DataLLM LabKimi vs DeepSeek in 2026: Cache Discount vs Cheapest Baseline
Kimi and DeepSeek are both cheap, open-weights, and capable — but they win in different ways. DeepSeek V3.2 is the cheapest capable baseline on headline price; Kimi's K2 family brings a deep cache-hit discount, a large 256K context, and a coding-tuned K2.7 Code model. For workloads that reuse a big fixed context every call, Kimi's cache discount can flip the cost math. This guide compares them on price, cache mechanics, context, and use case.
The short answer
DeepSeek for the cheapest baseline and reasoning; Kimi for a large reused context, long windows, and coding. On headline price DeepSeek wins, but Kimi's deep cache-hit discount can flip the math for workloads that resend a big fixed context every call — exactly what agents and RAG do.
Side by side
| Kimi (Moonshot) | DeepSeek | |
|---|---|---|
| Cheapest tier | K2.5 $0.38/$2.02 | V3.2 $0.23/$0.34 |
| Cache-hit discount | Yes (~5x on cached input) | Context caching (smaller) |
| Context window | ~256K-262K | Capable |
| Coding model | K2.7 Code (agentic) | V4 (all-round) |
| License | Modified MIT | MIT |
| Best at | Reused-context agents, long docs | Cheapest baseline, reasoning |
Headline price
On output price (cache-miss), DeepSeek V3.2 is the cheapest of the group:
What they cost to run
At cache-miss rates (the conservative ceiling for Kimi), DeepSeek wins every row:
| Monthly workload | DeepSeek V3.2 | Kimi K2.6 | Kimi K2.5 | Kimi K2.7 Code |
|---|---|---|---|---|
| Support chatbot | $13.3 | $68.1 | $39.4 | $86.0 |
| RAG / knowledge base | $52.8 | $204 | $116 | $270 |
| Coding agent | $26.9 | $140 | $80.9 | $176 |
| Batch extraction | $37.2 | $129 | $73.2 | $175 |
| Content generation | $18.2 | $150 | $88.4 | $179 |
The cache-discount flip
Those figures ignore Kimi's signature lever. For a workload that resends a large fixed context every call — an agent with a 30K-token system prompt + tools + retrieved docs across 100K calls/month — Kimi K2.7 Code serves the repeated prefix at its cache-hit rate (~$0.19/M) instead of fresh input (~$0.95/M), roughly 5x cheaper on that portion. That can cut the input bill by ~75% and pull Kimi's effective cost on input-heavy, reused-context workloads down toward — and sometimes below — DeepSeek's. So the honest rule: DeepSeek wins on headline price; Kimi can win when a big context repeats. Structure your prompt to keep the stable part at the front to maximize the discount.
Which to pick
Cheapest baseline DeepSeek V3.2
- Lowest headline cost for general traffic.
Reused context Kimi
- Big fixed context every call → the cache discount wins.
Long documents Kimi
- 256K context for long-doc and agentic workflows.
Best move Route both
- DeepSeek default, Kimi for reused-context work. One key.
Route Kimi and DeepSeek from one key
DeepSeek V3.2, Kimi K2.6, K2.5 and 300+ more — one OpenAI-compatible endpoint, live price comparison, failover.
FAQ
Is Kimi or DeepSeek cheaper?
Headline price → DeepSeek V3.2. But Kimi's cache-hit discount (~5x on cached input) can make Kimi cheaper for big-reused-context workloads. Compare on traffic shape.
What is Kimi's cache-hit discount?
Repeated prefixes bill at ~$0.19/M cached vs $0.95 fresh for K2.7 Code (~5x cheaper). For agents/RAG reusing a big context, ~75% off the input bill.
Kimi or DeepSeek for coding?
Both strong, cheap — Kimi K2.7 Code (agentic, cache discount) vs DeepSeek V4 (all-round, reasoning). Long-context reused-context coding → Kimi; cheapest → DeepSeek.
Which has the bigger context window?
Kimi — ~256K-262K, ideal for long-context agents and documents, and it pairs with the cache discount when context is reused.
Are both open source?
Yes — DeepSeek MIT, Kimi K2 Modified MIT (attribution only above huge scale). Both effectively permissive and self-hostable.
Should I use Kimi or DeepSeek?
DeepSeek for the cheapest baseline; Kimi for big reused context, long windows, and its coding model. Route both via one gateway.
Which is more capable?
Close and task-dependent — DeepSeek for reasoning, Kimi K2.7 Code for agentic coding. Both within a few points; test on your workload.
When does Kimi beat DeepSeek on cost?
When a large fixed context repeats every call — the cache-hit discount cuts the input bill enough to rival or beat DeepSeek's headline price.