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Model comparison
DeepSeek's DeepSeek V4 Pro 0423 against Moonshot AI's Kimi K3: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
DeepSeek V4 Pro 0423 lists at $1.11 per million tokens blended against $5.17 for Kimi K3 - 4.7x less.
Kimi K3 scores 44 on the intelligence index against 30 - 13 points apart.
Kimi K3 scores 76 on the coding index against 59 - 17 points apart.
Kimi K3 scores 50 on the agentic index against 26 - 24 points apart.
only Kimi K3 offers image input.
Kimi K3 was released about 3 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | DeepSeek V4 Pro 0423 | Kimi K3 |
|---|---|---|
| Input price / 1M tokens | $0.89 | $0.23 |
| Output price / 1M tokens | $1.78 | $20.0 |
| Blended (3 in : 1 out) | $1.11 | $5.17 |
| Cached input / 1M | $0.07 | $0.22 |
| Context window | 1.0M | 1.0M |
| Max output | 384K | 944K |
| Input | text | text, image, video |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 30 | 44 |
| Coding index | 59 | 76 |
| Agentic index | 26 | 50 |
| Released | Apr 24, 2026 | Jul 16, 2026 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | DeepSeek V4 Pro 0423 | Kimi K3 | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $26.67 | $105 | $77.95 less on DeepSeek V4 Pro 0423 |
| Document summaries100M in, 5M out - long inputs, short answers. | $97.79 | $123 | $25.31 less on DeepSeek V4 Pro 0423 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $88.90 | $414 | $325 less on DeepSeek V4 Pro 0423 |
DeepSeek V4 Pro 0423 takes the short and general requests - at $1.11 blended it is the default - and Kimi K3 the ones whose complexity or quality floor rules the cheaper model out. The split is per request, not per app, and every decision carries the scores for both.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: deepseek-v4-pro, kind: llm, metadata: { model: deepseek/deepseek-v4-pro } }
- { id: kimi-k3, kind: llm, metadata: { model: moonshotai/kimi-k3 } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [deepseek-v4-pro]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [kimi-k3]
weight: 0.8Every DeepSeek and Moonshot AI model, with prices, is on the vendor pages; the rankings show what the router actually picks.