DeepSeek V4 Pro vs Kimi K3
At a Glance
| Compare |  DeepSeek V4 ProDeepSeek |  Kimi K3Moonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #19 of 5757.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.6–72.0 | #13 of 5773.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #10 of 27$0.054 per LiveBench case | #19 of 27$0.163 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | #11 of 2751.5 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.9–58.5 | #12 of 2750.1 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.66DeepSeek ↗ · Sep 2, 2026 | $0.239Openrouter ↗ · Oct 19, 2026 |
| Output priceFrom · USD / 1M tokens | $1.5441Openrouter ↗ · Aug 28, 2026 | $11.936Openrouter ↗ · Oct 19, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | DeepSeek-V4-Pro | Kimi-K3 |
|---|---|---|
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 76.7995% of row best · percent · deepseek-v4-pro · 35,014 output tokens / case | 81.02100% of row best · percent · kimi-k3 · 13,647 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 73.0789% of row best · points · DeepSeek V4 Pro (xhigh) · 17,924 output tokens / case | 82.23100% of row best · points · Kimi K3 (max) · 21,438 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | DeepSeek-V4-Pro | Kimi-K3 |
|---|---|---|
| Developer | DeepSeek | Moonshot AI |
| Family | Deepseek V4 Pro | Kimi K3 |
| Model | DeepSeek-V4-Pro | Kimi-K3 |
| Version | DeepSeek-V4-Pro | Kimi-K3 |
| Lifecycle | active | active |
| Released | 2026-04-24 | 2026-07-16 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | 1.6T | 2.78T |
| Active parameters | 49B | 104B |
| License | mit | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning |
DeepSeek V4 Pro Capabilities
Kimi K3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V3.2DeepSeek | vs | DeepSeek-V4-ProDeepSeek | family variantstext |
DeepSeek-V4-ProDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4-ProDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
| vs | DeepSeek-V4-ProDeepSeek | cross-developer peerstext | |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs | DeepSeek-V4-ProDeepSeek | family variantstext |
DeepSeek-V4-ProDeepSeek | vs | DeepSeek-V4-Pro-0813DeepSeek | family variantstext |
DeepSeek-V4-ProDeepSeek | vs |  Gemini 3.1 Flash-LiteGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | Kimi-K2-ThinkingMoonshot AI | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | cross-developer peerstext | |
DeepSeek-V4-ProDeepSeek | vs |  GLM-5.2Z.ai | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Pro vs Kimi K3 FAQs
Is DeepSeek V4 Pro or Kimi K3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro and Kimi K3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Pro or Kimi K3?+
DeepSeek V4 Pro is $0.66 and Kimi K3 is $0.239 per million tokens, so Kimi K3 is cheaper on this metric. DeepSeek V4 Pro is $1.5441 and Kimi K3 is $11.936 per million tokens, so DeepSeek V4 Pro is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Pro or Kimi K3?+
Both models have the same sourced context window: 1,048,576 tokens each.
Which performs better in benchmarks, DeepSeek V4 Pro or Kimi K3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Pro or Kimi K3 be self-hosted?+
Both models have the same sourced self-hosting status: supported. DeepSeek V4 Pro is open weight; Kimi K3 is open weight.
Can DeepSeek V4 Pro and Kimi K3 understand images?+
DeepSeek V4 Pro is not documented with image input; Kimi K3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Pro or Kimi K3?+
Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.
Do DeepSeek V4 Pro and Kimi K3 support reasoning and tool use?+
DeepSeek V4 Pro: reasoning. Kimi K3: reasoning and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Pro or Kimi K3?+
DeepSeek V4 Pro has 6 sourced provider routes; Kimi K3 has 5, so DeepSeek V4 Pro has broader tracked availability.
Which offers better value, DeepSeek V4 Pro or Kimi K3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.