AI model comparison
Kimi K2 0711 vs R1
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Moonshot AI
Kimi K2 0711
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass.
- Input / 1M
- $0.57
- Output / 1M
- $2.30
- Context
- 131K
DeepSeek
R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens.
- Input / 1M
- $0.70
- Output / 1M
- $2.50
- Context
- 64K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Kimi K2 0711
1.2x cheaper for a typical chat
Higher intelligence score
Kimi K2 0711
12.7 vs 11.4 on Artificial Analysis
Larger context window
Kimi K2 0711
131K vs 64K tokens
Newer release
Kimi K2 0711
Released July 11, 2025
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
R111.4Coding index
R124.6Agentic index
R11.1Pricing
Kimi K2 0711 vs R1 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | R1 | |
|---|---|---|
| Input tokensPer 1M tokens | $0.57 | $0.70 |
| Output tokensPer 1M tokens | $2.30 | $2.50 |
| Cached input (read)Per 1M tokens | Not available | Not available |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
R1$2.65Coding task
30K in, 4K out
R1$31Long document summary
150K in, 2K out
R1$110Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | R1 | |
|---|---|---|
| Context window | 131K tokens | 64K tokens |
| Max output | 98K tokens | 16K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | July 11, 2025 | January 20, 2025 |
Features
Supported features
API features available for each model.
| Metric | R1 | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Not supported |
| JSON mode | Not supported | Supported |
| Reasoning | Not supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
Our take
Kimi K2 0711 is 1.2x cheaper for a typical chat. Kimi K2 0711 scores higher on the Artificial Analysis Intelligence Index (12.7 vs 11.4). Kimi K2 0711 has the larger context window (131K vs 64K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Kimi K2 0711 or R1 cheaper?
Kimi K2 0711 is cheaper for a typical chat (2K input tokens and 500 output tokens). Kimi K2 0711 costs $0.57 per 1M input tokens and $2.30 per 1M output tokens, while R1 costs $0.70 per 1M input tokens and $2.50 per 1M output tokens.
Which has a bigger context window, Kimi K2 0711 or R1?
Kimi K2 0711 supports up to 131K tokens of context, compared with 64K for R1.
Is Kimi K2 0711 smarter than R1?
On the Artificial Analysis Intelligence Index, Kimi K2 0711 scores 12.7 and R1 scores 11.4, putting Kimi K2 0711 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Kimi K2 0711 and R1 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Kimi K2 0711 and R1 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Kimi K2 0711 and R1 at the same time. Compare the answers side by side and keep the best one.