Llama-3.2-1B vs Qwen-VL OCR vs Qwen2.5-Coder-0.5B
Llama-3.2-1B comes out ahead, 55 to 49 and 36 on our weighted score, and it is the cheaper option too.
- Our pick
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
- Alibaba (Qwen)
Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Llama-3.2-1B is our pick
Llama-3.2-1B is the better all-round choice, scoring 55/100 against Qwen2.5-Coder-0.5B (49) and Qwen-VL OCR (36). It leads on price and context window. Qwen-VL OCR 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-0.5B $0.10 · Qwen-VL OCR $0.72 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Qwen-VL OCR 34,096 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsQwen-VL OCRLlama-3.2-1B: Text · Qwen-VL OCR: Text, Images · Qwen2.5-Coder-0.5B: Text
- Self-hostingLlama-3.2-1B and Qwen2.5-Coder-0.5BPublishes downloadable weights (Llama 3.2 Community License and Apache 2.0)
| Measure | Weight | Llama-3.2-1B | Qwen-VL OCR | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 100 | 57 | 97 |
| Inputs & features | 30% | 0 | 25 | 0 |
| Context window | 20% | 24 | 1 | 0 |
| Overall | 100% | 55/100 | 36/100 | 49/100 |
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 | Llama-3.2-1BMeta | Qwen-VL OCRAlibaba (Qwen) | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 102.0 (best) | — | 88.2 |
| ECI rank | #147 of 148 (best) | — | #148 of 148 |
| GPQA DiamondGraduate-level science questions | 23.9% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | — | — |
| Price per million tokens | |||
| Input | $0.064 (best) | $0.72 | $0.10 |
| Output | $0.15 | $0.72 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.72 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 34,096 tokens | 32,768 tokens |
| Max output | 8,192 tokens (best) | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Proprietary | OpenApache 2.0 |
| API model ID | — | qwen-vl-ocr | — |
| API providers | 2 (best) | 1 | 1 |
| Released | Sep 25, 2024 | Oct 28, 2024 | Nov 12, 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.
- Llama-3.2-1B$0.936
- Qwen-VL OCR$8.64
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.2-1B, Qwen-VL OCR or Qwen2.5-Coder-0.5B?
Llama-3.2-1B is the better all-round choice, scoring 55/100 against Qwen2.5-Coder-0.5B (49) and Qwen-VL OCR (36). It leads on price and context window. Qwen-VL OCR 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, Llama-3.2-1B, Qwen-VL OCR or Qwen2.5-Coder-0.5B?
Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). Qwen2.5-Coder-0.5B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider); Qwen-VL OCR costs $0.72 input / $0.72 output per million tokens (official Alibaba API price). 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 $0.10 for Qwen2.5-Coder-0.5B (1.2× as much) and $0.72 for Qwen-VL OCR (8.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Qwen-VL OCR has not been scored yet and Qwen2.5-Coder-0.5B has an ECI of 88.2.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Qwen-VL OCR and Qwen2.5-Coder-0.5B 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, Qwen-VL OCR and Qwen2.5-Coder-0.5B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B has the largest context window at 131,072 tokens, against 34,096 for Qwen-VL OCR and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Llama-3.2-1B up to 8,192, Qwen-VL OCR up to 4,096, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Qwen-VL OCR accepts text and images; Qwen2.5-Coder-0.5B accepts text. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Llama-3.2-1B and Qwen2.5-Coder-0.5B publishes its weights (Llama 3.2 Community License and Apache 2.0) and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Qwen-VL OCR came out Oct 28, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B 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.
