Kimi K2 vs Kimi K2 0905
Head-to-head specifications
| Metric | Kimi K2 | Kimi K2 0905 | Difference |
|---|---|---|---|
| Intelligence Index | 24.0 | 28.0 | -14.3% |
| Context window | 200K tokens | 300K tokens | — |
| Blended price ($/1M tokens) | $0.51 | $0.62 | -17.7% |
| Output speed (tokens/s) | 35 | 36 | -2.8% |
| Access | Open weights | Open weights | — |
- Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0).
- Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.2× cheaper.
- Kimi K2 0905 offers the larger context window (300K tokens), useful for long documents and codebases.
Verdict: Kimi K2 or Kimi K2 0905?
Kimi K2 advantages
- Affordability (+18%)
Kimi K2 0905 advantages
- General intelligence (+14%)
- Context window (+33%)
Which should you choose?
- Choose the Kimi K2 if you want the lowest cost per token at scale.
- Choose the Kimi K2 0905 if you need the strongest overall reasoning and accuracy.
Value for money
Kimi K2 offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Kimi K2 vs Kimi K2 0905: which should you choose?
Kimi K2 — Moonshot AI text model with an Intelligence Index of 24, a 200K-token context window and a blended price of $0.51/1M tokens (open weights).
Kimi K2 0905 — Moonshot AI text model with an Intelligence Index of 28, a 300K-token context window and a blended price of $0.62/1M tokens (open weights).
Kimi K2 vs Kimi K2 0905: Kimi K2 0905 scores higher on the Intelligence Index. Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0). Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Kimi K2 0905 scores 28.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Kimi K2 0905 accepts up to 300K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Kimi K2 0905 generates faster (36 vs 35 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Kimi K2 is the cheaper model to run ($0.51 vs $0.62 per 1M tokens). Kimi K2 is open weights and Kimi K2 0905 is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Kimi K2 better than the Kimi K2 0905?
Kimi K2 0905 takes the overall edge, though Kimi K2 wins in specific areas worth weighing. Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0).
What is the main difference between the Kimi K2 and the Kimi K2 0905?
Kimi K2 0905 leads overall capability (Intelligence Index 28.0 vs 24.0). Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.2× cheaper.
Which is better value?
Kimi K2 offers more intelligence per dollar (1.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Kimi K2 if you want the lowest cost per token at scale. Choose the Kimi K2 0905 if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.