Qwen3.5-35B-A3B
Qwen · released Feb 25, 2026
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
Specification
- Context window
- 262K
- Max output
- 262K
- Knowledge cutoff
- Not stated
- Parameters
- 36B
- Licence
- apache-2.0
- Serving providers
- 9
- Moderated
- No
- Uptime
- 100.0%
Intelligence
29.9
73th percentile
Coding
—
Coding Index
Agentic
11.8
Agentic Index
Output speed
203 t/s
Median across providers
Latency
419ms
Time to first token
Cost per task
$0.09
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)89.2%
- GPQA Diamond84.5%
- IFBench72.5%
- AA-LCR (long context)68.3%
- SciCode37.7%
- Terminal-Bench Hard26.5%
- Humanity's Last Exam21.0%
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) | 89.2% |
| GPQA Diamond | 84.5% |
| IFBench | 72.5% |
| AA-LCR (long context) | 68.3% |
| SciCode | 37.7% |
| Terminal-Bench Hard | 26.5% |
| Humanity's Last Exam | 21.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Ring-2.6-1TInclusionAI31.1
- o3OpenAI31.1
- Step 3.7 FlashStepFun30.9
- Mistral Medium 3.5Mistral AI30.4
- Qwen3.5-35B-A3BQwen29.9
- Claude Sonnet 4.5Anthropic29.9
- MiniMax M2MiniMax28.9
- Claude Opus 4.1Anthropic28.8
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Ring-2.6-1T | 31.1 |
| o3 | 31.1 |
| Step 3.7 Flash | 30.9 |
| Mistral Medium 3.5 | 30.4 |
| Qwen3.5-35B-A3B | 29.9 |
| Claude Sonnet 4.5 | 29.9 |
| MiniMax M2 | 28.9 |
| Claude Opus 4.1 | 28.8 |
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
- $0.14
- Output / 1M tokens
- $1
- Cached input / 1M
- Not offered
- Blended 3:1
- $0.355
One task, estimated
$0.09
- 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.