Saba vs ALLaM-2-7b
Saba comes out ahead, 21 to 0 on our weighted score, and it is the cheaper option too.
- Our pick
Mistral AISaba
21/100- ECI—
- Price$0.20 / $0.60
- Context33K
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
Add a model
Make it a three-way comparison.
Saba is our pick
Saba is the better all-round choice, scoring 21/100 against 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 priceSabaSaba $0.30 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contextSabaSaba 32,768 · ALLaM-2-7b 4,096 tokens
- Widest inputsSabaSaba: Text, PDFs · ALLaM-2-7b: Text
- Self-hostingALLaM-2-7bPublishes downloadable weights
| Measure | Weight | Saba | ALLaM-2-7b |
|---|---|---|---|
| Inputs & features | 60% | 35 | 0 |
| Context window | 40% | 0 | 0 |
| Overall | 100% | 21/100 | 0/100 |
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 | SabaMistral AI | ALLaM-2-7bSDAIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.20 | — |
| Output | $0.60 | — |
| Cached input | — | — |
| Blended (3:1) | $0.30 | — |
| Long-context rate | Same rate | — |
| Price source | Median of 4 providers | — |
| Limits | ||
| Context window | 32,768 tokens (best) | 4,096 tokens |
| Max output | 26,214 tokens (best) | 4,096 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | 4 (best) | 2 |
| Released | Feb 17, 2025 | Jan 23, 2025 |
| Knowledge cutoff | Sep 30, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Saba$3.20
- ALLaM-2-7b—
Which should you choose?
Which is better: Saba or ALLaM-2-7b?
Saba is the better all-round choice, scoring 21/100 against 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, Saba or ALLaM-2-7b?
Saba is cheaper at $0.20 input / $0.60 output per million tokens (median across 4 API providers). . At a typical mix of three input tokens to one output token, that is $0.30 per million tokens for Saba versus . ALLaM-2-7b has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Saba has not been scored yet and ALLaM-2-7b has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Saba and ALLaM-2-7b yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ALLaM-2-7b does not support tool calling, which most coding agents need.
Which has the bigger context window?
Saba has the largest context window at 32,768 tokens, against 4,096 for ALLaM-2-7b. Maximum output per response: Saba up to 26,214, ALLaM-2-7b up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Saba accepts text and PDFs; ALLaM-2-7b accepts text. Saba handles the widest range of inputs.
Are any of these open source?
ALLaM-2-7b publishes its weights and can be self-hosted; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 2025. ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: Saba Sep 30, 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.
