Aya Expanse 32B vs Llama-3.1-8B-Instruct vs Qwen2.5 7B Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 62, Llama-3.1-8B-Instruct 62, Aya Expanse 32B 32). The right pick depends on what you value most.
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
MetaLlama-3.1-8B-Instruct
62/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Alibaba (Qwen)Qwen2.5 7B Instruct
62/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Aya Expanse 32B 32/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B 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 priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Aya Expanse 32B 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsAya Expanse 32B: Text · Llama-3.1-8B-Instruct: Text · Qwen2.5 7B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Aya Expanse 32B | Llama-3.1-8B-Instruct | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 53 | 100 | 100 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 24 | 25 |
| Overall | 100% | 32/100 | 62/100 | 62/100 |
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 | Llama-3.1-8B-InstructMeta | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.4 | 118.5 (best) |
| ECI rank | — | #162 of 165 | #159 of 165 (best) |
| GPQA DiamondGraduate-level science questions | — | 27.0% | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% | 2.5% (best) |
| Price per million tokens | |||
| Input | $0.50 | $0.06 | $0.05 (best) |
| Output | $1.50 | $0.08 (best) | $0.10 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.065 | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 17 providers | Median of 3 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 4,096 tokens | 8,192 tokens (best) |
| 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 | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Open | OpenApache 2.0 |
| API model ID | c4ai-aya-expanse-32b | — | — |
| API providers | 2 | 18 (best) | 4 |
| Released | Oct 24, 2024 | Jul 23, 2024 | Sep 19, 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.
- Aya Expanse 32B$8.00
- Llama-3.1-8B-Instruct$0.76
- Qwen2.5 7B Instruct$0.70
Which should you choose?
Which is better: Aya Expanse 32B, Llama-3.1-8B-Instruct or Qwen2.5 7B Instruct?
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Aya Expanse 32B 32/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B 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, Aya Expanse 32B, Llama-3.1-8B-Instruct or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Llama-3.1-8B-Instruct costs $0.06 input / $0.08 output per million tokens (median across 17 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 $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.065 for Llama-3.1-8B-Instruct (1× as much) and $0.75 for Aya Expanse 32B (12× 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, Llama-3.1-8B-Instruct has an ECI of 116.4 and Qwen2.5 7B Instruct has an ECI of 118.5.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, Llama-3.1-8B-Instruct and Qwen2.5 7B 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 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Aya Expanse 32B up to 4,000, Llama-3.1-8B-Instruct up to 4,096, Qwen2.5 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Llama-3.1-8B-Instruct accepts text; Qwen2.5 7B Instruct 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 and Apache 2.0), so you can self-host them.
Which is newer?
Aya Expanse 32B is the newest, released Oct 24, 2024. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct 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 Nov 2, 2026.
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