Phi-4-multimodal-instruct vs Qwen-VL OCR
Phi-4-multimodal-instruct comes out ahead, 72 to 55 on our weighted score, and it is the cheaper option too.
- Our pick
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
Alibaba (Qwen)Qwen-VL OCR
55/100- ECI—
- Price$0.07 / $0.16
- Context34K
Add a model
Make it a three-way comparison.
Phi-4-multimodal-instruct is our pick
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Qwen-VL OCR (55). It leads on price, inputs & features and context window. 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 per 1M tokens (3:1 blend)
- Longest contextPhi-4-multimodal-instructPhi-4-multimodal-instruct 131,072 · Qwen-VL OCR 34,096 tokens
- Widest inputsPhi-4-multimodal-instructPhi-4-multimodal-instruct: Text, Images, Audio · Qwen-VL OCR: Text, Images
- Self-hostingPhi-4-multimodal-instructPublishes downloadable weights (MIT)
| Measure | Weight | Phi-4-multimodal-instruct | Qwen-VL OCR |
|---|---|---|---|
| Price | 50% | 98 | 95 |
| Inputs & features | 30% | 60 | 25 |
| Context window | 20% | 25 | 1 |
| Overall | 100% | 72/100 | 55/100 |
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 | Phi-4-multimodal-instructMicrosoft | Qwen-VL OCRAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.07 | $0.07 |
| Output | $0.11 (best) | $0.16 |
| Cached input | — | — |
| Blended (3:1) | $0.08 (best) | $0.092 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API |
| Limits | ||
| Context window | 131,072 tokens (best) | 34,096 tokens |
| Max output | 131,072 tokens (best) | 4,096 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | Yes | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenMIT | Proprietary |
| API model ID | — | qwen-vl-ocr |
| API providers | 1 | 1 |
| Released | Feb 26, 2025 | Oct 28, 2024 |
| Knowledge cutoff | Jun 2024 | 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.
- Phi-4-multimodal-instruct$0.92
- Qwen-VL OCR$1.02
Which should you choose?
Which is better: Phi-4-multimodal-instruct or Qwen-VL OCR?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Qwen-VL OCR (55). It leads on price, inputs & features and context window. 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, Phi-4-multimodal-instruct or Qwen-VL OCR?
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). 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).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Phi-4-multimodal-instruct has not been scored yet and Qwen-VL OCR has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Phi-4-multimodal-instruct and Qwen-VL OCR 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?
Phi-4-multimodal-instruct has the largest context window at 131,072 tokens, against 34,096 for Qwen-VL OCR. Maximum output per response: Phi-4-multimodal-instruct up to 131,072, Qwen-VL OCR up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Phi-4-multimodal-instruct accepts text, images and audio; Qwen-VL OCR accepts text and images. Phi-4-multimodal-instruct 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 is proprietary.
Which is newer?
Phi-4-multimodal-instruct is the newest, released Feb 26, 2025. Qwen-VL OCR came out Oct 28, 2024. Knowledge cutoff: Phi-4-multimodal-instruct Jun 2024, 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 Nov 2, 2026.
