Qwen2.5-Coder-32B-Instruct vs Saba vs Aya Expanse 32B
Saba comes out ahead, 46 to 39 and 32 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
- Our pick
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
Saba is our pick
Saba is the better all-round choice, scoring 46/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads on price and 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 priceSabaSaba $0.30 · Qwen2.5-Coder-32B-Instruct $0.745 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 · Saba 32,768 tokens
- Widest inputsSabaQwen2.5-Coder-32B-Instruct: Text · Saba: Text, PDFs · 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 | Saba | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 54 | 72 | 53 |
| Inputs & features | 30% | 25 | 35 | 0 |
| Context window | 20% | 25 | 0 | 24 |
| Overall | 100% | 39/100 | 46/100 | 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) | SabaMistral AI | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.66 | $0.20 (best) | $0.50 |
| Output | $1.00 | $0.60 (best) | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.745 | $0.30 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 4 providers | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 26,214 tokens (best) | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | — | — | c4ai-aya-expanse-32b |
| API providers | 7 (best) | 4 | 2 |
| Released | Nov 12, 2024 | Feb 17, 2025 | Oct 24, 2024 |
| Knowledge cutoff | — | Sep 30, 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
- Saba$3.20
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Saba or Aya Expanse 32B?
Saba is the better all-round choice, scoring 46/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads on price and 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, Saba or Aya Expanse 32B?
Saba is cheaper at $0.20 input / $0.60 output per million tokens (median across 4 API providers). Qwen2.5-Coder-32B-Instruct costs $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). At a typical mix of three input tokens to one output token, that is $0.30 per million tokens for Saba versus $0.745 for Qwen2.5-Coder-32B-Instruct (2.5× as much) and $0.75 for Aya Expanse 32B (2.5× 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, Saba 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, Saba 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 Saba. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Saba up to 26,214, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; Saba accepts text and PDFs; Aya Expanse 32B accepts text. Saba handles the widest range of inputs.
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; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 2025. Qwen2.5-Coder-32B-Instruct came out Nov 12, 2024; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: Saba Sep 30, 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.
