Solar Pro 2 vs Llama-3.2-11B-Vision-Instruct vs Qwen-MT Turbo
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 52 and 40 on our weighted score, though Qwen-MT Turbo is 12% cheaper per token.
Upstage
Solar Pro 2
52/100- ECI—
- Price$0.25 / $0.25
- Context66K
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
Llama-3.2-11B-Vision-Instruct is our pick
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Solar Pro 2 (52) and Qwen-MT Turbo (40). It leads 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 priceQwen-MT TurboQwen-MT Turbo $0.242 · Solar Pro 2 $0.25 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct 128,000 · Solar Pro 2 65,536 · Qwen-MT Turbo 16,384 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructSolar Pro 2: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Qwen-MT Turbo: Text
- Self-hostingLlama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Solar Pro 2 | Llama-3.2-11B-Vision-Instruct | Qwen-MT Turbo |
|---|---|---|---|---|
| Price | 50% | 78 | 76 | 79 |
| Inputs & features | 30% | 35 | 50 | 0 |
| Context window | 20% | 12 | 24 | 0 |
| Overall | 100% | 52/100 | 58/100 | 40/100 |
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 | Solar Pro 2Upstage | Llama-3.2-11B-Vision-InstructMeta | Qwen-MT TurboAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.197 | $0.16 (best) |
| Output | $0.25 (best) | $0.51 | $0.49 |
| Cached input | — | — | — |
| Blended (3:1) | $0.25 | $0.275 | $0.242 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Upstage API | Median of 2 providers | Official Alibaba API |
| Limits | |||
| Context window | 65,536 tokens | 128,000 tokens (best) | 16,384 tokens |
| Max output | 8,192 tokens (best) | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yesminimal · high | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | solar-pro2 | — | qwen-mt-turbo |
| API providers | 2 (best) | 2 (best) | 1 |
| Released | May 20, 2025 | Sep 25, 2024 | Jan 2025 |
| Knowledge cutoff | Mar 2025 | Dec 2023 | 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.
- Solar Pro 2$3.00
- Llama-3.2-11B-Vision-Instruct$2.99
- Qwen-MT Turbo$2.58
Which should you choose?
Which is better: Solar Pro 2, Llama-3.2-11B-Vision-Instruct or Qwen-MT Turbo?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Solar Pro 2 (52) and Qwen-MT Turbo (40). It leads 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, Solar Pro 2, Llama-3.2-11B-Vision-Instruct or Qwen-MT Turbo?
Qwen-MT Turbo is cheaper at $0.16 input / $0.49 output per million tokens (official Alibaba API price). Solar Pro 2 costs $0.25 input / $0.25 output per million tokens (official Upstage API price); Llama-3.2-11B-Vision-Instruct costs $0.197 input / $0.51 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.242 per million tokens for Qwen-MT Turbo versus $0.25 for Solar Pro 2 (1× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Solar Pro 2 has not been scored yet, Llama-3.2-11B-Vision-Instruct has not been scored yet and Qwen-MT Turbo has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Solar Pro 2, Llama-3.2-11B-Vision-Instruct and Qwen-MT Turbo yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-MT Turbo does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-11B-Vision-Instruct has the largest context window at 128,000 tokens, against 65,536 for Solar Pro 2 and 16,384 for Qwen-MT Turbo. Maximum output per response: Solar Pro 2 up to 8,192, Llama-3.2-11B-Vision-Instruct up to 4,096, Qwen-MT Turbo up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Solar Pro 2 accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Qwen-MT Turbo accepts text. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Solar Pro 2 and Qwen-MT Turbo is proprietary.
Which is newer?
Solar Pro 2 is the newest, released May 20, 2025. Qwen-MT Turbo came out Jan 2025; Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Solar Pro 2 Mar 2025, Llama-3.2-11B-Vision-Instruct Dec 2023, Qwen-MT Turbo 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 Oct 4, 2026.
