GLM-4.5V vs Morph V3 Fast vs Qwen3 VL 235B A22B Thinking
GLM-4.5V comes out ahead, 48 to 44 and 32 on our weighted score.
- Our pick
Z.ai (Zhipu)GLM-4.5V
48/100- ECI—
- Price$0.60 / $1.80
- Context64K
Morph
Morph V3 Fast
32/100- ECI—
- Price$0.80 / $1.20
- Context262K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
44/100- ECI—
- Price$0.50 / $3.95
- Context131K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3 VL 235B A22B Thinking (44) and Morph V3 Fast (32). It leads on inputs & features. Morph V3 Fast 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-4.5V and Morph V3 FastGLM-4.5V $0.90 · Morph V3 Fast $0.90 · Qwen3 VL 235B A22B Thinking $1.36 per 1M tokens (3:1 blend)
- Longest contextMorph V3 FastMorph V3 Fast 262,144 · Qwen3 VL 235B A22B Thinking 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Morph V3 Fast: Text · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingGLM-4.5V and Qwen3 VL 235B A22B ThinkingPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Morph V3 Fast | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 50 | 50 | 42 |
| Inputs & features | 30% | 70 | 0 | 60 |
| Context window | 20% | 12 | 37 | 25 |
| Overall | 100% | 48/100 | 32/100 | 44/100 |
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-4.5VZ.ai (Zhipu) | Morph V3 FastMorph | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.80 | $0.50 (best) |
| Output | $1.80 | $1.20 (best) | $3.95 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $0.90 (best) | $0.90 (best) | $1.36 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 5 providers | Median of 5 providers |
| Limits | |||
| Context window | 64,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.5v | — | — |
| API providers | 17 (best) | 5 | 5 |
| Released | Aug 11, 2025 | Jul 7, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Apr 2025 | — | Mar 31, 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-4.5V$9.60
- Morph V3 Fast$10.40
- Qwen3 VL 235B A22B Thinking$12.90
Which should you choose?
Which is better: GLM-4.5V, Morph V3 Fast or Qwen3 VL 235B A22B Thinking?
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3 VL 235B A22B Thinking (44) and Morph V3 Fast (32). It leads on inputs & features. Morph V3 Fast 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-4.5V, Morph V3 Fast or Qwen3 VL 235B A22B Thinking?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Morph V3 Fast costs $0.80 input / $1.20 output per million tokens (median across 5 API providers); Qwen3 VL 235B A22B Thinking costs $0.50 input / $3.95 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $0.90 for Morph V3 Fast (1× as much) and $1.36 for Qwen3 VL 235B A22B Thinking (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Morph V3 Fast has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Morph V3 Fast and Qwen3 VL 235B A22B Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Morph V3 Fast does not support tool calling, which most coding agents need.
Which has the bigger context window?
Morph V3 Fast has the largest context window at 262,144 tokens, against 131,072 for Qwen3 VL 235B A22B Thinking and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Morph V3 Fast up to 131,072, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Morph V3 Fast accepts text; Qwen3 VL 235B A22B Thinking accepts text and images. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V and Qwen3 VL 235B A22B Thinking publishes its weights and can be self-hosted; Morph V3 Fast is proprietary.
Which is newer?
Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. GLM-4.5V came out Aug 11, 2025; Morph V3 Fast came out Jul 7, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3 VL 235B A22B Thinking Mar 31, 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 Nov 2, 2026.
