Qwen2.5 14B Instruct vs Llama 3.1 Nemotron 70B Instruct vs Aya Expanse 32B
Too close to call on our weighted score (Llama 3.1 Nemotron 70B Instruct 45, Qwen2.5 14B Instruct 42, Aya Expanse 32B 33). The right pick depends on what you value most.
Alibaba (Qwen)Qwen2.5 14B Instruct
42/100- ECI—
- Price$0.35 / $1.40
- Context131K
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 3.1 Nemotron 70B Instruct 45/100, Qwen2.5 14B Instruct 42/100, Aya Expanse 32B 33/100), so choose by what matters most for your work: Llama 3.1 Nemotron 70B Instruct on price and Qwen2.5 14B Instruct for long inputs. 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 3.1 Nemotron 70B InstructLlama 3.1 Nemotron 70B Instruct $0.485 · Qwen2.5 14B Instruct $0.613 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 14B InstructQwen2.5 14B Instruct 131,072 · Llama 3.1 Nemotron 70B Instruct 128,000 · Aya Expanse 32B 128,000 tokens
- Widest inputsSame inputsQwen2.5 14B Instruct: Text · Llama 3.1 Nemotron 70B Instruct: Text · Aya Expanse 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5 14B Instruct | Llama 3.1 Nemotron 70B Instruct | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 60 | 65 | 56 |
| Inputs & features | 30% | 25 | 25 | 0 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 42/100 | 45/100 | 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 14B InstructAlibaba (Qwen) | Llama 3.1 Nemotron 70B InstructNVIDIA | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.35 (best) | $0.478 | $0.50 |
| Output | $1.40 | $0.504 (best) | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.613 | $0.485 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 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 | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenCC-BY-NC-4.0 |
| API model ID | qwen2-5-14b-instruct | nvidia/llama-3.1-nemotron-70b-instruct | c4ai-aya-expanse-32b |
| API providers | 1 | 3 (best) | 2 |
| Released | Sep 2024 | Apr 15, 2025 | 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 14B Instruct$6.30
- Llama 3.1 Nemotron 70B Instruct$5.79
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5 14B Instruct, Llama 3.1 Nemotron 70B Instruct or Aya Expanse 32B?
It is close. Our weighted score puts them within 2 points (Llama 3.1 Nemotron 70B Instruct 45/100, Qwen2.5 14B Instruct 42/100, Aya Expanse 32B 33/100), so choose by what matters most for your work: Llama 3.1 Nemotron 70B Instruct on price and Qwen2.5 14B Instruct for long inputs. 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 14B Instruct, Llama 3.1 Nemotron 70B Instruct or Aya Expanse 32B?
Llama 3.1 Nemotron 70B Instruct is cheaper at $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia). Qwen2.5 14B Instruct costs $0.35 input / $1.40 output per million tokens (official Alibaba API price); 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 $0.485 per million tokens for Llama 3.1 Nemotron 70B Instruct versus $0.613 for Qwen2.5 14B Instruct (1.3× as much) and $0.75 for Aya Expanse 32B (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5 14B Instruct has not been scored yet, Llama 3.1 Nemotron 70B Instruct 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 14B Instruct, Llama 3.1 Nemotron 70B Instruct 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 14B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron 70B Instruct and 128,000 for Aya Expanse 32B. Maximum output per response: Qwen2.5 14B Instruct up to 8,192, Llama 3.1 Nemotron 70B Instruct up to 8,192, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 14B Instruct accepts text; Llama 3.1 Nemotron 70B Instruct 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 (CC-BY-NC-4.0), so you can self-host them.
Which is newer?
Llama 3.1 Nemotron 70B Instruct is the newest, released Apr 15, 2025. Aya Expanse 32B came out Oct 24, 2024; Qwen2.5 14B Instruct came out Sep 2024. Knowledge cutoff: Qwen2.5 14B Instruct 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.
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