Aya Expanse 32B vs Mistral Large vs Qwen2.5-Coder-32B-Instruct
Mistral Large comes out ahead, 45 to 39 and 32 on our weighted score.
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
- Our pick
Mistral AIMistral Large
45/100- ECI122.0
- Price$0.50 / $1.50
- Context128K
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
Mistral Large is our pick
Mistral Large is the better all-round choice, scoring 45/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads 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 · Mistral Large $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 · Mistral Large 128,000 tokens
- Widest inputsMistral LargeAya Expanse 32B: Text · Mistral Large: Text, PDFs · 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 | Mistral Large | Qwen2.5-Coder-32B-Instruct |
|---|---|---|---|---|
| Price | 50% | 53 | 53 | 54 |
| Inputs & features | 30% | 0 | 45 | 25 |
| Context window | 20% | 24 | 24 | 25 |
| Overall | 100% | 32/100 | 45/100 | 39/100 |
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 | Mistral LargeMistral AI | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 122.0 | — |
| ECI rank | — | #153 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 38.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.9% | — |
| Price per million tokens | |||
| Input | $0.50 (best) | $0.50 (best) | $0.66 |
| Output | $1.50 | $1.50 | $1.00 (best) |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.75 | $0.75 | $0.745 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Mistral API | Median of 7 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 102,400 tokens (best) | 8,192 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 | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | Open |
| API model ID | c4ai-aya-expanse-32b | mistral-large-latest | — |
| API providers | 2 | 11 (best) | 7 |
| Released | Oct 24, 2024 | Feb 26, 2024 | Nov 12, 2024 |
| Knowledge cutoff | — | Nov 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.
- Aya Expanse 32B$8.00
- Mistral Large$8.00
- Qwen2.5-Coder-32B-Instruct$8.60
Which should you choose?
Which is better: Aya Expanse 32B, Mistral Large or Qwen2.5-Coder-32B-Instruct?
Mistral Large is the better all-round choice, scoring 45/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads 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, Aya Expanse 32B, Mistral Large or Qwen2.5-Coder-32B-Instruct?
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); Mistral Large costs $0.50 input / $1.50 output per million tokens (official Mistral 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 $0.75 for Mistral Large (1× 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, Mistral Large has an ECI of 122.0 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, Mistral Large 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?
Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 128,000 for Mistral Large. Maximum output per response: Aya Expanse 32B up to 4,000, Mistral Large up to 102,400, Qwen2.5-Coder-32B-Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Mistral Large accepts text and PDFs; Qwen2.5-Coder-32B-Instruct accepts text. Mistral Large handles the widest range of inputs.
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; Mistral Large 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; Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 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.
