Solar Pro 2 vs Qwen2.5-VL 72B Instruct vs Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite comes out ahead, 83 to 51 and 44 on our weighted score, and it is the cheaper option too.
Upstage
Solar Pro 2
51/100- ECI—
- Price$0.25 / $0.25
- Context66K
Alibaba (Qwen)
Qwen2.5-VL 72B Instruct
44/100- ECI—
- Price$0.905 / $1.00
- Context131K
- Our pick
Google
Gemini 2.5 Flash-Lite
83/100- ECI—
- Price$0.10 / $0.40
- Context1.05M
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Solar Pro 2 (51) and Qwen2.5-VL 72B Instruct (44). It leads on price, 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 priceGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite $0.175 · Solar Pro 2 $0.25 · Qwen2.5-VL 72B Instruct $0.93 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Qwen2.5-VL 72B Instruct 131,072 · Solar Pro 2 65,536 tokens
- Widest inputsGemini 2.5 Flash-LiteSolar Pro 2: Text · Qwen2.5-VL 72B Instruct: Text, Images · Gemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video
- Self-hostingQwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | Solar Pro 2 | Qwen2.5-VL 72B Instruct | Gemini 2.5 Flash-Lite |
|---|---|---|---|---|
| Price | 50% | 75 | 49 | 82 |
| Inputs & features | 30% | 35 | 50 | 100 |
| Context window | 20% | 12 | 25 | 61 |
| Overall | 100% | 51/100 | 44/100 | 83/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.905 | $0.10 (best) |
| Output | $0.25 (best) | $1.00 | $0.40 |
| Cached input | — | — | $0.01 |
| Blended (3:1) | $0.25 | $0.93 | $0.175 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Upstage API | Median of 6 providers | Official Google API |
| Limits | |||
| Context window | 65,536 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yesminimal · high | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | solar-pro2 | — | gemini-2.5-flash-lite |
| API providers | 2 | 6 | 24 (best) |
| Released | May 20, 2025 | Jan 26, 2025 | Jun 17, 2025 |
| Knowledge cutoff | Mar 2025 | Apr 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.
Solar Pro 2$3.00
Qwen2.5-VL 72B Instruct$11.06
Gemini 2.5 Flash-Lite$1.80
Which should you choose?
Which is better: Solar Pro 2, Qwen2.5-VL 72B Instruct or Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Solar Pro 2 (51) and Qwen2.5-VL 72B Instruct (44). It leads on price, 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, Qwen2.5-VL 72B Instruct or Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite is cheaper at $0.10 input / $0.40 output per million tokens (official Google API price). Solar Pro 2 costs $0.25 input / $0.25 output per million tokens (official Upstage API price); 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 $0.175 per million tokens for Gemini 2.5 Flash-Lite versus $0.25 for Solar Pro 2 (1.4× as much) and $0.93 for Qwen2.5-VL 72B Instruct (5.3× 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, Qwen2.5-VL 72B Instruct has not been scored yet and Gemini 2.5 Flash-Lite has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Solar Pro 2, Qwen2.5-VL 72B Instruct and Gemini 2.5 Flash-Lite 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?
Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 131,072 for Qwen2.5-VL 72B Instruct and 65,536 for Solar Pro 2. Maximum output per response: Solar Pro 2 up to 8,192, Qwen2.5-VL 72B Instruct up to 8,192, Gemini 2.5 Flash-Lite up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Solar Pro 2 accepts text; Qwen2.5-VL 72B Instruct accepts text and images; Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video. Gemini 2.5 Flash-Lite 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; Solar Pro 2 and Gemini 2.5 Flash-Lite is proprietary.
Which is newer?
Gemini 2.5 Flash-Lite is the newest, released Jun 17, 2025. Solar Pro 2 came out May 20, 2025; Qwen2.5-VL 72B Instruct came out Jan 26, 2025. Knowledge cutoff: Solar Pro 2 Mar 2025, Qwen2.5-VL 72B Instruct Apr 2024, Gemini 2.5 Flash-Lite 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 Nov 2, 2026.