Kimi K2 Thinking vs GLM-4.6V
GLM-4.6V comes out ahead, 59 to 42 on our weighted score, and it is the cheaper option too.
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
Add a model
Make it a three-way comparison.
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGLM-4.6VGLM-4.6V $0.45 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VKimi K2 Thinking: Text · GLM-4.6V: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Kimi K2 Thinking | GLM-4.6V |
|---|---|---|---|
| Price | 50% | 48 | 66 |
| Inputs & features | 30% | 35 | 70 |
| Context window | 20% | 37 | 24 |
| Overall | 100% | 42/100 | 59/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2 ThinkingMoonshot AI | GLM-4.6VZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 146.0 | — |
| ECI rank | #72 of 148 | — |
| GPQA DiamondGraduate-level science questions | 84.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.1% | — |
| Price per million tokens | ||
| Input | $0.60 | $0.30 (best) |
| Output | $2.50 | $0.90 (best) |
| Cached input | — | — |
| Blended (3:1) | $1.07 | $0.45 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Z.AI API |
| Limits | ||
| Context window | 262,144 tokens (best) | 128,000 tokens |
| Max output | 262,144 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | glm-4.6v |
| API providers | 10 | 10 |
| Released | Nov 6, 2025 | Dec 8, 2025 |
| Knowledge cutoff | Aug 2024 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K2 Thinking$11.00
- GLM-4.6V$4.80
Which should you choose?
Which is better: Kimi K2 Thinking or GLM-4.6V?
GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42). It leads on price and inputs & features. Kimi K2 Thinking wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Kimi K2 Thinking or GLM-4.6V?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI API price). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for GLM-4.6V versus $1.07 for Kimi K2 Thinking (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Kimi K2 Thinking has an ECI of 146.0 and GLM-4.6V has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Thinking and GLM-4.6V yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2 Thinking has the largest context window at 262,144 tokens, against 128,000 for GLM-4.6V. Maximum output per response: Kimi K2 Thinking up to 262,144, GLM-4.6V up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Thinking accepts text; GLM-4.6V accepts text, images and video. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-4.6V is the newest, released Dec 8, 2025. Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, GLM-4.6V Apr 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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