GLM-4.5V vs Qwen3-Next 80B-A3B Instruct vs Qwen3-VL 30B-A3B
Qwen3-VL 30B-A3B comes out ahead, 59 to 49 and 39 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
- Our pickAlibaba (Qwen)
Qwen3-VL 30B-A3B
59/100- ECI—
- Price$0.20 / $0.80
- Context131K
Qwen3-VL 30B-A3B is our pick
Qwen3-VL 30B-A3B is the better all-round choice, scoring 59/100 against GLM-4.5V (49) and Qwen3-Next 80B-A3B Instruct (39). It leads on price. GLM-4.5V wins on inputs & features. 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 priceQwen3-VL 30B-A3BQwen3-VL 30B-A3B $0.35 · Qwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
- Longest contextQwen3-Next 80B-A3B Instruct and Qwen3-VL 30B-A3BQwen3-Next 80B-A3B Instruct 131,072 · Qwen3-VL 30B-A3B 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen3-Next 80B-A3B Instruct: Text · Qwen3-VL 30B-A3B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5V | Qwen3-Next 80B-A3B Instruct | Qwen3-VL 30B-A3B |
|---|---|---|---|---|
| Price | 50% | 52 | 53 | 72 |
| Inputs & features | 30% | 70 | 25 | 60 |
| Context window | 20% | 12 | 24 | 24 |
| Overall | 100% | 49/100 | 39/100 | 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-4.5VZ.ai (Zhipu) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) | Qwen3-VL 30B-A3BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.50 | $0.20 (best) |
| Output | $1.80 | $2.00 | $0.80 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.875 | $0.35 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 16,384 tokens | 32,768 tokens (best) | 32,768 tokens (best) |
| 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5v | qwen3-next-80b-a3b-instruct | qwen3-vl-30b-a3b |
| API providers | 11 | 13 (best) | 1 |
| Released | Aug 11, 2025 | Sep 2025 | Apr 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | 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-4.5V$9.60
- Qwen3-Next 80B-A3B Instruct$9.00
- Qwen3-VL 30B-A3B$3.60
Which should you choose?
Which is better: GLM-4.5V, Qwen3-Next 80B-A3B Instruct or Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B is the better all-round choice, scoring 59/100 against GLM-4.5V (49) and Qwen3-Next 80B-A3B Instruct (39). It leads on price. GLM-4.5V wins on inputs & features. 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, Qwen3-Next 80B-A3B Instruct or Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B is cheaper at $0.20 input / $0.80 output per million tokens (official Alibaba API price). Qwen3-Next 80B-A3B Instruct costs $0.50 input / $2.00 output per million tokens (official Alibaba API price); GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.35 per million tokens for Qwen3-VL 30B-A3B versus $0.875 for Qwen3-Next 80B-A3B Instruct (2.5× as much) and $0.90 for GLM-4.5V (2.6× 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, Qwen3-Next 80B-A3B Instruct has not been scored yet and Qwen3-VL 30B-A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Qwen3-Next 80B-A3B Instruct and Qwen3-VL 30B-A3B 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-Next 80B-A3B Instruct and Qwen3-VL 30B-A3B have the largest context windows (131,072 and 131,072 tokens), against 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Qwen3-Next 80B-A3B Instruct up to 32,768, Qwen3-VL 30B-A3B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Qwen3-Next 80B-A3B Instruct accepts text; Qwen3-VL 30B-A3B accepts text and images. GLM-4.5V 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?
Qwen3-Next 80B-A3B Instruct is the newest, released Sep 2025. GLM-4.5V came out Aug 11, 2025; Qwen3-VL 30B-A3B came out Apr 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3-Next 80B-A3B Instruct Apr 2025, Qwen3-VL 30B-A3B 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.
