ALLaM-2-7b vs Saba vs Qwen2.5-VL 72B Instruct
Qwen2.5-VL 72B Instruct comes out ahead, 40 to 21 and 0 on our weighted score, though Saba is 3.1× cheaper per token.
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
Mistral AISaba
21/100- ECI—
- Price$0.20 / $0.60
- Context33K
- Our pick
Alibaba (Qwen)Qwen2.5-VL 72B Instruct
40/100- ECI—
- Price$0.905 / $1.00
- Context131K
Qwen2.5-VL 72B Instruct is our pick
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 40/100 against Saba (21) and ALLaM-2-7b (0). It leads on inputs & features and context window. 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 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Saba 32,768 · ALLaM-2-7b 4,096 tokens
- Widest inputsSaba and Qwen2.5-VL 72B InstructALLaM-2-7b: Text · Saba: Text, PDFs · Qwen2.5-VL 72B Instruct: Text, Images
- Self-hostingALLaM-2-7b and Qwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | ALLaM-2-7b | Saba | Qwen2.5-VL 72B Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 35 | 50 |
| Context window | 40% | 0 | 0 | 25 |
| Overall | 100% | 0/100 | 21/100 | 40/100 |
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 | SabaMistral AI | Qwen2.5-VL 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.20 (best) | $0.905 |
| Output | — | $0.60 (best) | $1.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.30 (best) | $0.93 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 4 providers | Median of 6 providers |
| Limits | |||
| Context window | 4,096 tokens | 32,768 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 26,214 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 2 | 4 | 6 (best) |
| Released | Jan 23, 2025 | Feb 17, 2025 | Jan 26, 2025 |
| Knowledge cutoff | — | Sep 30, 2024 | Apr 2024 |
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—
- Saba$3.20
- Qwen2.5-VL 72B Instruct$11.06
Which should you choose?
Which is better: ALLaM-2-7b, Saba or Qwen2.5-VL 72B Instruct?
Qwen2.5-VL 72B Instruct is the better all-round choice, scoring 40/100 against Saba (21) and ALLaM-2-7b (0). It leads on inputs & features and context window. 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, Saba or Qwen2.5-VL 72B Instruct?
Saba is cheaper at $0.20 input / $0.60 output per million tokens (median across 4 API providers). Qwen2.5-VL 72B Instruct costs $0.905 input / $1.00 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.30 per million tokens for Saba versus $0.93 for Qwen2.5-VL 72B Instruct (3.1× 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, Saba has not been scored yet and Qwen2.5-VL 72B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for ALLaM-2-7b, Saba and Qwen2.5-VL 72B Instruct 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?
Qwen2.5-VL 72B Instruct has the largest context window at 131,072 tokens, against 32,768 for Saba and 4,096 for ALLaM-2-7b. Maximum output per response: ALLaM-2-7b up to 4,096, Saba up to 26,214, Qwen2.5-VL 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
ALLaM-2-7b accepts text; Saba accepts text and PDFs; Qwen2.5-VL 72B Instruct accepts text and images. Saba handles the widest range of inputs.
Are any of these open source?
ALLaM-2-7b and Qwen2.5-VL 72B Instruct publishes its weights and can be self-hosted; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 2025. Qwen2.5-VL 72B Instruct came out Jan 26, 2025; ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: Saba Sep 30, 2024, Qwen2.5-VL 72B Instruct Apr 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.
