Qwen2.5-Coder-32B-Instruct vs Llama-3.2-1B vs Aya Expanse 32B
Llama-3.2-1B comes out ahead, 55 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 pick
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Llama-3.2-1B is our pick
Llama-3.2-1B is the better all-round choice, scoring 55/100 against Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on price. Qwen2.5-Coder-32B-Instruct 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 priceLlama-3.2-1BLlama-3.2-1B $0.085 · Qwen2.5-Coder-32B-Instruct $0.473 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-Instruct and Llama-3.2-1BQwen2.5-Coder-32B-Instruct 131,072 · Llama-3.2-1B 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsSame inputsQwen2.5-Coder-32B-Instruct: Text · Llama-3.2-1B: Text · Aya Expanse 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-Coder-32B-Instruct | Llama-3.2-1B | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 65 | 100 | 56 |
| Inputs & features | 30% | 25 | 0 | 0 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 45/100 | 55/100 | 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) | Llama-3.2-1BMeta | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 102.0 | — |
| ECI rank | — | #147 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 23.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 0.6% | — |
| Price per million tokens | |||
| Input | $0.43 | $0.064 (best) | $0.50 |
| Output | $0.60 | $0.15 (best) | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.473 | $0.085 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 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 | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | OpenCC-BY-NC-4.0 |
| API model ID | — | — | c4ai-aya-expanse-32b |
| API providers | 4 (best) | 2 | 2 |
| Released | Nov 12, 2024 | Sep 25, 2024 | Oct 24, 2024 |
| Knowledge cutoff | — | Dec 2023 | — |
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
- Llama-3.2-1B$0.936
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Llama-3.2-1B or Aya Expanse 32B?
Llama-3.2-1B is the better all-round choice, scoring 55/100 against Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on price. Qwen2.5-Coder-32B-Instruct 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, Llama-3.2-1B or Aya Expanse 32B?
Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). 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 $0.085 per million tokens for Llama-3.2-1B versus $0.473 for Qwen2.5-Coder-32B-Instruct (5.5× as much) and $0.75 for Aya Expanse 32B (8.8× 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, Llama-3.2-1B has an ECI of 102.0 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, Llama-3.2-1B 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 Llama-3.2-1B and Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-Coder-32B-Instruct and Llama-3.2-1B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Aya Expanse 32B. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Llama-3.2-1B 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; Llama-3.2-1B accepts text; Aya Expanse 32B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Llama 3.2 Community License and CC-BY-NC-4.0), so you can self-host them.
Which is newer?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Aya Expanse 32B came out Oct 24, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023.
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.
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