// head_to_head
GPT-3.5 Turbo vs Qwen3.6 Plus
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
GPT-3.5 Turbo and Qwen3.6 Plus are priced within ~10% of each other.
GPT-3.5 Turbo does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
openai
GPT-3.5 Turbo
- Blended / 1M
- $0.750
- Context
- 16K
- Released
- May 28, 2023
- Overall score
- Not evaluated
qwen
Qwen3.6 Plus
- Blended / 1M
- $0.731
- Context
- 1M
- Released
- Apr 2, 2026
- Overall score
- 68.9
Specs and pricing
| Metric | GPT-3.5 Turbo | Qwen3.6 Plus |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 68.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1262 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.750 | $0.731 |
| Input price / 1M | $0.500 | $0.325win |
| Output price / 1M | $1.50win | $1.95 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | — |
| Context window | 16K | 1Mwin |
| Max output tokens | 4K | 66Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-3.5 Turbo on top, Qwen3.6 Plus below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | GPT-3.5 Turbo | Qwen3.6 Plus |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $240.00/mo | $234.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $490.00/mo | $377.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $520.00/mo | $416.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $612.50/mo | $471.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,400/mo | $1,008/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.6 Plus
Wider context window — 1M against 16K.
GPT-3.5 Turbo vs Qwen3.6 Plus FAQ
Which is better, GPT-3.5 Turbo or Qwen3.6 Plus?
GPT-3.5 Turbo and Qwen3.6 Plus are priced within ~10% of each other. GPT-3.5 Turbo does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is GPT-3.5 Turbo cheaper than Qwen3.6 Plus?
They cost about the same. Both land near $0.750 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does GPT-3.5 Turbo or Qwen3.6 Plus have a bigger context window?
Qwen3.6 Plus has the larger context window: 16K for GPT-3.5 Turbo against 1M for Qwen3.6 Plus. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.