GLM-4.5V vs QwQ 32B vs Qwen3-Next 80B-A3B Instruct
GLM-4.5V comes out ahead, 49 to 43 and 39 on our weighted score, though QwQ 32B is 17% cheaper per token.
- Our pick
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)QwQ 32B
43/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
- Alibaba (Qwen)
Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against QwQ 32B (43) and Qwen3-Next 80B-A3B Instruct (39). It leads on inputs & features. QwQ 32B wins 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 priceQwQ 32BQwQ 32B $0.745 · Qwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
- Longest contextQwQ 32B and Qwen3-Next 80B-A3B InstructQwQ 32B 131,072 · Qwen3-Next 80B-A3B Instruct 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · QwQ 32B: Text · 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 | QwQ 32B | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 56 | 53 |
| Inputs & features | 30% | 70 | 35 | 25 |
| Context window | 20% | 12 | 24 | 24 |
| Overall | 100% | 49/100 | 43/100 | 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) | QwQ 32BAlibaba (Qwen) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 137.6 | — |
| ECI rank | — | #109 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 65.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 59.2% | — |
| Price per million tokens | |||
| Input | $0.60 | $0.66 | $0.50 (best) |
| Output | $1.80 | $1.00 (best) | $2.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.745 (best) | $0.875 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 16,384 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | 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 | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5v | — | qwen3-next-80b-a3b-instruct |
| API providers | 11 | 1 | 13 (best) |
| Released | Aug 11, 2025 | Mar 5, 2025 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | 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.
- GLM-4.5V$9.60
- QwQ 32B$8.60
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, QwQ 32B or Qwen3-Next 80B-A3B Instruct?
GLM-4.5V is the better all-round choice, scoring 49/100 against QwQ 32B (43) and Qwen3-Next 80B-A3B Instruct (39). It leads on inputs & features. QwQ 32B wins 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, QwQ 32B or Qwen3-Next 80B-A3B Instruct?
QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). 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 $0.745 per million tokens for QwQ 32B versus $0.875 for Qwen3-Next 80B-A3B Instruct (1.2× as much) and $0.90 for GLM-4.5V (1.2× 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, QwQ 32B has an ECI of 137.6 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, QwQ 32B 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?
QwQ 32B 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, QwQ 32B up to 8,192, 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; QwQ 32B accepts text; 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-Next 80B-A3B Instruct is the newest, released Sep 2025. GLM-4.5V came out Aug 11, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, QwQ 32B Apr 2024, 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.
