93.5% GPQA)
- Slightly smaller parameter capacity on complex domain synthesis
🏆 Who Wins in Each Category?
Scientific Research & GPQA
Kimi K3 achieves a record 93.5% on GPQA Diamond scientific reasoning.
Coding & Software Engineering
DeepSeek V4 Pro solves 44.3% of SWE-Bench tasks at 199 tok/s.
Token Economics
DeepSeek costs $0.48/M vs $4.33/M for Kimi K3.
Our Pick: DeepSeek-V4 Pro
DeepSeek V4 Pro is the overall winner for software engineering, generation speed (199 tok/s), and pricing ($0.48/M); Kimi K3 wins for scientific research and 2.8T reasoning depth.
Try DeepSeek-V4 ProFrequently Asked Questions
Why is Kimi K3's GPQA score so much higher than DeepSeek's?▼
Moonshot AI trained Kimi K3 specifically on academic literature and scientific proofs using specialized synthetic reasoning chains, making it exceptionally strong at PhD-level research questions.
Can I run both models with Ollama or vLLM?▼
Yes. Both Kimi K3 and DeepSeek V4 Pro publish open weights that can be served locally via vLLM, SGLang, or llama.cpp.
Similar Strength Model Comparisons
Compare other equivalent frontier and mid-tier models with verified benchmark scores.
DeepSeek-R1 vs OpenAI o1
Gemini 1.5 Pro vs Claude 3.5 Sonnet