GPT-4o vs Qwen3.5-9B
Qwen3.5-9B leads the LLM Stats Score 24.6 to 14.1. Qwen3.5-9B is 38.9x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.5-9B leads the overall LLM Stats Score 24.6 to 14.1, ranking #177 overall.
In the 5 individual benchmarks reported for both models, Qwen3.5-9B wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.5-9B is roughly 38.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.5-9B also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GPT-4o
- you want predictable pricing at $2.50/M input and $10.00/M output
Choose Qwen3.5-9B
- overall performance matters — it scores 24.6 and ranks #177 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
- cost matters — it's about 38.9x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Mar 2026
- you need open weights you can self-host or fine-tune
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
38 reported for GPT-4o · 25 for Qwen3.5-9B
GPT-4o outperforms in 1 benchmarks (MMMLU), while Qwen3.5-9B is better at 4 benchmarks (GPQA, IFEval, MMLU-Pro, Multi-Challenge).
Qwen3.5-9B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GPT-4o ($2.50/1M tokens) is 25.0x more expensive than Qwen3.5-9B ($0.10/1M tokens).
For output processing, GPT-4o ($10.00/1M tokens) is 66.7x more expensive than Qwen3.5-9B ($0.15/1M tokens).
In conclusion, GPT-4o is more expensive than Qwen3.5-9B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen3.5-9B accepts 262,144 input tokens compared to GPT-4o's 128,000 tokens. Qwen3.5-9B can generate longer responses up to 262,144 tokens, while GPT-4o is limited to 16,384 tokens.
Input capabilities
Documented input modalities across available providers
Both GPT-4o and Qwen3.5-9B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GPT-4o
Qwen3.5-9B
License
Usage and distribution terms
GPT-4o is licensed under a proprietary license, while Qwen3.5-9B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT-4o was released on 2024-08-06, while Qwen3.5-9B was released on 2026-03-02.
Qwen3.5-9B is 19 months newer than GPT-4o.
Aug 6, 2024
2.2 years ago
Mar 2, 2026
7 months ago
1.6yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GPT-4o is available from Azure, OpenAI. Qwen3.5-9B is available from DeepInfra.
GPT-4o
Qwen3.5-9B
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4o and Qwen3.5-9B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4o vs Qwen3.5-9B.