GLM-4.6V vs GLM-4.7 vs Kimi K2 Thinking
GLM-4.6V comes out ahead, 59 to 42 and 42 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Z.ai (Zhipu)
GLM-4.7
42/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
GLM-4.6V is our pick
GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42) and GLM-4.7 (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 · GLM-4.7 $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · GLM-4.7 204,800 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6VGLM-4.6V: Text, Images, Video · GLM-4.7: Text · Kimi K2 Thinking: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.6V | GLM-4.7 | Kimi K2 Thinking |
|---|---|---|---|---|
| Price | 50% | 66 | 50 | 48 |
| Inputs & features | 30% | 70 | 35 | 35 |
| Context window | 20% | 24 | 32 | 37 |
| Overall | 100% | 59/100 | 42/100 | 42/100 |
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 | GLM-4.6VZ.ai (Zhipu) | GLM-4.7Z.ai (Zhipu) | Kimi K2 ThinkingMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.5 | 146.0 (best) |
| ECI rank | — | #84 of 148 | #72 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 83.3% | 84.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% (best) | 83.1% |
| SimpleQA VerifiedShort factual questions | — | 32.2% | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.60 | $0.60 |
| Output | $0.90 (best) | $2.20 | $2.50 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $0.45 (best) | $1.00 | $1.07 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Z.AI API | Median of 10 providers |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.6v | glm-4.7 | — |
| API providers | 10 | 20 (best) | 10 |
| Released | Dec 8, 2025 | Dec 22, 2025 | Nov 6, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.6V$4.80
- GLM-4.7$10.40
- Kimi K2 Thinking$11.00
Which should you choose?
Which is better: GLM-4.6V, GLM-4.7 or Kimi K2 Thinking?
GLM-4.6V is the better all-round choice, scoring 59/100 against Kimi K2 Thinking (42) and GLM-4.7 (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, GLM-4.6V, GLM-4.7 or Kimi K2 Thinking?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI API price). GLM-4.7 costs $0.60 input / $2.20 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 $1.00 for GLM-4.7 (2.2× as much) and $1.07 for Kimi K2 Thinking (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V has not been scored yet, GLM-4.7 has an ECI of 143.5 and Kimi K2 Thinking has an ECI of 146.0.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, GLM-4.7 and Kimi K2 Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three 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 204,800 for GLM-4.7 and 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, GLM-4.7 up to 131,072, Kimi K2 Thinking up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; GLM-4.7 accepts text; Kimi K2 Thinking accepts text. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. GLM-4.6V came out Dec 8, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, GLM-4.7 Apr 2025, Kimi K2 Thinking Aug 2024.
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.
