GPT-5.4 Nano vs Qwen3.5 9b
Qwen3.5 9b: 56.0% average across four Vision Evals tasks (#44 of 65 models). We have not run GPT-5.4 Nano through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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GPT-5.4 Nano vs Qwen3.5 9b Comparison Table
Evals updated October 26, 2026Pricing updated November 4, 2026
| Property | GPT-5.4 Nano | Qwen3.5 9b |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Mar 2026 |
| Context Window | 400K | 262K |
| Parameters | Unknown | 9B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.170 |
| Output $/1M | $1.25 | $0.250 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | Not evaluated | 56.0%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | – | $0.0021 |
| Avg speed / sample | – | 41.36s |
| By task | ||
| Object Detection | – | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| Counting | – | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Identification | – | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| OCR | – | – |
| by category | ||
| Reasoning | – | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
GPT-5.4 Nano vs Qwen3.5 9b: Overview
GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.
While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.