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Qwen: Qwen3.6 35B A3B

qwen/qwen3.6-35b-a3b

Model weights

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. 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.

Modalities
In / out price
$0.025 / $0.229per 1M
Context
262K
Released
Apr 27, 2026

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).

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

Input
$0.06357/M tokens
Output
$0.8609/M tokens
Price history
No effective price data for this selection

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).

Throughput
123tok/s

P50, best across providers

Latency
0.46s

P50, best provider

AutoExacto benchmarks
GPQA DiamondTAU-BenchSiliconFlow84.7%--CoreWeave82.2%--AtlasCloud85.4%78.0%auto-routing84.6%75.7%AkashML84.5%74.4%
+7 more providers

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.

Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
99.88%

Availability over the last 3 days

Last 72 hours
Availability 99.88%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.96%
Without Routing
98.52%

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.

Benchmark score summary for Qwen: Qwen3.6 35B A3B (Artificial Analysis)
SourceBenchmarkScore
Artificial AnalysisQwen3.6 35B A3B (Reasoning) Coding Index41.9
Artificial AnalysisQwen3.6 35B A3B (Reasoning) Agentic Index13.1
Artificial AnalysisQwen3.6 35B A3B (Reasoning) GPQA Diamond84.1%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) HLE22.2%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) IFBench64.4%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) τ²-Bench Telecom95.3%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) AA-LCR71.7%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) τ-Bench Banking9.3%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) GDPval-AA19.9%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) CritPt0.3%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) SciCode36.6%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) Terminal-Bench Hard34.8%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) Terminal-Bench 4.00.0%
Artificial AnalysisQwen3.6 35B A3B (Reasoning) Terminal-Bench 2.144.9%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) Coding Index28.1
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) GPQA Diamond81.7%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) HLE13.9%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) IFBench36.2%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) τ²-Bench Telecom85.1%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) AA-LCR64.3%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) τ-Bench Banking5.4%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) GDPval-AA17.7%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) CritPt0.0%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) Terminal-Bench Hard25.8%
Artificial AnalysisQwen3.6 35B A3B (Non-reasoning) Terminal-Bench 2.141.6%

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.

1.
Favicon for https://portkey.ai/
Portkey AI
103Btokens
2.
55.4Btokens
3.
8.55Btokens
4.
Favicon for https://nousresearch.com
Hermes Agent
5.32Btokens
5.
4.58Btokens
Rest of OpenRouter
7.65B
Top 5 Apps
6.78B

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