Phi-4-multimodal-instruct vs Nova Micro vs Command R7B Arabic
Phi-4-multimodal-instruct comes out ahead, 72 to 65 and 62 on our weighted score, though Nova Micro is 23% cheaper per token.
- Our pick
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
AmazonNova Micro
62/100- ECI—
- Price$0.035 / $0.14
- 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 Nova Micro (62). It leads on inputs & features. 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 priceNova MicroNova Micro $0.061 · Command R7B Arabic $0.066 · Phi-4-multimodal-instruct $0.08 per 1M tokens (3:1 blend)
- Longest contextPhi-4-multimodal-instructPhi-4-multimodal-instruct 131,072 · Nova Micro 128,000 · Command R7B Arabic 128,000 tokens
- Widest inputsPhi-4-multimodal-instructPhi-4-multimodal-instruct: Text, Images, Audio · Nova Micro: Text · Command R7B Arabic: Text
- Self-hostingPhi-4-multimodal-instruct and Command R7B ArabicPublishes downloadable weights (MIT)
| Measure | Weight | Phi-4-multimodal-instruct | Nova Micro | Command R7B Arabic |
|---|---|---|---|---|
| Price | 50% | 98 | 100 | 100 |
| Inputs & features | 30% | 60 | 25 | 35 |
| Context window | 20% | 25 | 24 | 24 |
| Overall | 100% | 72/100 | 62/100 | 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 | Nova MicroAmazon | Command R7B ArabicCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.035 (best) | $0.037 |
| Output | $0.11 (best) | $0.14 | $0.15 |
| Cached input | — | $0.0088 | — |
| Blended (3:1) | $0.08 | $0.061 (best) | $0.066 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Amazon Bedrock API | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 131,072 tokens (best) | 10,000 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 | Proprietary | Open |
| API model ID | — | amazon.nova-micro-v1:0 | command-r7b-arabic-02-2025 |
| API providers | 1 | 8 (best) | 1 |
| Released | Feb 26, 2025 | Dec 3, 2024 | Feb 27, 2025 |
| Knowledge cutoff | Jun 2024 | Oct 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
- Nova Micro$0.63
- Command R7B Arabic$0.675
Which should you choose?
Which is better: Phi-4-multimodal-instruct, Nova Micro or Command R7B Arabic?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B Arabic (65) and Nova Micro (62). It leads on inputs & features. 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, Nova Micro or Command R7B Arabic?
Nova Micro is cheaper at $0.035 input / $0.14 output per million tokens (official Amazon Bedrock API price). Command R7B Arabic costs $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). At a typical mix of three input tokens to one output token, that is $0.061 per million tokens for Nova Micro versus $0.066 for Command R7B Arabic (1.1× as much) and $0.08 for Phi-4-multimodal-instruct (1.3× 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, Nova Micro 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, Nova Micro 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 Nova Micro and 128,000 for Command R7B Arabic. Maximum output per response: Phi-4-multimodal-instruct up to 131,072, Nova Micro up to 10,000, 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; Nova Micro accepts text; Command R7B Arabic accepts text. Phi-4-multimodal-instruct handles the widest range of inputs.
Are any of these open source?
Phi-4-multimodal-instruct and Command R7B Arabic publishes its weights (MIT) and can be self-hosted; Nova Micro is proprietary.
Which is newer?
Command R7B Arabic is the newest, released Feb 27, 2025. Phi-4-multimodal-instruct came out Feb 26, 2025; Nova Micro came out Dec 3, 2024. Knowledge cutoff: Phi-4-multimodal-instruct Jun 2024, Nova Micro Oct 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.
