GLM-4.7 vs Kimi K2 Thinking
Head-to-head specifications
| Metric | GLM-4.7 | Kimi K2 Thinking | Difference |
|---|---|---|---|
| Intelligence Index | 30.0 | 33.0 | -9.1% |
| Context window | 256K tokens | 300K tokens | — |
| Blended price ($/1M tokens) | $0.60 | $0.62 | -3.2% |
| Output speed (tokens/s) | 87 | 131 | -33.6% |
| Access | Open weights | Open weights | — |
- Kimi K2 Thinking leads overall capability (Intelligence Index 33.0 vs 30.0).
- Both cost about the same to run (~$0.60/1M blended tokens), so capability and speed should decide.
- Kimi K2 Thinking offers the larger context window (300K tokens), useful for long documents and codebases.
Verdict: GLM-4.7 or Kimi K2 Thinking?
GLM-4.7 advantages
- No decisive advantage on the tracked metrics.
Kimi K2 Thinking advantages
- General intelligence (+9%)
- Context window (+15%)
- Output speed (+34%)
Which should you choose?
- Choose the Kimi K2 Thinking if you need the strongest overall reasoning and accuracy.
Value for money
Kimi K2 Thinking offers more intelligence per dollar (1.1× 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.
GLM-4.7 vs Kimi K2 Thinking: which should you choose?
GLM-4.7 — Z.ai (Zhipu) text model with an Intelligence Index of 30, a 256K-token context window and a blended price of $0.6/1M tokens (open weights).
Kimi K2 Thinking — Moonshot AI text model with an Intelligence Index of 33, a 300K-token context window and a blended price of $0.62/1M tokens (open weights).
GLM-4.7 vs Kimi K2 Thinking: Kimi K2 Thinking scores higher on the Intelligence Index. Kimi K2 Thinking leads overall capability (Intelligence Index 33.0 vs 30.0). Both cost about the same to run (~$0.60/1M blended tokens), so capability and speed should decide.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Kimi K2 Thinking scores 33.0 versus 30.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Kimi K2 Thinking 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 Thinking generates faster (131 vs 87 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-4.7 is the cheaper model to run ($0.60 vs $0.62 per 1M tokens). GLM-4.7 is open weights and Kimi K2 Thinking 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 GLM-4.7 better than the Kimi K2 Thinking?
Kimi K2 Thinking is the clearly stronger overall choice, winning most of the dimensions that matter. Kimi K2 Thinking leads overall capability (Intelligence Index 33.0 vs 30.0).
What is the main difference between the GLM-4.7 and the Kimi K2 Thinking?
Kimi K2 Thinking leads overall capability (Intelligence Index 33.0 vs 30.0). Both cost about the same to run (~$0.60/1M blended tokens), so capability and speed should decide.
Which is better value?
Kimi K2 Thinking offers more intelligence per dollar (1.1× 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 Thinking 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.