Kimi K3 vs DeepSeek-R1
Moonshot AI vs DeepSeek · Prices checked 12 August 2026
There is a 6.3× price gap here. DeepSeek-R1 blends to $1.04 per million tokens; Kimi K3 to $6.60. A single 100K-input, 10K-output job costs $0.077 on DeepSeek-R1 against $0.450 on Kimi K3.
Kimi K3 reads more in one request: 1,049,000 tokens of context against 64,000.
Pricing and capacity
| Metric | Kimi K3 | DeepSeek-R1 | Difference |
|---|---|---|---|
| Input / 1M tokens | $3.00 | $0.550 | 5.5× |
| Output / 1M tokens | $15.00 | $2.19 | 6.8× |
| Blended · 70/30calculated | $6.60 | $1.04 | 6.3× |
| Blended · 50/50calculated | $9.00 | $1.37 | 6.6× |
| Blended · 80/20calculated | $5.40 | $0.878 | 6.2× |
| Blended · 20/80calculated | $12.60 | $1.86 | 6.8× |
| Job cost · 100K + 10Kcalculated | $0.450 | $0.077 | 5.9× |
| Job cost · 1M + 100Kcalculated | $4.50 | $0.769 | 5.9× |
| Context window | 1,049,000 | 64,000 | 16.4× |
Input and output rates are as published by each provider. Blended and job-cost rows are calculated comparison metrics, not published prices — how they are derived.
Benchmarks
These two models do not currently report scores on any of the same benchmark suites, so there is no like-for-like capability comparison to show. Rather than pad the page with one-sided numbers, the benchmark leaderboards list every score we do hold.
What each model is for
Kimi K3
Moonshot AI · Frontier
Moonshot AI's 2.7-trillion-parameter flagship, released in July 2026 as the largest open-weight model published to date and the first Chinese model widely treated as competitive with the top US frontier systems. It runs an always-on reasoning mode Moonshot calls "thinking mode", handles a 1M-token context window, and understands images natively. Two variants shipped: K3 Max for chat and agent work, K3 Swarm Max for large-scale parallel processing.
- Largest open-weight model released to date (2.7T parameters)
- Always-on "thinking mode" reasoning
- 1M-token context
- Native visual understanding
DeepSeek-R1
DeepSeek · Reasoning
Open-weights reasoning model with chain-of-thought trained via RL. Comparable to o1 on math benchmarks at a fraction of the price.
- Open-weights reasoning
- Visible chain-of-thought
- Math & code
- Cheap
Frequently asked questions
Which is cheaper, Kimi K3 or DeepSeek-R1?
There is a 6.3× price gap here. DeepSeek-R1 blends to $1.04 per million tokens; Kimi K3 to $6.60. A single 100K-input, 10K-output job costs $0.077 on DeepSeek-R1 against $0.450 on Kimi K3. Blended cost weights input at 70% and output at 30%; price your own token split for an exact answer, since the cheaper model on a blended basis is not always cheaper for your workload.
Which has the larger context window, Kimi K3 or DeepSeek-R1?
Kimi K3 reads more in one request: 1,049,000 tokens of context against 64,000. The context window caps everything in a single request: prompt, retrieved documents, conversation history and the model’s own output all share it.
Can I compare Kimi K3 and DeepSeek-R1 on my own numbers?
Yes. Open both in the side-by-side comparison tool to see every field at once, or use the token calculator to price your actual prompt and monthly request volume against each model. Both are free and need no sign-up.
Price it for your own workload
Related matchups
Terms used on this page
Prices and scores are recomputed on every deploy from the catalogue. Prices checked 12 August 2026.