Command R7B Arabic vs Mistral Small 3 vs Phi-4-multimodal-instruct
Phi-4-multimodal-instruct comes out ahead, 72 to 65 and 50 on our weighted score, though Mistral Small 3 is 28% cheaper per token.
CohereCommand R7B Arabic
65/100- ECI—
- Price$0.037 / $0.15
- Context128K
Mistral AIMistral Small 3
50/100- ECI—
- Price$0.05 / $0.08
- Context33K
- 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 Arabic (65) and Mistral Small 3 (50). 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 priceMistral Small 3Mistral Small 3 $0.058 · 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 · Command R7B Arabic 128,000 · Mistral Small 3 32,768 tokens
- Widest inputsPhi-4-multimodal-instructCommand R7B Arabic: Text · Mistral Small 3: Text · Phi-4-multimodal-instruct: Text, Images, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R7B Arabic | Mistral Small 3 | Phi-4-multimodal-instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 98 |
| Inputs & features | 30% | 35 | 0 | 60 |
| Context window | 20% | 24 | 0 | 25 |
| Overall | 100% | 65/100 | 50/100 | 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 R7B ArabicCohere | Mistral Small 3Mistral AI | Phi-4-multimodal-instructMicrosoft |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 47.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 6.7% | — |
| Price per million tokens | |||
| Input | $0.037 (best) | $0.05 | $0.07 |
| Output | $0.15 | $0.08 (best) | $0.11 |
| Cached input | — | — | — |
| Blended (3:1) | $0.066 | $0.058 (best) | $0.08 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 6 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 32,768 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 16,384 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 | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Open | OpenMIT |
| API model ID | command-r7b-arabic-02-2025 | — | — |
| API providers | 1 | 6 (best) | 1 |
| Released | Feb 27, 2025 | Jan 30, 2025 | Feb 26, 2025 |
| Knowledge cutoff | Jun 1, 2024 | Oct 31, 2023 | 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 Arabic$0.675
- Mistral Small 3$0.66
- Phi-4-multimodal-instruct$0.92
Which should you choose?
Which is better: Command R7B Arabic, Mistral Small 3 or Phi-4-multimodal-instruct?
Phi-4-multimodal-instruct is the better all-round choice, scoring 72/100 against Command R7B Arabic (65) and Mistral Small 3 (50). 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 Arabic, Mistral Small 3 or Phi-4-multimodal-instruct?
Mistral Small 3 is cheaper at $0.05 input / $0.08 output per million tokens (median across 6 API providers). 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 $0.058 per million tokens for Mistral Small 3 versus $0.066 for Command R7B Arabic (1.1× as much) and $0.08 for Phi-4-multimodal-instruct (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, Mistral Small 3 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 Arabic, Mistral Small 3 and Phi-4-multimodal-instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Mistral Small 3 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 32,768 for Mistral Small 3. Maximum output per response: Command R7B Arabic up to 4,000, Mistral Small 3 up to 16,384, Phi-4-multimodal-instruct up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Command R7B Arabic accepts text; Mistral Small 3 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?
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; Mistral Small 3 came out Jan 30, 2025. Knowledge cutoff: Command R7B Arabic Jun 1, 2024, Mistral Small 3 Oct 31, 2023, 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.
