Qwen3.5-122B-A10B
Qwen · released Feb 25, 2026
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...
Specification
- Context window
- 262K
- Max output
- 82K
- Knowledge cutoff
- Not stated
- Parameters
- 125.1B
- Licence
- apache-2.0
- Serving providers
- 5
- Moderated
- No
- Uptime
- 100.0%
Intelligence
32.8
78th percentile
Coding
45.7
Coding Index
Agentic
21.3
Agentic Index
Output speed
145 t/s
Median across providers
Latency
506ms
Time to first token
Cost per task
$0.21
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)93.6%
- GPQA Diamond85.7%
- IFBench75.7%
- AA-LCR (long context)70.3%
- SciCode42.0%
- Terminal-Bench Hard31.1%
- Humanity's Last Exam25.2%
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) | 93.6% |
| GPQA Diamond | 85.7% |
| IFBench | 75.7% |
| AA-LCR (long context) | 70.3% |
| SciCode | 42.0% |
| Terminal-Bench Hard | 31.1% |
| Humanity's Last Exam | 25.2% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- LongCat 2.0Meituan33.9
- KAT-Coder-Pro V2KwaiPilot33.9
- Kimi K2 ThinkingMoonshot AI33.5
- o3 ProOpenAI33.3
- Qwen3.5-122B-A10BQwen32.8
- 32.5
- Qwen3.6 35B A3BQwen32.1
- M2.1MiniMax32.1
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| LongCat 2.0 | 33.9 |
| KAT-Coder-Pro V2 | 33.9 |
| Kimi K2 Thinking | 33.5 |
| o3 Pro | 33.3 |
| Qwen3.5-122B-A10B | 32.8 |
| Qwen3 Max Thinking | 32.5 |
| Qwen3.6 35B A3B | 32.1 |
| M2.1 | 32.1 |
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
- $0.29
- Output / 1M tokens
- $2.4
- Cached input / 1M
- Not offered
- Blended 3:1
- $0.817
One task, estimated
$0.21
- 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.