Saba vs Qwen2.5-VL 72B Instruct vs Solar Pro 2
Solar Pro 2 comes out ahead, 51 to 46 and 44 on our weighted score, and it is the cheaper option too.
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
Alibaba (Qwen)Qwen2.5-VL 72B Instruct
44/100- ECI—
- Price$0.905 / $1.00
- Context131K
- Our pick
UpstageSolar Pro 2
51/100- ECI—
- Price$0.25 / $0.25
- Context66K
Solar Pro 2 is our pick
Solar Pro 2 is the better all-round choice, scoring 51/100 against Saba (46) and Qwen2.5-VL 72B Instruct (44). It leads on price. Qwen2.5-VL 72B Instruct wins on inputs & features and context window. 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 priceSolar Pro 2Solar Pro 2 $0.25 · Saba $0.30 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Solar Pro 2 65,536 · Saba 32,768 tokens
- Widest inputsSaba and Qwen2.5-VL 72B InstructSaba: Text, PDFs · Qwen2.5-VL 72B Instruct: Text, Images · Solar Pro 2: Text
- Self-hostingQwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | Saba | Qwen2.5-VL 72B Instruct | Solar Pro 2 |
|---|---|---|---|---|
| Price | 50% | 72 | 49 | 75 |
| Inputs & features | 30% | 35 | 50 | 35 |
| Context window | 20% | 0 | 25 | 12 |
| Overall | 100% | 46/100 | 44/100 | 51/100 |
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 | SabaMistral AI | Qwen2.5-VL 72B InstructAlibaba (Qwen) | Solar Pro 2Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.905 | $0.25 |
| Output | $0.60 | $1.00 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.30 | $0.93 | $0.25 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 6 providers | Official Upstage API |
| Limits | |||
| Context window | 32,768 tokens | 131,072 tokens (best) | 65,536 tokens |
| Max output | 26,214 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yesminimal · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | — | solar-pro2 |
| API providers | 4 | 6 (best) | 2 |
| Released | Feb 17, 2025 | Jan 26, 2025 | May 20, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Apr 2024 | Mar 2025 |
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
- Qwen2.5-VL 72B Instruct$11.06
- Solar Pro 2$3.00
Which should you choose?
Which is better: Saba, Qwen2.5-VL 72B Instruct or Solar Pro 2?
Solar Pro 2 is the better all-round choice, scoring 51/100 against Saba (46) and Qwen2.5-VL 72B Instruct (44). It leads on price. Qwen2.5-VL 72B Instruct wins on inputs & features and context window. 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, Saba, Qwen2.5-VL 72B Instruct or Solar Pro 2?
Solar Pro 2 is cheaper at $0.25 input / $0.25 output per million tokens (official Upstage API price). Saba costs $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 $0.25 per million tokens for Solar Pro 2 versus $0.30 for Saba (1.2× as much) and $0.93 for Qwen2.5-VL 72B Instruct (3.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Saba has not been scored yet, Qwen2.5-VL 72B Instruct has not been scored yet and Solar Pro 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Saba, Qwen2.5-VL 72B Instruct and Solar Pro 2 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?
Qwen2.5-VL 72B Instruct has the largest context window at 131,072 tokens, against 65,536 for Solar Pro 2 and 32,768 for Saba. Maximum output per response: Saba up to 26,214, Qwen2.5-VL 72B Instruct up to 8,192, Solar Pro 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Saba accepts text and PDFs; Qwen2.5-VL 72B Instruct accepts text and images; Solar Pro 2 accepts text. Saba handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 72B Instruct publishes its weights and can be self-hosted; Saba and Solar Pro 2 is proprietary.
Which is newer?
Solar Pro 2 is the newest, released May 20, 2025. Saba came out Feb 17, 2025; Qwen2.5-VL 72B Instruct came out Jan 26, 2025. Knowledge cutoff: Saba Sep 30, 2024, Qwen2.5-VL 72B Instruct Apr 2024, Solar Pro 2 Mar 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 Nov 2, 2026.
