Qwen2.5-Coder-32B-Instruct vs Qwen Max vs Aya Expanse 32B
Qwen2.5-Coder-32B-Instruct comes out ahead, 39 to 32 and 24 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
- Alibaba (Qwen)
Qwen Max
24/100- ECI—
- Price$1.60 / $6.40
- Context33K
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
Qwen2.5-Coder-32B-Instruct is our pick
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 39/100 against Aya Expanse 32B (32) and Qwen Max (24). Qwen Max 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 priceQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct $0.745 · Aya Expanse 32B $0.75 · Qwen Max $2.80 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 · Qwen Max 32,768 tokens
- Widest inputsSame inputsQwen2.5-Coder-32B-Instruct: Text · Qwen Max: Text · Aya Expanse 32B: Text
- Self-hostingQwen2.5-Coder-32B-Instruct and Aya Expanse 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Qwen2.5-Coder-32B-Instruct | Qwen Max | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 54 | 27 | 53 |
| Inputs & features | 30% | 25 | 35 | 0 |
| Context window | 20% | 25 | 0 | 24 |
| Overall | 100% | 39/100 | 24/100 | 32/100 |
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 | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | Qwen MaxAlibaba (Qwen) | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.66 | $1.60 | $0.50 (best) |
| Output | $1.00 (best) | $6.40 | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.745 (best) | $2.80 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official Alibaba API | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Max output | 8,192 tokens (best) | 8,192 tokens (best) | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | — | qwen-max | c4ai-aya-expanse-32b |
| API providers | 7 | 8 (best) | 2 |
| Released | Nov 12, 2024 | Apr 3, 2024 | Oct 24, 2024 |
| Knowledge cutoff | — | 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.
- Qwen2.5-Coder-32B-Instruct$8.60
- Qwen Max$28.80
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Qwen Max or Aya Expanse 32B?
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 39/100 against Aya Expanse 32B (32) and Qwen Max (24). Qwen Max 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, Qwen2.5-Coder-32B-Instruct, Qwen Max or Aya Expanse 32B?
Qwen2.5-Coder-32B-Instruct is cheaper at $0.66 input / $1.00 output per million tokens (median across 7 API providers). Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (official Cohere API price); Qwen Max costs $1.60 input / $6.40 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 Qwen2.5-Coder-32B-Instruct versus $0.75 for Aya Expanse 32B (1× as much) and $2.80 for Qwen Max (3.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5-Coder-32B-Instruct has not been scored yet, Qwen Max has not been scored yet and Aya Expanse 32B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-Coder-32B-Instruct, Qwen Max and Aya Expanse 32B 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?
Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 32,768 for Qwen Max. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Qwen Max up to 8,192, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; Qwen Max accepts text; Aya Expanse 32B accepts text. They handle the same number of input types.
Are any of these open source?
Qwen2.5-Coder-32B-Instruct and Aya Expanse 32B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen Max is proprietary.
Which is newer?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Aya Expanse 32B came out Oct 24, 2024; Qwen Max came out Apr 3, 2024. Knowledge cutoff: Qwen Max 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 Nov 2, 2026.
