Aya Expanse 32B vs GLM-4.5V vs Qwen3-Coder 30B-A3B Instruct
GLM-4.5V comes out ahead, 49 to 41 and 33 on our weighted score, though Aya Expanse 32B is 17% cheaper per token.
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
- 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
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against Qwen3-Coder 30B-A3B Instruct (41) and Aya Expanse 32B (33). It leads on inputs & features. Aya Expanse 32B wins on price. Qwen3-Coder 30B-A3B Instruct 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 priceAya Expanse 32BAya Expanse 32B $0.75 · 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 · Aya Expanse 32B 128,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VAya Expanse 32B: Text · GLM-4.5V: Text, Images, Video · Qwen3-Coder 30B-A3B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Aya Expanse 32B | GLM-4.5V | Qwen3-Coder 30B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 56 | 52 | 52 |
| Inputs & features | 30% | 0 | 70 | 25 |
| Context window | 20% | 24 | 12 | 37 |
| Overall | 100% | 33/100 | 49/100 | 41/100 |
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 | Aya Expanse 32BCohere | GLM-4.5VZ.ai (Zhipu) | Qwen3-Coder 30B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.50 | $0.60 | $0.45 (best) |
| Output | $1.50 (best) | $1.80 | $2.25 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 (best) | $0.90 | $0.90 |
| Long-context rate | Same rate | Same rate | Over 32K: $0.75 / $3.75 |
| Price source | Median of 1 providers | Official Z.AI API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 64,000 tokens | 262,144 tokens (best) |
| Max output | 4,000 tokens | 16,384 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Open | Open |
| API model ID | c4ai-aya-expanse-32b | glm-4.5v | qwen3-coder-30b-a3b-instruct |
| API providers | 2 | 11 | 13 (best) |
| Released | Oct 24, 2024 | Aug 11, 2025 | Apr 2025 |
| Knowledge cutoff | — | 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.
- Aya Expanse 32B$8.00
- GLM-4.5V$9.60
- Qwen3-Coder 30B-A3B Instruct$9.00
Which should you choose?
Which is better: Aya Expanse 32B, GLM-4.5V or Qwen3-Coder 30B-A3B Instruct?
GLM-4.5V is the better all-round choice, scoring 49/100 against Qwen3-Coder 30B-A3B Instruct (41) and Aya Expanse 32B (33). It leads on inputs & features. Aya Expanse 32B wins on price. Qwen3-Coder 30B-A3B Instruct 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, Aya Expanse 32B, GLM-4.5V or Qwen3-Coder 30B-A3B Instruct?
Aya Expanse 32B is cheaper at $0.50 input / $1.50 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 $0.75 per million tokens for Aya Expanse 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. Aya Expanse 32B has not been scored yet, GLM-4.5V has not been scored yet and Qwen3-Coder 30B-A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, GLM-4.5V and Qwen3-Coder 30B-A3B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3-Coder 30B-A3B Instruct has the largest context window at 262,144 tokens, against 128,000 for Aya Expanse 32B and 64,000 for GLM-4.5V. Maximum output per response: Aya Expanse 32B up to 4,000, GLM-4.5V up to 16,384, Qwen3-Coder 30B-A3B Instruct up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; GLM-4.5V accepts text, images and video; Qwen3-Coder 30B-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 (CC-BY-NC-4.0), 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; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3-Coder 30B-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.
