Qwen-VL OCR vs Gemini 2.0 Flash-Lite vs Nova Lite
Gemini 2.0 Flash-Lite comes out ahead, 82 to 75 and 55 on our weighted score, though Qwen-VL OCR is 17% cheaper per token.
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
AmazonNova Lite
75/100- ECI—
- Price$0.06 / $0.24
- Context300K
Gemini 2.0 Flash-Lite is our pick
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 82/100 against Nova Lite (75) and Qwen-VL OCR (55). 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 · Nova Lite $0.105 · Gemini 2.0 Flash-Lite $0.111 per 1M tokens (3:1 blend)
- Longest contextGemini 2.0 Flash-LiteGemini 2.0 Flash-Lite 1,048,576 · Nova Lite 300,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsGemini 2.0 Flash-LiteQwen-VL OCR: Text, Images · Gemini 2.0 Flash-Lite: Text, Images, PDFs, Audio, Video · Nova Lite: Text, Images, PDFs, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Qwen-VL OCR | Gemini 2.0 Flash-Lite | Nova Lite |
|---|---|---|---|---|
| Price | 50% | 95 | 91 | 92 |
| Inputs & features | 30% | 25 | 80 | 70 |
| Context window | 20% | 1 | 61 | 39 |
| Overall | 100% | 55/100 | 82/100 | 75/100 |
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 | Qwen-VL OCRAlibaba (Qwen) | Gemini 2.0 Flash-LiteGoogle | Nova LiteAmazon |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.064 | $0.06 (best) |
| Output | $0.16 (best) | $0.255 | $0.24 |
| Cached input | — | — | $0.015 |
| Blended (3:1) | $0.092 (best) | $0.111 | $0.105 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Official Amazon Bedrock API |
| Limits | |||
| Context window | 34,096 tokens | 1,048,576 tokens (best) | 300,000 tokens |
| Max output | 4,096 tokens | 8,192 tokens | 10,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | Yes | No |
| Video | No | Yes | Yes |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | qwen-vl-ocr | — | amazon.nova-lite-v1:0 |
| API providers | 1 | 3 | 8 (best) |
| Released | Oct 28, 2024 | Dec 11, 2024 | Dec 3, 2024 |
| Knowledge cutoff | Apr 2024 | Jun 2024 | Oct 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen-VL OCR$1.02
- Gemini 2.0 Flash-Lite$1.15
- Nova Lite$1.08
Which should you choose?
Which is better: Qwen-VL OCR, Gemini 2.0 Flash-Lite or Nova Lite?
Gemini 2.0 Flash-Lite is the better all-round choice, scoring 82/100 against Nova Lite (75) and Qwen-VL OCR (55). 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, Qwen-VL OCR, Gemini 2.0 Flash-Lite or Nova Lite?
Qwen-VL OCR is cheaper at $0.07 input / $0.16 output per million tokens (official Alibaba API price). Nova Lite costs $0.06 input / $0.24 output per million tokens (official Amazon Bedrock API price); Gemini 2.0 Flash-Lite costs $0.064 input / $0.255 output per million tokens (median across 2 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 $0.105 for Nova Lite (1.1× as much) and $0.111 for Gemini 2.0 Flash-Lite (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen-VL OCR has not been scored yet, Gemini 2.0 Flash-Lite has not been scored yet and Nova Lite has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen-VL OCR, Gemini 2.0 Flash-Lite and Nova Lite yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that 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 300,000 for Nova Lite and 34,096 for Qwen-VL OCR. Maximum output per response: Qwen-VL OCR up to 4,096, Gemini 2.0 Flash-Lite up to 8,192, Nova Lite up to 10,000 tokens.
Which can read images, PDFs, audio or video?
Qwen-VL OCR accepts text and images; Gemini 2.0 Flash-Lite accepts text, images, PDFs, audio and video; Nova Lite accepts text, images, PDFs and video. Gemini 2.0 Flash-Lite handles the widest range of inputs.
Are any of these open source?
No. Qwen-VL OCR, Gemini 2.0 Flash-Lite and Nova Lite are proprietary and only available through APIs and apps.
Which is newer?
Gemini 2.0 Flash-Lite is the newest, released Dec 11, 2024. Nova Lite came out Dec 3, 2024; Qwen-VL OCR came out Oct 28, 2024. Knowledge cutoff: Qwen-VL OCR Apr 2024, Gemini 2.0 Flash-Lite Jun 2024, Nova Lite Oct 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.
