ALLaM-2-7b vs Command R7B Arabic vs Sonar Deep Research
Command R7B Arabic comes out ahead, 25 to 16 and 0 on our weighted score, and it is the cheaper option too.
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
- Our pick
CohereCommand R7B Arabic
25/100- ECI—
- Price$0.037 / $0.15
- Context128K
PerplexitySonar Deep Research
16/100- ECI—
- Price$2.00 / $8.00
- Context128K
Command R7B Arabic is our pick
Command R7B Arabic is the better all-round choice, scoring 25/100 against Sonar Deep Research (16) and ALLaM-2-7b (0). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. 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 · Sonar Deep Research $3.50 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contextCommand R7B Arabic and Sonar Deep ResearchCommand R7B Arabic 128,000 · Sonar Deep Research 128,000 · ALLaM-2-7b 4,096 tokens
- Widest inputsSame inputsALLaM-2-7b: Text · Command R7B Arabic: Text · Sonar Deep Research: Text
- Self-hostingALLaM-2-7b and Command R7B ArabicPublishes downloadable weights
| Measure | Weight | ALLaM-2-7b | Command R7B Arabic | Sonar Deep Research |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 25 | 10 |
| Context window | 40% | 0 | 24 | 24 |
| Overall | 100% | 0/100 | 25/100 | 16/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ALLaM-2-7bSDAIA | Command R7B ArabicCohere | Sonar Deep ResearchPerplexity |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.037 (best) | $2.00 |
| Output | — | $0.15 (best) | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.066 (best) | $3.50 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Cohere API | Official Perplexity API |
| Limits | |||
| Context window | 4,096 tokens | 128,000 tokens (best) | 128,000 tokens (best) |
| Max output | 4,096 tokens | 4,000 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yesminimal · low · medium · high |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | command-r7b-arabic-02-2025 | sonar-deep-research |
| API providers | 2 | 1 | 3 (best) |
| Released | Jan 23, 2025 | Feb 27, 2025 | Feb 1, 2025 |
| Knowledge cutoff | — | Jun 1, 2024 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- ALLaM-2-7b—
- Command R7B Arabic$0.675
- Sonar Deep Research$36.00
Which should you choose?
Which is better: ALLaM-2-7b, Command R7B Arabic or Sonar Deep Research?
Command R7B Arabic is the better all-round choice, scoring 25/100 against Sonar Deep Research (16) and ALLaM-2-7b (0). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, ALLaM-2-7b, Command R7B Arabic or Sonar Deep Research?
Command R7B Arabic is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). Sonar Deep Research costs $2.00 input / $8.00 output per million tokens (official Perplexity 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 $3.50 for Sonar Deep Research (53× as much). ALLaM-2-7b has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. ALLaM-2-7b has not been scored yet, Command R7B Arabic has not been scored yet and Sonar Deep Research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for ALLaM-2-7b, Command R7B Arabic and Sonar Deep Research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ALLaM-2-7b and Sonar Deep Research does not support tool calling, which most coding agents need.
Which has the bigger context window?
Command R7B Arabic and Sonar Deep Research have the largest context windows (128,000 and 128,000 tokens), against 4,096 for ALLaM-2-7b. Maximum output per response: ALLaM-2-7b up to 4,096, Command R7B Arabic up to 4,000, Sonar Deep Research up to 32,768 tokens.
Which can read images, PDFs, audio or video?
ALLaM-2-7b accepts text; Command R7B Arabic accepts text; Sonar Deep Research accepts text. They handle the same number of input types.
Are any of these open source?
ALLaM-2-7b and Command R7B Arabic publishes its weights and can be self-hosted; Sonar Deep Research is proprietary.
Which is newer?
Command R7B Arabic is the newest, released Feb 27, 2025. Sonar Deep Research came out Feb 1, 2025; ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: Command R7B Arabic Jun 1, 2024, Sonar Deep Research Jan 2025.
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.
