Llama-3.2-3B vs Qwen-VL OCR vs Gemini 2.0 Flash-Lite
Gemini 2.0 Flash-Lite comes out ahead, 82 to 55 and 50 on our weighted score, though Qwen-VL OCR is 17% cheaper per token.
MetaLlama-3.2-3B
50/100- ECI—
- Price$0.051 / $0.333
- Context131K
Alibaba (Qwen)Qwen-VL OCR
55/100- ECI—
- Price$0.07 / $0.16
- Context34K
- Our pick
GoogleGemini 2.0 Flash-Lite
82/100- ECI—
- Price$0.064 / $0.255
- Context1.05M
Gemini 2.0 Flash-Lite is our pick
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 82/100 against Qwen-VL OCR (55) and Llama-3.2-3B (50). It leads on inputs & features and context window. Qwen-VL OCR wins 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 priceQwen-VL OCRQwen-VL OCR $0.092 · Gemini 2.0 Flash-Lite $0.111 · Llama-3.2-3B $0.121 per 1M tokens (3:1 blend)
- Longest contextGemini 2.0 Flash-LiteGemini 2.0 Flash-Lite 1,048,576 · Llama-3.2-3B 131,072 · Qwen-VL OCR 34,096 tokens
- Widest inputsGemini 2.0 Flash-LiteLlama-3.2-3B: Text · Qwen-VL OCR: Text, Images · Gemini 2.0 Flash-Lite: Text, Images, PDFs, Audio, Video
- Self-hostingLlama-3.2-3BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | Llama-3.2-3B | Qwen-VL OCR | Gemini 2.0 Flash-Lite |
|---|---|---|---|---|
| Price | 50% | 89 | 95 | 91 |
| Inputs & features | 30% | 0 | 25 | 80 |
| Context window | 20% | 25 | 1 | 61 |
| Overall | 100% | 50/100 | 55/100 | 82/100 |
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-3.2-3BMeta | Qwen-VL OCRAlibaba (Qwen) | Gemini 2.0 Flash-LiteGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.051 (best) | $0.07 | $0.064 |
| Output | $0.333 | $0.16 (best) | $0.255 |
| Cached input | — | — | — |
| Blended (3:1) | $0.121 | $0.092 (best) | $0.111 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 6 providers | Official Alibaba API | Median of 2 providers |
| Limits | |||
| Context window | 131,072 tokens | 34,096 tokens | 1,048,576 tokens (best) |
| Max output | 8,192 tokens (best) | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | No | No | No |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Proprietary | Proprietary |
| API model ID | — | qwen-vl-ocr | — |
| API providers | 6 (best) | 1 | 3 |
| Released | Sep 25, 2024 | Oct 28, 2024 | Dec 11, 2024 |
| Knowledge cutoff | Dec 2023 | Apr 2024 | Jun 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-3.2-3B$1.17
- Qwen-VL OCR$1.02
- Gemini 2.0 Flash-Lite$1.15
Which should you choose?
Which is better: Llama-3.2-3B, Qwen-VL OCR or Gemini 2.0 Flash-Lite?
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 82/100 against Qwen-VL OCR (55) and Llama-3.2-3B (50). It leads on inputs & features and context window. Qwen-VL OCR wins 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, Llama-3.2-3B, Qwen-VL OCR or Gemini 2.0 Flash-Lite?
Qwen-VL OCR is cheaper at $0.07 input / $0.16 output per million tokens (official Alibaba API price). Gemini 2.0 Flash-Lite costs $0.064 input / $0.255 output per million tokens (median across 2 API providers); Llama-3.2-3B costs $0.051 input / $0.333 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.092 per million tokens for Qwen-VL OCR versus $0.111 for Gemini 2.0 Flash-Lite (1.2× as much) and $0.121 for Llama-3.2-3B (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-3B has not been scored yet, Qwen-VL OCR has not been scored yet and Gemini 2.0 Flash-Lite has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-3B, Qwen-VL OCR and Gemini 2.0 Flash-Lite yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-3B and Qwen-VL OCR does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemini 2.0 Flash-Lite has the largest context window at 1,048,576 tokens, against 131,072 for Llama-3.2-3B and 34,096 for Qwen-VL OCR. Maximum output per response: Llama-3.2-3B up to 8,192, Qwen-VL OCR up to 4,096, Gemini 2.0 Flash-Lite up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-3B accepts text; Qwen-VL OCR accepts text and images; Gemini 2.0 Flash-Lite accepts text, images, PDFs, audio and video. Gemini 2.0 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Llama-3.2-3B publishes its weights (Llama 3.2 Community License) and can be self-hosted; Qwen-VL OCR and Gemini 2.0 Flash-Lite is proprietary.
Which is newer?
Gemini 2.0 Flash-Lite is the newest, released Dec 11, 2024. Qwen-VL OCR came out Oct 28, 2024; Llama-3.2-3B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-3B Dec 2023, Qwen-VL OCR Apr 2024, Gemini 2.0 Flash-Lite Jun 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 Nov 2, 2026.
