Phi-4-multimodal-instruct vs Phi-4-mini vs Command R7B Arabic
Phi-4-multimodal-instruct comes out ahead, 72 to 65 and 56 on our weighted score, though Command R7B Arabic is 18% cheaper per token.
- Our pick
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
- Microsoft
Phi-4-mini
56/100- ECI—
- Price$0.075 / $0.30
- Context128K
CohereCommand R7B Arabic
65/100- ECI—
- Price$0.037 / $0.15
- Context128K
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 Phi-4-mini (56). 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 · Phi-4-mini $0.131 per 1M tokens (3:1 blend)
- Longest contextPhi-4-multimodal-instructPhi-4-multimodal-instruct 131,072 · Phi-4-mini 128,000 · Command R7B Arabic 128,000 tokens
- Widest inputsPhi-4-multimodal-instructPhi-4-multimodal-instruct: Text, Images, Audio · Phi-4-mini: Text · Command R7B Arabic: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Phi-4-multimodal-instruct | Phi-4-mini | Command R7B Arabic |
|---|---|---|---|---|
| Price | 50% | 98 | 88 | 100 |
| Inputs & features | 30% | 60 | 25 | 35 |
| Context window | 20% | 25 | 24 | 24 |
| Overall | 100% | 72/100 | 56/100 | 65/100 |
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 | Phi-4-miniMicrosoft | Command R7B ArabicCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.075 | $0.037 (best) |
| Output | $0.11 (best) | $0.30 | $0.15 |
| Cached input | — | — | — |
| Blended (3:1) | $0.08 | $0.131 | $0.066 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Azure API | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 131,072 tokens (best) | 4,096 tokens | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenMIT | Open | Open |
| API model ID | — | phi-4-mini | command-r7b-arabic-02-2025 |
| API providers | 1 | 2 (best) | 1 |
| Released | Feb 26, 2025 | Feb 26, 2025 | Feb 27, 2025 |
| Knowledge cutoff | Jun 2024 | Jun 2024 | Jun 1, 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
- Phi-4-mini$1.35
- Command R7B Arabic$0.675
Which should you choose?
Which is better: Phi-4-multimodal-instruct, Phi-4-mini or Command R7B Arabic?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B Arabic (65) and Phi-4-mini (56). 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, Phi-4-multimodal-instruct, Phi-4-mini or Command R7B Arabic?
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); Phi-4-mini costs $0.075 input / $0.30 output per million tokens (official Azure 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 $0.131 for Phi-4-mini (2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Phi-4-multimodal-instruct has not been scored yet, Phi-4-mini has not been scored yet and Command R7B Arabic has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Phi-4-multimodal-instruct, Phi-4-mini and Command R7B Arabic yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Phi-4-multimodal-instruct has the largest context window at 131,072 tokens, against 128,000 for Phi-4-mini and 128,000 for Command R7B Arabic. Maximum output per response: Phi-4-multimodal-instruct up to 131,072, Phi-4-mini up to 4,096, Command R7B Arabic up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Phi-4-multimodal-instruct accepts text, images and audio; Phi-4-mini accepts text; Command R7B Arabic accepts text. Phi-4-multimodal-instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT), so you can self-host them.
Which is newer?
Command R7B Arabic is the newest, released Feb 27, 2025. Phi-4-multimodal-instruct came out Feb 26, 2025; Phi-4-mini came out Feb 26, 2025. Knowledge cutoff: Phi-4-multimodal-instruct Jun 2024, Phi-4-mini Jun 2024, Command R7B Arabic Jun 1, 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.
