DeepSeek V4-Pro vs Kimi K3: Which is Better in 2026?
DeepSeek V4-Pro (DeepSeek, 1600B parameters) and Kimi K3 (Moonshot AI, 2800B parameters) are both frontier-class models competing for the same developer and enterprise audience in 2026. This page compiles benchmarks, task grades, and practical guidance to help you decide which model fits your workflow.
Last updated: October 2, 2026
Quick Verdict
Across the benchmarks where both models have published scores, Kimi K3 leads on 5 of 5 shared evaluation tasks. DeepSeek V4-Pro remains competitive, particularly in areas aligned with its training focus. For general-purpose quality, Kimi K3 currently holds an edge — but the right choice depends heavily on your specific use case, budget, and whether you need API access or self-hosted deployment.
Side-by-Side Comparison
The table below covers every benchmark for which at least one model has a published score. Higher scores are better on all metrics except where noted. Bold values indicate the higher score in each row.
| Benchmark | DeepSeek V4-Pro | Kimi K3 | Winner |
|---|---|---|---|
| MMLU-Pro | 87.5 | — | DeepSeek V4-Pro |
| GPQA Diamond | 90.1 | 93.5 | Kimi K3 |
| SWE-bench Verified | 80.6 | — | DeepSeek V4-Pro |
| HumanEval | 76.8 | — | DeepSeek V4-Pro |
| LiveCodeBench | 93.5 | — | DeepSeek V4-Pro |
| Arena Elo (Text) | 1451 | 1476 | Kimi K3 |
| Arena Elo (WebDev) | 1446 | 1658 | Kimi K3 |
| AIME 2024–25 (Epoch AI) | 96.7 | 97.2 | Kimi K3 |
| SimpleQA Verified | 47 | 50.6 | Kimi K3 |
Task Performance
Per-task grades are sourced from the id8 LLM leaderboard evaluations. Each grade reflects observed output quality across real-world prompts in that category. A dash (—) means grades are not yet published for that model.
| Task | DeepSeek V4-Pro | Kimi K3 |
|---|---|---|
| Coding | S | S |
| Math | S | S |
| Content Writing | A | S |
| Reasoning | S | S |
| Studying | S | S |
| Chat / Conversation | A | S |
| Summarization | A | S |
| Agents / Tool Use | S | S |
| Vision / Multimodal | B | B |
| Data Analysis | A | S |
| Hindi / Multilingual | B | B |
| Interview Prep | S | S |
| overall | S | S |
| tool_calling | — | S |
| long_context | — | S |
Grade scale: S = Exceptional A = Strong B = Good C = Fair D = Weak
Key Differences
- DeepSeek V4-Pro is an open-weight model you can download and self-host; Kimi K3 is available exclusively through Moonshot AI's API, which means no model weights are publicly released.
- In terms of raw scale, Kimi K3 (2800B parameters) is significantly larger than DeepSeek V4-Pro (1600B parameters). Larger parameter counts often correlate with stronger reasoning, though efficiency improvements mean smaller models can punch above their weight.
- For graduate-level science and reasoning (GPQA Diamond), Kimi K3 leads with 93.5%, indicating stronger performance on expert-level knowledge tasks.
- DeepSeek V4-Pro earns an S-grade (exceptional) in: overall, Coding, Math — making it the top choice for those workflows.
- Kimi K3 earns an S-grade in: overall, Coding, Math.
Which Should You Choose?
Choose DeepSeek V4-Pro if your primary use cases involve overall, Coding, Math. DeepSeek V4-Pro scores highly on these tasks and is especially well-suited for teams that need consistent, high-quality outputs at scale through DeepSeek's API.
Choose Kimi K3 if your work centres on overall, Coding, Math, and self-hosting a 2800B-parameter model is feasible for your infrastructure. It offers a strong value proposition for those use cases.
Still unsure? The LLM Leaderboard lets you sort and filter models by benchmark — useful for narrowing down the right model for a specific workload.
Explore Further
Use these tools to dig deeper into either model's hardware requirements and leaderboard ranking.
Related Comparisons
Related Tools