
Qwen: Qwen3.6 35B A3B
qwen/qwen3.6-35b-a3b
Model weightsQwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated DeltaNet linear attention with standard gated attention layers, enabling efficient inference at a fraction of the compute cost. The model supports a 262K token native context window (extensible to 1M via YaRN) and accepts text, image, and video inputs. It includes integrated thinking mode with reasoning traces preserved across multi-turn conversations, function calling, and structured output. Released under the Apache 2.0 license.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| $0.100 | $0.950 | $0.100 | 0.53s | 122 tok/s | ||
| $0.100 | $0.890 | $0.035 | 0.46s | 99 tok/s | ||
| $0.100 | $0.900 | $0.025 | 0.57s | 82 tok/s | ||
| $0.100 | $0.900 | $0.050 | 0.98s | 70 tok/s | ||
| $0.100 | $1.000 | $0.050 | 1.48s | 115 tok/s | ||
| $0.250 | $1.250 | $0.250 | 1.12s | 123 tok/s | ||
| $0.100 | $1.000 | -- | 0.83s | 80 tok/s | ||
| $0.025 | $0.229 | $0.022 | 0.46s | 52 tok/s | ||
| $0.050 | $0.700 | $0.025 | 0.58s | 72 tok/s | ||
| $0.240 | $1.800 | $0.150 | 2.38s | 19 tok/s |
Input $0.10 / Output $0.95122 tok/s99.45% uptime- Input $0.10 / Output $0.8999 tok/s99.68% uptime
- Input $0.10 / Output $0.9082 tok/s100.00% uptime
- Input $0.10 / Output $0.9070 tok/s99.23% uptime
- Input $0.10 / Output $1.00115 tok/s96.95% uptime
- Input $0.25 / Output $1.25123 tok/s64.30% uptime
- Input $0.10 / Output $1.0080 tok/s99.65% uptime
- Input $0.025 / Output $0.22952 tok/s100.00% uptime
Input $0.05 / Output $0.7072 tok/s99.93% uptime- Input $0.24 / Output $1.8019 tok/s91.02% uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
Qwen3.6 35B A3B — Price history
| $0.0386 | $0.554 | 19.5% | 7.8% | ||
| $0.0438 | $0.699 | 24.5% | 1.3% | ||
| $0.0569 | $1.000 | 86.3% | 14.8% | ||
| $0.0584 | $0.890 | 55.2% | 38.3% | ||
| $0.0685 | $0.898 | 62.9% | 25.1% | ||
| $0.0722 | $0.889 | 42.3% | 7.6% | ||
| $0.0996 | $1.000 | 19.0% | 0.1% | ||
| $0.1000 | $0.949 | 61.6% | 3.8% | ||
| $0.2499 | $1.250 | 0.0% | 1.3% | ||
| $0.2398 | $1.797 | 0.0% | 0.0% |
- Effective in $0.0386 / Effective out $0.554Token share7.8%
- Effective in $0.0438 / Effective out $0.699Token share1.3%
- Effective in $0.0569 / Effective out $1.000Token share14.8%
- Effective in $0.0584 / Effective out $0.890Token share38.3%
- Effective in $0.0685 / Effective out $0.898Token share25.1%
- Effective in $0.0722 / Effective out $0.889Token share7.6%
- Effective in $0.0996 / Effective out $1.000Token share0.1%
- Effective in $0.1000 / Effective out $0.949Token share3.8%
- Effective in $0.2499 / Effective out $1.250Token share1.3%
- Effective in $0.2398 / Effective out $1.797Token share0.0%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
P50, best across providers
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) Coding Index | 41.9 |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) Agentic Index | 13.1 |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) GPQA Diamond | 84.1% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) HLE | 22.2% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) IFBench | 64.4% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) τ²-Bench Telecom | 95.3% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) AA-LCR | 71.7% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) τ-Bench Banking | 9.3% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) GDPval-AA | 19.9% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) CritPt | 0.3% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) SciCode | 36.6% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) Terminal-Bench Hard | 34.8% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | Qwen3.6 35B A3B (Reasoning) Terminal-Bench 2.1 | 44.9% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) Coding Index | 28.1 |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) GPQA Diamond | 81.7% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) HLE | 13.9% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) IFBench | 36.2% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) τ²-Bench Telecom | 85.1% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) AA-LCR | 64.3% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) τ-Bench Banking | 5.4% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) GDPval-AA | 17.7% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) CritPt | 0.0% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) Terminal-Bench Hard | 25.8% |
| Artificial Analysis | Qwen3.6 35B A3B (Non-reasoning) Terminal-Bench 2.1 | 41.6% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Frequently asked questions
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated DeltaNet linear attention with standard gated attention layers, enabling efficient inference at a fraction of the compute cost.
Qwen3.6 35B A3B costs $0.025/M input tokens and $0.229/M output tokens, with separate rates for Cache Read at $0.022/M tokens.
Qwen3.6 35B A3B has a 262,144 token context window. It supports up to 65,536 completion tokens.
Yes. Qwen3.6 35B A3B accepts tools and tool_choice for function calling on 8 of the 10 providers serving it, and requests that send tools are routed to those providers. It also supports structured outputs via a JSON schema in response_format.
Qwen3.6 35B A3B accepts text, images, and video as input and returns text.
Qwen3.6 35B A3B is served by 10 providers on OpenRouter: Prime Intellect, Darkbloom, CoreWeave, AkashML, Parasail, DeepInfra, DekaLLM, Venice and 2 more. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
Qwen3.6 35B A3B was released on April 27, 2026.