GPT-3.5 Turbo vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct leads the LLM Stats Score 2.5 to -9.4. Qwen2.5 7B Instruct is 2.5x cheaper per token.
OpenAI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen2.5 7B Instruct leads the overall LLM Stats Score 2.5 to -9.4, ranking #329 overall.
In the 3 individual benchmarks reported for both models, Qwen2.5 7B Instruct wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen2.5 7B Instruct is roughly 2.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen2.5 7B Instruct also accepts a larger context window (131,072 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-3.5 Turbo
- you want predictable pricing at $0.50/M input and $1.50/M output
Choose Qwen2.5 7B Instruct
- overall performance matters — it scores 2.5 and ranks #329 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 2.5x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Sep 2024
- 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
8 reported for GPT-3.5 Turbo · 14 for Qwen2.5 7B Instruct
GPT-3.5 Turbo outperforms in 0 benchmarks, while Qwen2.5 7B Instruct is better at 3 benchmarks (GPQA, HumanEval, MATH).
Qwen2.5 7B Instruct 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-3.5 Turbo ($0.50/1M tokens) is 1.7x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
For output processing, GPT-3.5 Turbo ($1.50/1M tokens) is 5.0x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).
In conclusion, GPT-3.5 Turbo is more expensive than Qwen2.5 7B Instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Qwen2.5 7B Instruct accepts 131,072 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. Qwen2.5 7B Instruct can generate longer responses up to 8,192 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.
License
Usage and distribution terms
GPT-3.5 Turbo is licensed under a proprietary license, while Qwen2.5 7B Instruct 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-3.5 Turbo was released on 2023-03-21, while Qwen2.5 7B Instruct was released on 2024-09-19.
Qwen2.5 7B Instruct is 18 months newer than GPT-3.5 Turbo.
Mar 21, 2023
3.6 years ago
Sep 19, 2024
2.1 years ago
1.5yr newerKnowledge Cutoff
When training data ends
GPT-3.5 Turbo has a documented knowledge cutoff of 2021-09-30, while Qwen2.5 7B Instruct's cutoff date is not specified.
We can confirm GPT-3.5 Turbo's training data extends to 2021-09-30, but cannot make a direct comparison without Qwen2.5 7B Instruct's cutoff date.
Sep 2021
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Provider Availability
GPT-3.5 Turbo is available from Azure, OpenAI. Qwen2.5 7B Instruct is available from Together.
GPT-3.5 Turbo
Qwen2.5 7B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-3.5 Turbo and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-3.5 Turbo vs Qwen2.5 7B Instruct.