Solar Pro 3
Upstage · released Jan 27, 2026
Solar Pro 3 is Upstage's powerful Mixture-of-Experts (MoE) language model. With 102B total parameters and 12B active parameters per forward pass, it delivers exceptional performance while maintaining computational efficiency. Optimized...
Specification
- Context window
- 131K
- Max output
- 131K
- Knowledge cutoff
- Not stated
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
14.5
48th percentile
Coding
16.2
Coding Index
Agentic
2.9
Agentic Index
Output speed
162 t/s
Median across providers
Latency
791ms
Time to first token
Cost per task
$0.06
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- τ²-bench (Telecom)86.3%
- GPQA Diamond72.4%
- IFBench71.2%
- AA-LCR (long context)31.0%
- SciCode24.7%
- Humanity's Last Exam10.3%
- Terminal-Bench Hard7.6%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| τ²-bench (Telecom) | 86.3% |
| GPQA Diamond | 72.4% |
| IFBench | 71.2% |
| AA-LCR (long context) | 31.0% |
| SciCode | 24.7% |
| Humanity's Last Exam | 10.3% |
| Terminal-Bench Hard | 7.6% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- 15.0
- GPT-4.1 MiniOpenAI14.8
- Mistral Medium 3.1Mistral AI14.7
- Solar Pro 3Upstage14.5
- Llama 4 MaverickMeta14.5
- 14.4
- Ling-2.6-flashInclusionAI14.2
- Gemini 2.5 FlashGoogle14.2
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Nemotron 3 Nano Omni (free) | 15.0 |
| GPT-4.1 Mini | 14.8 |
| Mistral Medium 3.1 | 14.7 |
| Solar Pro 3 | 14.5 |
| Llama 4 Maverick | 14.5 |
| Qwen3 VL 235B A22B Instruct | 14.4 |
| Ling-2.6-flash | 14.2 |
| Gemini 2.5 Flash | 14.2 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.15
- Output / 1M tokens
- $0.6
- Cached input / 1M
- $0.015
- Blended 3:1
- $0.262
One task, estimated
$0.06
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.