Qwen3.5-0.8B is Alibaba's smallest model in the Qwen3.5 series, featuring a hybrid Gated Delta Networks and sparse Mixture-of-Experts architecture. Despite its compact size, it supports a 262K token context window, 201 languages, thinking/reasoning mode, and tool calling. Ideal for edge deployments, resource-constrained environments, and lightweight inference tasks.
function_callinghttps://api.deepinfra.com/v1/openai/v1/chat/completions| Model | Context | Max out | Pricing |
|---|---|---|---|
| Qwen3-Embedding-0.6B-batch | — | — | — |
| Qwen3-Embedding-0.6B | — | — | — |
| Qwen3-Embedding-4B-batch | — | — | — |
| Qwen3-Embedding-4B | — | — | — |
| Qwen3-Embedding-8B-batch | — | — | — |
| Qwen3-Embedding-8B | — | — | — |
| Qwen3-Reranker-0.6B | — | — | — |
| Qwen3-Reranker-4B | — | — | — |
| Qwen3-Reranker-8B | — | — | — |
| Qwen3-TTS-VoiceDesign | — | — | — |
| Qwen3-TTS | — | — | — |
| Qwen3.5-0.8B | 262K | — | $0.01$0.05 |
| Qwen3.5-2B | 262K | — | $0.02$0.1 |
| Qwen3.5-4B | 262K | — | $0.03$0.15 |
| Qwen3-32B | 41K | — | $0.08$0.28 |
| Qwen3-235B-A22B-Instruct-2507 | 262K | — | $0.09$0.55 |
| Qwen3-Next-80B-A3B-Instruct | 262K | — | $0.09$1.1 |
| Qwen3.6-35B-A3B | 262K | — | $0.1$0.95 |
| Qwen3-14B | 41K | — | $0.12$0.24 |
| Qwen3-30B-A3B | 41K | — | $0.12$0.5 |
| Qwen3.5-35B-A3B | 262K | — | $0.14$1 |
| Qwen3-VL-30B-A3B-Instruct | 262K | — | $0.15$0.6 |
| Qwen3-VL-235B-A22B-Instruct | 262K | — | $0.2$0.88 |
| Qwen3-235B-A22B-Thinking-2507 | 262K | — | $0.23$2.3 |
| Qwen3.5-27B | 262K | — | $0.26$2.6 |
| Qwen3.5-122B-A10B | 262K | — | $0.29$2.4 |
| Qwen3-Coder-480B-A35B-Instruct-Turbo | 262K | — | $0.3$1 |
| Qwen3.5-397B-A17B | 262K | — | $0.45$3 |
| Qwen3-Max-Thinking | 256K | — | $1.2$6 |
| Qwen3-Max | 256K | — | $1.2$6 |
/v1/models/deepinfra/Qwen/Qwen3.5-0.8BQwen3.5-0.8B is Alibaba's smallest model in the Qwen3.5 series, featuring a hybrid Gated Delta Networks and sparse Mixture-of-Experts architecture. Despite its compact size, it supports a 262K token context window, 201 languages, thinking/reasoning mode, and tool calling. Ideal for edge deployments, resource-constrained environments, and lightweight inference tasks.