Command R7B Arabic vs Phi-4-multimodal-instruct vs Qwen-VL OCR
Phi-4-multimodal-instruct comes out ahead, 72 to 65 and 55 on our weighted score, though Command R7B Arabic is 18% cheaper per token.
CohereCommand R7B Arabic
65/100- ECI—
- Price$0.037 / $0.15
- Context128K
- 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
Phi-4-multimodal-instruct is our pick
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B Arabic (65) and Qwen-VL OCR (55). It leads on inputs & features. Command R7B Arabic 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 priceCommand R7B ArabicCommand R7B Arabic $0.066 · Phi-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 · Command R7B Arabic 128,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsPhi-4-multimodal-instructCommand R7B Arabic: Text · Phi-4-multimodal-instruct: Text, Images, Audio · Qwen-VL OCR: Text, Images
- Self-hostingCommand R7B Arabic and Phi-4-multimodal-instructPublishes downloadable weights (MIT)
| Measure | Weight | Command R7B Arabic | Phi-4-multimodal-instruct | Qwen-VL OCR |
|---|---|---|---|---|
| Price | 50% | 100 | 98 | 95 |
| Inputs & features | 30% | 35 | 60 | 25 |
| Context window | 20% | 24 | 25 | 1 |
| Overall | 100% | 65/100 | 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 | Command R7B ArabicCohere | Phi-4-multimodal-instructMicrosoft | Qwen-VL OCRAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.037 (best) | $0.07 | $0.07 |
| Output | $0.15 | $0.11 (best) | $0.16 |
| Cached input | — | — | — |
| Blended (3:1) | $0.066 (best) | $0.08 | $0.092 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 34,096 tokens |
| Max output | 4,000 tokens | 131,072 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | OpenMIT | Proprietary |
| API model ID | command-r7b-arabic-02-2025 | — | qwen-vl-ocr |
| API providers | 1 | 1 | 1 |
| Released | Feb 27, 2025 | Feb 26, 2025 | Oct 28, 2024 |
| Knowledge cutoff | Jun 1, 2024 | 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.
- Command R7B Arabic$0.675
- Phi-4-multimodal-instruct$0.92
- Qwen-VL OCR$1.02
Which should you choose?
Which is better: Command R7B Arabic, Phi-4-multimodal-instruct or Qwen-VL OCR?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B Arabic (65) and Qwen-VL OCR (55). It leads on inputs & features. Command R7B Arabic 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, Command R7B Arabic, Phi-4-multimodal-instruct or Qwen-VL OCR?
Command R7B Arabic is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). Phi-4-multimodal-instruct costs $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 $0.066 per million tokens for Command R7B Arabic versus $0.08 for Phi-4-multimodal-instruct (1.2× as much) and $0.092 for Qwen-VL OCR (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R7B Arabic has not been scored 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 Command R7B Arabic, 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 128,000 for Command R7B Arabic and 34,096 for Qwen-VL OCR. Maximum output per response: Command R7B Arabic up to 4,000, Phi-4-multimodal-instruct up to 131,072, Qwen-VL OCR up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R7B Arabic accepts text; 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?
Command R7B Arabic and Phi-4-multimodal-instruct publishes its weights (MIT) and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Command R7B Arabic is the newest, released Feb 27, 2025. Phi-4-multimodal-instruct came out Feb 26, 2025; Qwen-VL OCR came out Oct 28, 2024. Knowledge cutoff: Command R7B Arabic Jun 1, 2024, 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.
