Qwen2.5-Coder-32B-Instruct vs Qwen Turbo vs Aya Expanse 32B
Qwen Turbo comes out ahead, 72 to 45 and 33 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
45/100- ECI—
- Price$0.43 / $0.60
- Context131K
- Our pickAlibaba (Qwen)
Qwen Turbo
72/100- ECI—
- Price$0.05 / $0.20
- Context1M
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
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 inputsQwen2.5-Coder-32B-Instruct: Text · Qwen Turbo: 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 Turbo | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 65 | 100 | 56 |
| Inputs & features | 30% | 25 | 35 | 0 |
| Context window | 20% | 24 | 60 | 24 |
| Overall | 100% | 45/100 | 72/100 | 33/100 |
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 TurboAlibaba (Qwen) | Aya Expanse 32BCohere |
|---|---|---|---|
| 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 | $0.43 | $0.05 (best) | $0.50 |
| Output | $0.60 | $0.20 (best) | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.473 | $0.087 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 16,384 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 | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | — | qwen-turbo | c4ai-aya-expanse-32b |
| API providers | 4 (best) | 3 | 2 |
| Released | Nov 12, 2024 | Nov 1, 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$5.50
- Qwen Turbo$0.90
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Qwen Turbo or Aya Expanse 32B?
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, Qwen2.5-Coder-32B-Instruct, Qwen Turbo or Aya Expanse 32B?
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. Qwen2.5-Coder-32B-Instruct has not been scored yet, Qwen Turbo 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 Turbo 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?
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: Qwen2.5-Coder-32B-Instruct up to 8,192, Qwen Turbo up to 16,384, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; Qwen Turbo 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 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.
Related comparisons
- Qwen2.5-Coder-32B-Instruct vs Llama 3.1 Nemotron 70B InstructNVIDIA
- Qwen2.5-Coder-32B-Instruct vs CodestralMistral AI
- Qwen2.5-Coder-32B-Instruct vs Llama-3.2-11B-Vision-InstructMeta
- Qwen Turbo vs Gemma 3 12B ITGoogle · ECI 123.5
- Qwen Turbo vs Claude Haiku 3.5Anthropic · ECI 127.2
- Qwen Turbo vs GPT-4o miniOpenAI · ECI 126.6
