GLM-4.5V vs Qwen3-Coder 30B-A3B Instruct vs QwQ 32B
GLM-4.5V comes out ahead, 49 to 43 and 41 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)Qwen3-Coder 30B-A3B Instruct
41/100- ECI—
- Price$0.45 / $2.25
- Context262K
- Alibaba (Qwen)
QwQ 32B
43/100- ECI137.6
- Price$0.66 / $1.00
- 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-Coder 30B-A3B Instruct (41). It leads on inputs & features. Qwen3-Coder 30B-A3B Instruct wins on context window. 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 · GLM-4.5V $0.90 · Qwen3-Coder 30B-A3B Instruct $0.90 per 1M tokens (3:1 blend)
- Longest contextQwen3-Coder 30B-A3B InstructQwen3-Coder 30B-A3B Instruct 262,144 · QwQ 32B 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen3-Coder 30B-A3B Instruct: Text · QwQ 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5V | Qwen3-Coder 30B-A3B Instruct | QwQ 32B |
|---|---|---|---|---|
| Price | 50% | 52 | 52 | 56 |
| Inputs & features | 30% | 70 | 25 | 35 |
| Context window | 20% | 12 | 37 | 24 |
| Overall | 100% | 49/100 | 41/100 | 43/100 |
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-Coder 30B-A3B InstructAlibaba (Qwen) | QwQ 32BAlibaba (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 | $0.45 (best) | $0.66 |
| Output | $1.80 | $2.25 | $1.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.90 | $0.745 (best) |
| Long-context rate | Same rate | Over 32K: $0.75 / $3.75 | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 64,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 65,536 tokens (best) | 8,192 tokens |
| 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 | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5v | qwen3-coder-30b-a3b-instruct | — |
| API providers | 11 | 13 (best) | 1 |
| Released | Aug 11, 2025 | Apr 2025 | Mar 5, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | Apr 2024 |
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-Coder 30B-A3B Instruct$9.00
- QwQ 32B$8.60
Which should you choose?
Which is better: GLM-4.5V, Qwen3-Coder 30B-A3B Instruct or QwQ 32B?
GLM-4.5V is the better all-round choice, scoring 49/100 against QwQ 32B (43) and Qwen3-Coder 30B-A3B Instruct (41). It leads on inputs & features. Qwen3-Coder 30B-A3B Instruct wins on context window. 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, Qwen3-Coder 30B-A3B Instruct or QwQ 32B?
QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price); Qwen3-Coder 30B-A3B Instruct costs $0.45 input / $2.25 output per million tokens (official Alibaba 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 $0.90 for GLM-4.5V (1.2× as much) and $0.90 for Qwen3-Coder 30B-A3B Instruct (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, Qwen3-Coder 30B-A3B Instruct has not been scored yet and QwQ 32B has an ECI of 137.6.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Qwen3-Coder 30B-A3B Instruct and QwQ 32B 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-Coder 30B-A3B Instruct has the largest context window at 262,144 tokens, against 131,072 for QwQ 32B and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Qwen3-Coder 30B-A3B Instruct up to 65,536, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Qwen3-Coder 30B-A3B Instruct accepts text; QwQ 32B 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?
GLM-4.5V is the newest, released Aug 11, 2025. Qwen3-Coder 30B-A3B Instruct came out Apr 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3-Coder 30B-A3B Instruct Apr 2025, QwQ 32B Apr 2024.
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.
