GPT OSS 20B vs Qwen3.5-9B
GPT OSS 20B and Qwen3.5-9B are closely matched at 18.0 and 24.7 on the LLM Stats Score. GPT OSS 20B is 2.0x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GPT OSS 20B and Qwen3.5-9B are closely matched on the overall LLM Stats Score at 18.0 and 24.7.
In the 1 individual benchmarks reported for both models, Qwen3.5-9B wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GPT OSS 20B is roughly 2.0x 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 OSS 20B
- cost matters — it's about 2.0x cheaper per token
Choose Qwen3.5-9B
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Mar 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
7 reported for GPT OSS 20B · 25 for Qwen3.5-9B
GPT OSS 20B outperforms in 0 benchmarks, while Qwen3.5-9B is better at 1 benchmark (GPQA).
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 OSS 20B ($0.03/1M tokens) is 3.3x cheaper than Qwen3.5-9B ($0.10/1M tokens).
For output processing, GPT OSS 20B ($0.14/1M tokens) is 1.1x cheaper than Qwen3.5-9B ($0.15/1M tokens).
In conclusion, Qwen3.5-9B is more expensive than GPT OSS 20B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GPT OSS 20B has 11.9B more parameters than Qwen3.5-9B, making it 132.2% larger.
Context Window
Maximum input and output token capacity
Qwen3.5-9B accepts 262,144 input tokens compared to GPT OSS 20B's 131,072 tokens. Qwen3.5-9B can generate longer responses up to 262,144 tokens, while GPT OSS 20B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.5-9B supports multimodal inputs, whereas GPT OSS 20B does not.
Qwen3.5-9B can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT OSS 20B
Qwen3.5-9B
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GPT OSS 20B was released on 2025-08-05, while Qwen3.5-9B was released on 2026-03-02.
Qwen3.5-9B is 7 months newer than GPT OSS 20B.
Aug 5, 2025
1.2 years ago
Mar 2, 2026
7 months ago
6mo 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 OSS 20B is available from DeepInfra, Novita, Fireworks, Groq, OpenAI. Qwen3.5-9B is available from DeepInfra.
GPT OSS 20B
Qwen3.5-9B
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT OSS 20B and Qwen3.5-9B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT OSS 20B vs Qwen3.5-9B.