Aya Expanse 32B vs Llama-3.1-8B-Instruct vs Qwen-VL OCR
Llama-3.1-8B-Instruct comes out ahead, 56 to 36 and 33 on our weighted score, and it is the cheaper option too.
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
- Our pick
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
Llama-3.1-8B-Instruct is our pick
Llama-3.1-8B-Instruct is the better all-round choice, scoring 56/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. 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-3.1-8B-InstructLlama-3.1-8B-Instruct $0.156 · Qwen-VL OCR $0.72 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextAya Expanse 32B and Llama-3.1-8B-InstructAya Expanse 32B 128,000 · Llama-3.1-8B-Instruct 128,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsQwen-VL OCRAya Expanse 32B: Text · Llama-3.1-8B-Instruct: Text · Qwen-VL OCR: Text, Images
- Self-hostingAya Expanse 32B and Llama-3.1-8B-InstructPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Aya Expanse 32B | Llama-3.1-8B-Instruct | Qwen-VL OCR |
|---|---|---|---|---|
| Price | 50% | 56 | 88 | 57 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 24 | 1 |
| Overall | 100% | 33/100 | 56/100 | 36/100 |
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 | Qwen-VL OCRAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.6 | — |
| ECI rank | — | #145 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 27.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% | — |
| Price per million tokens | |||
| Input | $0.50 | $0.152 (best) | $0.72 |
| Output | $1.50 | $0.167 (best) | $0.72 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.156 (best) | $0.72 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 9 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 34,096 tokens |
| Max output | 4,000 tokens | 4,096 tokens (best) | 4,096 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Open | Proprietary |
| API model ID | c4ai-aya-expanse-32b | — | qwen-vl-ocr |
| API providers | 2 | 9 (best) | 1 |
| Released | Oct 24, 2024 | Jul 23, 2024 | Oct 28, 2024 |
| Knowledge cutoff | — | Dec 2023 | 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.
- Aya Expanse 32B$8.00
- Llama-3.1-8B-Instruct$1.85
- Qwen-VL OCR$8.64
Which should you choose?
Which is better: Aya Expanse 32B, Llama-3.1-8B-Instruct or Qwen-VL OCR?
Llama-3.1-8B-Instruct is the better all-round choice, scoring 56/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. 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 Qwen-VL OCR?
Llama-3.1-8B-Instruct is cheaper at $0.152 input / $0.167 output per million tokens (median across 9 API providers). Qwen-VL OCR costs $0.72 input / $0.72 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 $0.156 per million tokens for Llama-3.1-8B-Instruct versus $0.72 for Qwen-VL OCR (4.6× as much) and $0.75 for Aya Expanse 32B (4.8× 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 116.6 and Qwen-VL OCR has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, Llama-3.1-8B-Instruct and Qwen-VL OCR yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B and Qwen-VL OCR does not support tool calling, which most coding agents need.
Which has the bigger context window?
Aya Expanse 32B and Llama-3.1-8B-Instruct have the largest context windows (128,000 and 128,000 tokens), against 34,096 for Qwen-VL OCR. Maximum output per response: Aya Expanse 32B up to 4,000, Llama-3.1-8B-Instruct up to 4,096, Qwen-VL OCR up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Llama-3.1-8B-Instruct accepts text; Qwen-VL OCR accepts text and images. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Aya Expanse 32B and Llama-3.1-8B-Instruct publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Qwen-VL OCR is the newest, released Oct 28, 2024. Aya Expanse 32B came out Oct 24, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct Dec 2023, Qwen-VL OCR 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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