GLM-4.5V vs Qwen3 VL 235B A22B Thinking vs Qwen3-Next 80B-A3B Instruct
Too close to call on our weighted score (GLM-4.5V 49, Qwen3 VL 235B A22B Thinking 48, Qwen3-Next 80B-A3B Instruct 39). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
- Alibaba (Qwen)
Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Qwen3 VL 235B A22B Thinking 48/100, Qwen3-Next 80B-A3B Instruct 39/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price. 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-Next 80B-A3B InstructQwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 · Qwen3 VL 235B A22B Thinking $1.30 per 1M tokens (3:1 blend)
- Longest contextQwen3 VL 235B A22B Thinking and Qwen3-Next 80B-A3B InstructQwen3 VL 235B A22B Thinking 131,072 · Qwen3-Next 80B-A3B Instruct 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen3 VL 235B A22B Thinking: Text, Images · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5V | Qwen3 VL 235B A22B Thinking | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 44 | 53 |
| Inputs & features | 30% | 70 | 70 | 25 |
| Context window | 20% | 12 | 24 | 24 |
| Overall | 100% | 49/100 | 48/100 | 39/100 |
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 VL 235B A22B ThinkingAlibaba (Qwen) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.40 (best) | $0.50 |
| Output | $1.80 (best) | $4.00 | $2.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $1.30 | $0.875 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 9 providers | 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 | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5v | — | qwen3-next-80b-a3b-instruct |
| API providers | 11 | 9 | 13 (best) |
| Released | Aug 11, 2025 | Sep 23, 2025 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | Mar 31, 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 VL 235B A22B Thinking$12.00
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, Qwen3 VL 235B A22B Thinking or Qwen3-Next 80B-A3B Instruct?
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Qwen3 VL 235B A22B Thinking 48/100, Qwen3-Next 80B-A3B Instruct 39/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price. 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 VL 235B A22B Thinking or Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper at $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); Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.875 per million tokens for Qwen3-Next 80B-A3B Instruct versus $0.90 for GLM-4.5V (1× as much) and $1.30 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, Qwen3 VL 235B A22B Thinking has not been scored yet and Qwen3-Next 80B-A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Qwen3 VL 235B A22B Thinking and Qwen3-Next 80B-A3B Instruct 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 VL 235B A22B Thinking and Qwen3-Next 80B-A3B Instruct 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 VL 235B A22B Thinking up to 32,768, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Qwen3 VL 235B A22B Thinking accepts text and images; Qwen3-Next 80B-A3B Instruct accepts text. 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 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Qwen3-Next 80B-A3B Instruct came out Sep 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3 VL 235B A22B Thinking Mar 31, 2025, Qwen3-Next 80B-A3B Instruct 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.
