Command R7B Arabic vs Gemma 3 4B IT vs Ministral 3B
Gemma 3 4B IT comes out ahead, 70 to 62 and 61 on our weighted score, and it is the cheaper option too.
CohereCommand R7B Arabic
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
- Our pick
GoogleGemma 3 4B IT
70/100- ECI116.1
- Price$0.05 / $0.10
- Context131K
Mistral AIMinistral 3B
61/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
Gemma 3 4B IT is our pick
Gemma 3 4B IT is the better all-round choice, scoring 70/100 against Command R7B Arabic (62) and Ministral 3B (61). 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 priceGemma 3 4B ITGemma 3 4B IT $0.063 · Command R7B Arabic $0.066 · Ministral 3B $0.10 per 1M tokens (3:1 blend)
- Longest contextGemma 3 4B ITGemma 3 4B IT 131,072 · Command R7B Arabic 128,000 · Ministral 3B 128,000 tokens
- Widest inputsGemma 3 4B ITCommand R7B Arabic: Text · Gemma 3 4B IT: Text, Images · Ministral 3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R7B Arabic | Gemma 3 4B IT | Ministral 3B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 97 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 70/100 | 61/100 |
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 | Gemma 3 4B ITGoogle | Ministral 3BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.1 | 118.1 (best) |
| ECI rank | — | #146 of 148 | #144 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 23.2% | 25.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.5% | — |
| Price per million tokens | |||
| Input | $0.037 (best) | $0.05 | $0.10 |
| Output | $0.15 | $0.10 (best) | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.066 | $0.063 (best) | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 7 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 131,072 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r7b-arabic-02-2025 | — | — |
| API providers | 1 | 7 (best) | 1 |
| Released | Feb 27, 2025 | Mar 12, 2025 | Oct 16, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Aug 2024 | Mar 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
- Gemma 3 4B IT$0.70
- Ministral 3B$1.20
Which should you choose?
Which is better: Command R7B Arabic, Gemma 3 4B IT or Ministral 3B?
Gemma 3 4B IT is the better all-round choice, scoring 70/100 against Command R7B Arabic (62) and Ministral 3B (61). 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, Gemma 3 4B IT or Ministral 3B?
Gemma 3 4B IT is cheaper at $0.05 input / $0.10 output per million tokens (median across 7 API providers). Command R7B Arabic costs $0.037 input / $0.15 output per million tokens (official Cohere API price); Ministral 3B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Gemma 3 4B IT versus $0.066 for Command R7B Arabic (1× as much) and $0.10 for Ministral 3B (1.6× 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, Gemma 3 4B IT has an ECI of 116.1 and Ministral 3B has an ECI of 118.1.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B Arabic, Gemma 3 4B IT and Ministral 3B 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?
Gemma 3 4B IT has the largest context window at 131,072 tokens, against 128,000 for Command R7B Arabic and 128,000 for Ministral 3B. Maximum output per response: Command R7B Arabic up to 4,000, Gemma 3 4B IT up to 131,072, Ministral 3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Command R7B Arabic accepts text; Gemma 3 4B IT accepts text and images; Ministral 3B accepts text. Gemma 3 4B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Gemma 3 4B IT is the newest, released Mar 12, 2025. Command R7B Arabic came out Feb 27, 2025; Ministral 3B came out Oct 16, 2024. Knowledge cutoff: Command R7B Arabic Jun 1, 2024, Gemma 3 4B IT Aug 2024, Ministral 3B Mar 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 Oct 4, 2026.
