Command R7B vs Nova Micro vs Phi-4-multimodal-instruct
Phi-4-multimodal-instruct comes out ahead, 72 to 65 and 62 on our weighted score, though Nova Micro is 23% cheaper per token.
CohereCommand R7B
65/100- ECI—
- Price$0.037 / $0.15
- Context128K
AmazonNova Micro
62/100- ECI—
- Price$0.035 / $0.14
- Context128K
- Our pick
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
Phi-4-multimodal-instruct is our pick
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B (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 $0.066 · Phi-4-multimodal-instruct $0.08 per 1M tokens (3:1 blend)
- Longest contextPhi-4-multimodal-instructPhi-4-multimodal-instruct 131,072 · Command R7B 128,000 · Nova Micro 128,000 tokens
- Widest inputsPhi-4-multimodal-instructCommand R7B: Text · Nova Micro: Text · Phi-4-multimodal-instruct: Text, Images, Audio
- Self-hostingCommand R7B and Phi-4-multimodal-instructPublishes downloadable weights (MIT)
| Measure | Weight | Command R7B | Nova Micro | Phi-4-multimodal-instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 98 |
| Inputs & features | 30% | 35 | 25 | 60 |
| Context window | 20% | 24 | 24 | 25 |
| Overall | 100% | 65/100 | 62/100 | 72/100 |
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 R7BCohere | Nova MicroAmazon | Phi-4-multimodal-instructMicrosoft |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.037 | $0.035 (best) | $0.07 |
| Output | $0.15 | $0.14 | $0.11 (best) |
| Cached input | — | $0.0088 | — |
| Blended (3:1) | $0.066 | $0.061 (best) | $0.08 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Amazon Bedrock API | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 10,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenMIT |
| API model ID | command-r7b-12-2024 | amazon.nova-micro-v1:0 | — |
| API providers | 6 | 8 (best) | 1 |
| Released | Dec 13, 2024 | Dec 3, 2024 | Feb 26, 2025 |
| Knowledge cutoff | Jun 1, 2024 | Oct 2024 | Jun 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$0.675
- Nova Micro$0.63
- Phi-4-multimodal-instruct$0.92
Which should you choose?
Which is better: Command R7B, Nova Micro or Phi-4-multimodal-instruct?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B (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, Command R7B, Nova Micro or Phi-4-multimodal-instruct?
Nova Micro is cheaper at $0.035 input / $0.14 output per million tokens (official Amazon Bedrock API price). Command R7B 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 (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. Command R7B has not been scored yet, Nova Micro has not been scored yet and Phi-4-multimodal-instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B, Nova Micro and Phi-4-multimodal-instruct 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 Command R7B and 128,000 for Nova Micro. Maximum output per response: Command R7B up to 4,000, Nova Micro up to 10,000, Phi-4-multimodal-instruct up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Command R7B accepts text; Nova Micro accepts text; Phi-4-multimodal-instruct accepts text, images and audio. Phi-4-multimodal-instruct handles the widest range of inputs.
Are any of these open source?
Command R7B and Phi-4-multimodal-instruct publishes its weights (MIT) and can be self-hosted; Nova Micro is proprietary.
Which is newer?
Phi-4-multimodal-instruct is the newest, released Feb 26, 2025. Command R7B came out Dec 13, 2024; Nova Micro came out Dec 3, 2024. Knowledge cutoff: Command R7B Jun 1, 2024, Nova Micro Oct 2024, Phi-4-multimodal-instruct Jun 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.
