Qwen2.5 7B Instruct vs Llama-3.2-90B-Vision-Instruct
Llama-3.2-90B-Vision-Instruct comes out ahead, 44 to 33 on our weighted score.
Alibaba (Qwen)Qwen2.5 7B Instruct
33/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
- Our pick
MetaLlama-3.2-90B-Vision-Instruct
44/100- ECI125.5
- Price—
- Context128K
Add a model
Make it a three-way comparison.
Llama-3.2-90B-Vision-Instruct is our pick
Llama-3.2-90B-Vision-Instruct is the better all-round choice, scoring 44/100 against Qwen2.5 7B Instruct (33). It leads on capability and inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityLlama-3.2-90B-Vision-InstructCapabilities Index (ECI): Llama-3.2-90B-Vision-Instruct 125.5 · Qwen2.5 7B Instruct 118.5
- Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 per 1M tokens (3:1 blend) · Llama-3.2-90B-Vision-Instruct unpriced
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.2-90B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-90B-Vision-InstructQwen2.5 7B Instruct: Text · Llama-3.2-90B-Vision-Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5 7B Instruct | Llama-3.2-90B-Vision-Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 38 | 46 |
| Inputs & features | 20% | 25 | 50 |
| Context window | 13% | 25 | 24 |
| Overall | 100% | 33/100 | 44/100 |
Left out because at least one model lacks the data: price. 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 7B InstructAlibaba (Qwen) | Llama-3.2-90B-Vision-InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 118.5 | 125.5 (best) |
| ECI rank | #159 of 165 | #150 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 35.5% | 41.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 2.5% | 2.6% (best) |
| Price per million tokens | ||
| Input | $0.05 | — |
| Output | $0.10 | — |
| Cached input | — | — |
| Blended (3:1) | $0.063 | — |
| Long-context rate | Same rate | — |
| Price source | Median of 3 providers | — |
| Limits | ||
| Context window | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens (best) | 4,096 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | OpenLlama 3.2 Community License |
| API model ID | — | — |
| API providers | 4 (best) | 1 |
| Released | Sep 19, 2024 | Sep 25, 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 7B Instruct$0.70
- Llama-3.2-90B-Vision-Instruct—
Which should you choose?
Which is better: Qwen2.5 7B Instruct or Llama-3.2-90B-Vision-Instruct?
Llama-3.2-90B-Vision-Instruct is the better all-round choice, scoring 44/100 against Qwen2.5 7B Instruct (33). It leads on capability and inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Qwen2.5 7B Instruct or Llama-3.2-90B-Vision-Instruct?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). . 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 . Llama-3.2-90B-Vision-Instruct has no published per-token price.
Which scores higher on benchmarks?
Llama-3.2-90B-Vision-Instruct scores higher on the Capabilities Index (ECI): Llama-3.2-90B-Vision-Instruct 125.5 (#150 of 165) and Qwen2.5 7B Instruct 118.5 (#159 of 165). The confidence ranges of the top two overlap (120.3–127.4 vs 110.6–121.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama-3.2-90B-Vision-Instruct 41.0%, Qwen2.5 7B Instruct 35.5%; OTIS Mock AIME 2024–2025 — Llama-3.2-90B-Vision-Instruct 2.6%, Qwen2.5 7B Instruct 2.5%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5 7B Instruct and Llama-3.2-90B-Vision-Instruct yet, so there is no like-for-like coding score. On overall capability, Llama-3.2-90B-Vision-Instruct leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.2-90B-Vision-Instruct. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Llama-3.2-90B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct accepts text; Llama-3.2-90B-Vision-Instruct accepts text and images. Llama-3.2-90B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache 2.0 and Llama 3.2 Community License), so you can self-host them.
Which is newer?
Llama-3.2-90B-Vision-Instruct is the newest, released Sep 25, 2024. Qwen2.5 7B Instruct came out Sep 19, 2024. Knowledge cutoff: Llama-3.2-90B-Vision-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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