Qwen-VL OCR vs Phi-4-multimodal-instruct vs Nova Lite
Nova Lite comes out ahead, 75 to 72 and 55 on our weighted score, though Phi-4-multimodal-instruct is 24% cheaper per token.
Alibaba (Qwen)Qwen-VL OCR
55/100- ECI—
- Price$0.07 / $0.16
- Context34K
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
- Our pick
AmazonNova Lite
75/100- ECI—
- Price$0.06 / $0.24
- Context300K
Nova Lite is our pick
Nova Lite is the better all-round choice, scoring 75/100 against Phi-4-multimodal-instruct (72) and Qwen-VL OCR (55). It leads on inputs & features and context window. Phi-4-multimodal-instruct 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 pricePhi-4-multimodal-instructPhi-4-multimodal-instruct $0.08 · Qwen-VL OCR $0.092 · Nova Lite $0.105 per 1M tokens (3:1 blend)
- Longest contextNova LiteNova Lite 300,000 · Phi-4-multimodal-instruct 131,072 · Qwen-VL OCR 34,096 tokens
- Widest inputsNova LiteQwen-VL OCR: Text, Images · Phi-4-multimodal-instruct: Text, Images, Audio · Nova Lite: Text, Images, PDFs, Video
- Self-hostingPhi-4-multimodal-instructPublishes downloadable weights (MIT)
| Measure | Weight | Qwen-VL OCR | Phi-4-multimodal-instruct | Nova Lite |
|---|---|---|---|---|
| Price | 50% | 95 | 98 | 92 |
| Inputs & features | 30% | 25 | 60 | 70 |
| Context window | 20% | 1 | 25 | 39 |
| Overall | 100% | 55/100 | 72/100 | 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) | Phi-4-multimodal-instructMicrosoft | Nova LiteAmazon |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.07 | $0.06 (best) |
| Output | $0.16 | $0.11 (best) | $0.24 |
| Cached input | — | — | $0.015 |
| Blended (3:1) | $0.092 | $0.08 (best) | $0.105 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 1 providers | Official Amazon Bedrock API |
| Limits | |||
| Context window | 34,096 tokens | 131,072 tokens | 300,000 tokens (best) |
| Max output | 4,096 tokens | 131,072 tokens (best) | 10,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | Yes | No |
| Video | No | No | Yes |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenMIT | Proprietary |
| API model ID | qwen-vl-ocr | — | amazon.nova-lite-v1:0 |
| API providers | 1 | 1 | 8 (best) |
| Released | Oct 28, 2024 | Feb 26, 2025 | 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
- Phi-4-multimodal-instruct$0.92
- Nova Lite$1.08
Which should you choose?
Which is better: Qwen-VL OCR, Phi-4-multimodal-instruct or Nova Lite?
Nova Lite is the better all-round choice, scoring 75/100 against Phi-4-multimodal-instruct (72) and Qwen-VL OCR (55). It leads on inputs & features and context window. Phi-4-multimodal-instruct 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, Phi-4-multimodal-instruct or Nova Lite?
Phi-4-multimodal-instruct is cheaper at $0.07 input / $0.11 output per million tokens (median across 1 API provider). Qwen-VL OCR costs $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). At a typical mix of three input tokens to one output token, that is $0.08 per million tokens for Phi-4-multimodal-instruct versus $0.092 for Qwen-VL OCR (1.2× as much) and $0.105 for Nova Lite (1.3× 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, Phi-4-multimodal-instruct 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, Phi-4-multimodal-instruct 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?
Nova Lite has the largest context window at 300,000 tokens, against 131,072 for Phi-4-multimodal-instruct and 34,096 for Qwen-VL OCR. Maximum output per response: Qwen-VL OCR up to 4,096, Phi-4-multimodal-instruct up to 131,072, Nova Lite up to 10,000 tokens.
Which can read images, PDFs, audio or video?
Qwen-VL OCR accepts text and images; Phi-4-multimodal-instruct accepts text, images and audio; Nova Lite accepts text, images, PDFs and video. Nova Lite handles the widest range of inputs.
Are any of these open source?
Phi-4-multimodal-instruct publishes its weights (MIT) and can be self-hosted; Qwen-VL OCR and Nova Lite is proprietary.
Which is newer?
Phi-4-multimodal-instruct is the newest, released Feb 26, 2025. Nova Lite came out Dec 3, 2024; Qwen-VL OCR came out Oct 28, 2024. Knowledge cutoff: Qwen-VL OCR Apr 2024, Phi-4-multimodal-instruct 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.
