GLM-4.6V vs Kimi K2 Thinking vs Qwen3.5 Plus
Too close to call on our weighted score (Qwen3.5 Plus 59, GLM-4.6V 59, Kimi K2 Thinking 42). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.6V
59/100- ECI—
- Price$0.30 / $0.90
- Context128K
Moonshot AIKimi K2 Thinking
42/100- ECI146.0
- Price$0.60 / $2.50
- Context262K
Alibaba (Qwen)Qwen3.5 Plus
59/100- ECI146.8
- Price$0.40 / $2.40
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (Qwen3.5 Plus 59/100, GLM-4.6V 59/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: GLM-4.6V on price and Qwen3.5 Plus for long inputs. 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 · Qwen3.5 Plus $0.90 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 PlusQwen3.5 Plus 1,000,000 · Kimi K2 Thinking 262,144 · GLM-4.6V 128,000 tokens
- Widest inputsGLM-4.6V and Qwen3.5 PlusGLM-4.6V: Text, Images, Video · Kimi K2 Thinking: Text · Qwen3.5 Plus: Text, Images, Video
- Self-hostingGLM-4.6V and Kimi K2 ThinkingPublishes downloadable weights
| Measure | Weight | GLM-4.6V | Kimi K2 Thinking | Qwen3.5 Plus |
|---|---|---|---|---|
| Price | 50% | 66 | 48 | 52 |
| Inputs & features | 30% | 70 | 35 | 70 |
| Context window | 20% | 24 | 37 | 60 |
| Overall | 100% | 59/100 | 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 | GLM-4.6VZ.ai (Zhipu) | Kimi K2 ThinkingMoonshot AI | Qwen3.5 PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 146.0 | 146.8 (best) |
| ECI rank | — | #72 of 148 | #65 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 84.2% | 84.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.1% | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 25.4% |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.60 | $0.40 |
| Output | $0.90 (best) | $2.50 | $2.40 |
| Cached input | — | — | — |
| Blended (3:1) | $0.45 (best) | $1.07 | $0.90 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 10 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 32,768 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-4.6v | — | qwen3.5-plus |
| API providers | 10 | 10 | 10 |
| Released | Dec 8, 2025 | Nov 6, 2025 | Feb 16, 2026 |
| Knowledge cutoff | Apr 2025 | 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.
- GLM-4.6V$4.80
- Kimi K2 Thinking$11.00
- Qwen3.5 Plus$8.80
Which should you choose?
Which is better: GLM-4.6V, Kimi K2 Thinking or Qwen3.5 Plus?
It is close. Our weighted score puts them within a point (Qwen3.5 Plus 59/100, GLM-4.6V 59/100, Kimi K2 Thinking 42/100), so choose by what matters most for your work: GLM-4.6V on price and Qwen3.5 Plus for long inputs. 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, Kimi K2 Thinking or Qwen3.5 Plus?
GLM-4.6V is cheaper at $0.30 input / $0.90 output per million tokens (official Z.AI API price). Qwen3.5 Plus costs $0.40 input / $2.40 output per million tokens (official Alibaba 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 $0.90 for Qwen3.5 Plus (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, Kimi K2 Thinking has an ECI of 146.0 and Qwen3.5 Plus has an ECI of 146.8.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V, Kimi K2 Thinking and Qwen3.5 Plus 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?
Qwen3.5 Plus has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2 Thinking and 128,000 for GLM-4.6V. Maximum output per response: GLM-4.6V up to 32,768, Kimi K2 Thinking up to 262,144, Qwen3.5 Plus up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V accepts text, images and video; Kimi K2 Thinking accepts text; Qwen3.5 Plus accepts text, images and video. GLM-4.6V handles the widest range of inputs.
Are any of these open source?
GLM-4.6V and Kimi K2 Thinking publishes its weights and can be self-hosted; Qwen3.5 Plus is proprietary.
Which is newer?
Qwen3.5 Plus is the newest, released Feb 16, 2026. GLM-4.6V came out Dec 8, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.6V Apr 2025, Kimi K2 Thinking Aug 2024, Qwen3.5 Plus 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.
