QVQ Max vs GLM-4.5V
GLM-4.5V comes out ahead, 49 to 40 on our weighted score, and it is the cheaper option too.
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against QVQ Max (40). It leads on price and inputs & features. QVQ Max 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-4.5VGLM-4.5V $0.90 · QVQ Max $2.10 per 1M tokens (3:1 blend)
- Longest contextQVQ MaxQVQ Max 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VQVQ Max: Text, Images · GLM-4.5V: Text, Images, Video
- Self-hostingGLM-4.5VPublishes downloadable weights
| Measure | Weight | QVQ Max | GLM-4.5V |
|---|---|---|---|
| Price | 50% | 35 | 52 |
| Inputs & features | 30% | 60 | 70 |
| Context window | 20% | 24 | 12 |
| Overall | 100% | 40/100 | 49/100 |
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 |  QVQ MaxAlibaba (Qwen) |  GLM-4.5VZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $1.20 | $0.60 (best) |
| Output | $4.80 | $1.80 (best) |
| Cached input | — | — |
| Blended (3:1) | $2.10 | $0.90 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Z.AI API |
| Limits | ||
| Context window | 131,072 tokens (best) | 64,000 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | qvq-max | glm-4.5v |
| API providers | 1 | 11 (best) |
| Released | Mar 25, 2025 | Aug 11, 2025 |
| Knowledge cutoff | Apr 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.
- QVQ Max$21.60
- GLM-4.5V$9.60
Which should you choose?
Which is better: QVQ Max or GLM-4.5V?
GLM-4.5V is the better all-round choice, scoring 49/100 against QVQ Max (40). It leads on price and inputs & features. QVQ Max 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, QVQ Max or GLM-4.5V?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). QVQ Max costs $1.20 input / $4.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $2.10 for QVQ Max (2.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. QVQ Max has not been scored yet and GLM-4.5V has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for QVQ Max and GLM-4.5V 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?
QVQ Max has the largest context window at 131,072 tokens, against 64,000 for GLM-4.5V. Maximum output per response: QVQ Max up to 8,192, GLM-4.5V up to 16,384 tokens.
Which can read images, PDFs, audio or video?
QVQ Max accepts text and images; GLM-4.5V accepts text, images and video. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V publishes its weights and can be self-hosted; QVQ Max is proprietary.
Which is newer?
GLM-4.5V is the newest, released Aug 11, 2025. QVQ Max came out Mar 25, 2025. Knowledge cutoff: QVQ Max Apr 2024, GLM-4.5V 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.
