Aya Expanse 32B vs Qwen Turbo vs Qwen2.5-Coder-32B-Instruct
Qwen Turbo comes out ahead, 72 to 45 and 33 on our weighted score, and it is the cheaper option too.
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
- Our pick
Alibaba (Qwen)Qwen Turbo
72/100- ECI—
- Price$0.05 / $0.20
- Context1M
- Alibaba (Qwen)
Qwen2.5-Coder-32B-Instruct
45/100- ECI—
- Price$0.43 / $0.60
- Context131K
Qwen Turbo is our pick
Qwen Turbo is the better all-round choice, scoring 72/100 against Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on price, inputs & features and 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 priceQwen TurboQwen Turbo $0.087 · Qwen2.5-Coder-32B-Instruct $0.473 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen TurboQwen Turbo 1,000,000 · Qwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsSame inputsAya Expanse 32B: Text · Qwen Turbo: Text · Qwen2.5-Coder-32B-Instruct: Text
- Self-hostingAya Expanse 32B and Qwen2.5-Coder-32B-InstructPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Aya Expanse 32B | Qwen Turbo | Qwen2.5-Coder-32B-Instruct |
|---|---|---|---|---|
| Price | 50% | 56 | 100 | 65 |
| Inputs & features | 30% | 0 | 35 | 25 |
| Context window | 20% | 24 | 60 | 24 |
| Overall | 100% | 33/100 | 72/100 | 45/100 |
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 | Qwen TurboAlibaba (Qwen) | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 41.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 6.1% | — |
| Price per million tokens | |||
| Input | $0.50 | $0.05 (best) | $0.43 |
| Output | $1.50 | $0.20 (best) | $0.60 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.087 (best) | $0.473 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API | Median of 4 providers |
| Limits | |||
| Context window | 128,000 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 4,000 tokens | 16,384 tokens (best) | 8,192 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 | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | Open |
| API model ID | c4ai-aya-expanse-32b | qwen-turbo | — |
| API providers | 2 | 3 | 4 (best) |
| Released | Oct 24, 2024 | Nov 1, 2024 | Nov 12, 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.
- Aya Expanse 32B$8.00
- Qwen Turbo$0.90
- Qwen2.5-Coder-32B-Instruct$5.50
Which should you choose?
Which is better: Aya Expanse 32B, Qwen Turbo or Qwen2.5-Coder-32B-Instruct?
Qwen Turbo is the better all-round choice, scoring 72/100 against Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on price, inputs & features and 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, Qwen Turbo or Qwen2.5-Coder-32B-Instruct?
Qwen Turbo is cheaper at $0.05 input / $0.20 output per million tokens (official Alibaba API price). Qwen2.5-Coder-32B-Instruct costs $0.43 input / $0.60 output per million tokens (median across 4 API providers); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.087 per million tokens for Qwen Turbo versus $0.473 for Qwen2.5-Coder-32B-Instruct (5.4× as much) and $0.75 for Aya Expanse 32B (8.6× 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, Qwen Turbo has not been scored yet and Qwen2.5-Coder-32B-Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, Qwen Turbo and Qwen2.5-Coder-32B-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?
Qwen Turbo has the largest context window at 1,000,000 tokens, against 131,072 for Qwen2.5-Coder-32B-Instruct and 128,000 for Aya Expanse 32B. Maximum output per response: Aya Expanse 32B up to 4,000, Qwen Turbo up to 16,384, Qwen2.5-Coder-32B-Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Qwen Turbo accepts text; Qwen2.5-Coder-32B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Aya Expanse 32B and Qwen2.5-Coder-32B-Instruct publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen Turbo is proprietary.
Which is newer?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Qwen Turbo came out Nov 1, 2024; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: Qwen Turbo 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.
