DeepSeek-V4-Pro vs Kimi K2.6
Compare DeepSeek-V4-Pro and Kimi K2.6 on the metrics that decide an agent workload.
| Metric | DeepSeek-V4-Pro | Kimi K2.6 |
|---|---|---|
| Provider | Fireworks AI | Fireworks AI |
| Input / 1M | $1.74 | $0.95 |
| Output / 1M | $3.48 | $4.00 |
| Cache read / 1M | $0.14 (−92%) | $0.16 (−83%) |
| Reuse-adj @55% | $1.52 | $1.39 |
| Context | 1M | 262K |
| Cache capture | 80% | 81% |
| Warm TTFT | 130ms (−63%) | 140ms (−63%) |
| Quality index | 88 | 85 |
Teal marks the better value in each row. Reuse-adjusted assumes 55% prefix reuse and a 25% output share.
Pick DeepSeek-V4-Pro when it scores higher on the composite quality index, it carries the larger 1M context window, it answers faster on warm cache hits.
Pick Kimi K2.6 when it is cheaper once prefix reuse is priced in, it captures more of the available reuse.
DeepSeek-V4-Pro leads on quality while Kimi K2.6 leads on reuse-adjusted cost. Send latency- and budget-sensitive volume to Kimi K2.6 and reserve DeepSeek-V4-Pro for the hard requests. Zumik's aliases route between them automatically under policy.
DeepSeek-V4-Pro vs Kimi K2.6, answered.
Is DeepSeek-V4-Pro or Kimi K2.6 cheaper?
At 55% prefix reuse, DeepSeek-V4-Pro blends to about $1.52 per 1M tokens and Kimi K2.6 to about $1.39. Kimi K2.6 is cheaper on that basis.
Which has the larger context window?
DeepSeek-V4-Pro has the larger window: 1M vs 262K tokens.
Which captures more reuse?
Kimi K2.6 shows higher measured cache capture (81% vs 80%) in the Zumik corpus.
Let an alias pick for you.
Route to whichever model wins under current policy automatically. Zumik resolves the alias to the best fit per request, so you never hard-code a loser.